Software Engineering Mastery: Innovate, Learn, and Scale

The key to a successful software development project lies in careful planning and a systematic framework. This framework acts as a roadmap, guiding developers to make well-informed decisions throughout the project. Predictive or Adaptive software development life cycle processes ensure a structured approach where all stakeholders can track the progress in multiple stages. Choosing the right software development method helps organizations manage potential risks, improve scalability, enhance project quality, control development costs, and increase team collaboration. 

Large Language Models (LLMs) have long been at the forefront of artificial intelligence, leveraging vast amounts of textual data to perform tasks ranging from language translation to content generation. These models have laid the foundation for numerous applications, fundamentally transforming the way machines understand and generate human-like text. However, as the landscape of AI continues to evolve, a shift is occurring with the rise of Multimodal LLMs.

The era of guessing your size and hoping for the best when ordering clothes online is becoming a thing of the past. In today’s digital world, shoppers are looking for a more tailored experience, one that mirrors the personal touch of in-store shopping but the convenience of doing it from their own home. This is where the idea of trying on clothes virtually, with the help of smart technology, comes into play.

The Internet of Things (IoT) is a technological framework that is being adopted in myriad industries at a fierce rate. There has been continuous innovation in this field of technology as it converges with various technology stacks associated with Big Data and Artificial Intelligence. Simultaneously, the number of connected IoT devices is also increasing rapidly, posing the need to improve one of the most important aspects of IoT - scalability.

AWS and GCP are two of the popular public cloud platforms. Although GCP gained momentum recently, AWS remains the dominant player in this market. Data is the backbone of all successful app deployments, analytics workflows, and machine learning practices. While migrating data between the clouds, you first need to understand the location where you will move the data for various use cases, the variety of data you are moving, and the available resources.

The adoption of cloud solutions has become a fundamental aspect of corporate strategies. Regardless of the extent to which a company relies on public cloud services, integrating cloud solutions into their operational plans has become commonplace. This shift is primarily driven by the convenience, affordability, and reasonable reliability that cloud technologies offer.

The introduction of chatbots revolutionized customer and brand interaction. With the ability to mimic conversations and offer instant, digital connection, chatbots made their way into businesses like a wildfire. In fact, Gartner predicts that more than 50% of enterprises will spend more annually on bots and chatbot creation than traditional mobile app development.

Daffodil Software, a global leader in software engineering solutions, announces its inclusion in the Everest Group's PEAK Matrix® Assessment 2023 for Next-Generation Quality Engineering (QE) Services. The provider has been positioned as an 'Aspirant' in the Quality Engineering QE Services category.

Benefits of cloud are no more limited to shared infrastructure to host an application. Cloud-native, that leverages loosely-coupled components of cloud for application development is gaining grounds in the software industry due to its exponential scalability and increased robustness.

The buzz around AI is hard to miss. With startups securing substantial funding and AI technology becoming more accessible, business leaders are keen to harness its potential. However, for many, the question remains: How and where can AI be effectively employed in our organization?

The increasing business complexity and demand for highly scalable applications have brought a paradigm shift in how software applications are engineered. In the past few years, different software architectures are adopted by developers to reduce code complexity, improve fault isolation, and minimize time-to-market.

Vision AI (also known as Computer Vision) is a field of computer science that trains computers to replicate the human vision system. This enables digital devices (like face detectors, QR Code Scanners) to identify and process objects in images and videos, just like humans do. 

Have you noticed the ‘Smart Compose’ feature in Gmail that gives auto-suggestions to complete sentences while writing an email? This is one of the various use-cases of language models used in Natural Language Processing (NLP).

In today's digital age, where every click, transaction, and interaction matter, how we design software systems has transformed significantly. Think of it this way: picture a smart home system that adjusts the lighting and temperature based on your actions, just like a responsive assistant who understands your needs without you saying a word. This is the essence of Event-Driven Architecture (EDA), a concept that might sound complex but is rooted in a surprisingly simple idea: making technology respond to the real world just like we do.

Software development methodologies have come a long way. From Spiral to Waterfall to Agile, there is a significant shift in team roles, ceremonies, planning, and delivery activities. And if we talk about the pace at which software applications are brought to market these days, Agile seems to have made an impact and is considered the most relevant approach to manage the SDLC cycle. 

The idea of adopting an iterative development approach has been gaining grounds in the software industry. Agile methodology for project management, which was introduced back in 2001 has transformed the way software development companies deliver the product throughput.  The Kanban Vs Scrum tilt has been going on for quite a time now. Here are the key differentiators that hold them apart. 

Ever since the intense scrutiny faced by organizations that developed COVID vaccines, pharmacovigilance has become a hotly discussed area of interest. The practice of pharmacovigilance basically aims to reduce the entry of drugs with adverse side effects into regular circulation. Artificial Intelligence (AI) has been permeating this field in recent years due to the immense potential for automated pharmaceutical discovery that it offers.

AI, a technology that enables computers to replicate human-like thinking and problem-solving, has captured our attention. Meanwhile, Cloud Computing, with its capability to deliver abundant computing resources over the internet, has changed how businesses handle their IT infrastructure.

Artificial Intelligence (AI) has introduced major breakthroughs as fintech solutions in the finance industry. Risk management, customer segmentation, credit scoring, personalized banking, process automation, etc. are some of the advantages of AI and its technologies that are leveraged by the finance sector. 

Today's business world relies heavily on software, which is widely used in enterprise applications and products. As technology evolves rapidly, software development teams are under increasing pressure to deliver solutions that are both faster and of superior quality. They often grapple with issues such as functional issues, security vulnerabilities, and technical debt.

Conversational systems that leverage Artificial Intelligence (AI) have helped automate a wide range of business processes, especially those involving interactions with the customer. Natural Language Processing (NLP) comes into play for a majority of these processes, but it is often hindered by functional hurdles. Reinforcement learning is a method for navigating these hurdles to make NLP-driven business processes more seamless.

Take a look around. What's in your sight? A TV? Maybe your smartwatch, washing machine, or a Bluetooth speaker? All of these gadgets have something in common: they run on embedded systems. They're all over the place, quietly making our modern lives run smoothly. You probably interact with dozens of these devices every day, without even realizing it.

In today's rapid development cycle, Continuous Integration and Continuous Deployment (CI/CD) have become essential practices. While tools like Jenkins and TeamCity have been mainstays, AWS CodeBuild and AWS CodePipeline have emerged as revolutionary alternatives. 

Not long ago, banking was a rather traditional affair. People would visit physical bank branches, wait in lines, and fill out paperwork for even the simplest financial transactions. The world of banking was rooted in physicality, with brick-and-mortar branches serving as the epicenters of financial operations.

There are Yottabytes of sensitive data being generated from the interfacing of humans with machines. For cost-effective and optimal enrichment of this data, Machine Learning (ML) algorithms are our best bet. One of the most reliable categories of ML algorithms is clustering algorithms, irrespective of the complexity of data.

In the daily routines of healthcare professionals, the handling of sensitive patient data is a critical responsibility. However, storing this information on local computers, once the norm, has become a risky proposition. The reason? A growing wave of ransomware attacks that have the power to paralyze entire healthcare institutions. Therefore, if you're a healthcare practitioner, safeguarding your organization's data by backing it up with a HIPAA-compliant cloud storage service is not merely a wise choice; it's imperative.

Less than two decades ago, Deep Learning (DL), or the simulation of human neural networks, was only a concept limited to theory. Fast-forward to the present day and it is being leveraged to solve real-world problems such as converting audio-based speech to text transcripts and in the various implementations of computer vision. The underlying mechanism behind these applications is known as the Attention Mechanism or Attention Model.

Natural Language Processing (NLP) is a pre-eminent AI technology that enables machines to read, decipher, understand, and make sense of human languages. From text prediction and sentiment analysis to speech recognition, NLP is allowing machines to emulate human intelligence and abilities impressively.

The Artificial Intelligence (AI) ecosystem today is geared towards optimizing the capabilities of generative AI across various industries and everyday utility. Generative AI services are utilized for creating endless variations of ad copy, realistic image generation, refining low-quality images, and much more. Setting up the pace of generative AI advancement are Diffusion Models which help develop AI solutions that are pushing the boundaries of innovation.

In the healthcare domain, there are two significant challenges that come into view: the concern of high costs and the ongoing effort to enhance patient experiences. Despite the abundance of data gathered from various sources like health records, insurance claims, medical trials, and interconnected devices, finding practical insights to improve patient outcomes and operational efficiency remains a hurdle.

In recent times, the way healthcare works has changed significantly. Instead of solely relying on traditional methods such as paper records and telephone communication, the integration of software and digital technology has brought about a complete transformation in the delivery of healthcare services.

The way businesses interact with their customers has drastically changed in recent years, thanks to the rise of chatbots. Once considered a novelty, chatbots have become a crucial part of the digital landscape, changing how companies provide customer service, sales support, and more.

To me, nothing seems more fascinating than the fact that machine, a manifestation of human knowledge, could be developed to such an extent that it is able to be on par with human. Be it self-driving vehicles, the infamous Chess match or Google's Alpha Go, technology is giving end-to-end competition to humans. With the rapid advancement of technology and automation, there is rarely a field left untouched by machines. One such field is the healthcare industry.

In the not-so-distant past, virtual reality (VR) was mainly associated with gaming and entertainment. However, today, VR has become a driving force in the healthcare industry, along with other significant healthcare technology trends.

Throughout history, medical devices have played a crucial role in improving patient outcomes. From the stethoscope's introduction in the early 19th century to the invention of X-ray machines and pacemakers, these physical devices have completely changed the way healthcare is delivered.

From our smartphones to smart homes, technology has permeated every aspect of our lives, and now, it's making a profound impact on how we approach our health and well-being. Among the numerous technological advancements, one that stands out for its transformative potential in healthcare - wearable technology.

Even before the pandemic hit, healthcare organizations were already embracing digital transformation strategies to provide patient-centered experiences and ensure their survival in a competitive landscape. However, when the world faced an unprecedented crisis, hospitals and health systems had to kick their transformation into high gear. In a matter of months, the healthcare industry experienced a digital revolution that would normally take years to accomplish.

Artificial intelligence (AI) has made waves across industries, revolutionizing various fields of work, and the cybersecurity sector is no exception. With its rapid evolution, AI has become a driving force in shaping the future of cybersecurity. As organizations strive to safeguard their sensitive data and protect their systems from the ever-changing threat landscape, the need for AI-driven patch management has become increasingly apparent.

In a world where machine learning is ubiquitous, the power of this technology is undeniable. From helping diagnose illnesses to predicting customer behavior, machine learning has disrupted countless industries with its endless possibilities. However, with great power comes great responsibility, and one of the greatest challenges facing machine learning is its susceptibility to deception.

As our reliance on technology intensifies, the demand for faster and more powerful solutions becomes increasingly pressing. While classical computers have served us well, they face inherent limitations when tackling complex problems. That's where quantum computing comes in—a paradigm that taps into the enigmatic principles of quantum physics to unlock computational power.

Life can be a rollercoaster ride, filled with ups and downs that can affect our mental health and well-being. However, not everyone has access to the resources and support they need to cope with life's challenges. While traditional therapy and counseling have been the go-to options for many years, it has some drawbacks.

ChatGPT has undoubtedly captured the world's attention, but the recent announcements made by Google during the Google I/O event were truly remarkable. Alongside the highly-anticipated unveiling of Google Pixel products, the tech giant astounded attendees with its latest advancements in AI.

The shift towards cloud-native architecture has gained significant momentum as organizations worldwide recognize the transformative power of cloud computing. By harnessing the power of cloud computing, businesses can unlock a plethora of benefits, enabling them to thrive in today's evolving technological landscape.

At various stages of the buying journey, retail customers today switch between devices quite frequently, sometimes in the middle of a single session. Retail companies must align their digital platforms as well as physical outlets in a way that allows this kind of switching without losing details about any of the saved cart items or billing information. Omnichannel commerce that unifies disparate points of interaction is not just a feature but a default requirement to operate in the e-commerce arena.

As the popularity of public cloud services soared and businesses rushed to migrate their applications, the practice of monitoring and reporting on cloud expenses swiftly became an essential task for every team. However, what many organizations soon realized was that while a cloud migration may offer savings on data center costs, it also presents a whole new set of financial challenges to navigate.

Artificial Intelligence (AI) solutions that generate increasingly original content in the form of images, videos, and text have evolved into mainstream technologies today. The underlying innovation that has made this possible is Generative AI, a cutting-edge technology forcing several industries to rethink the way they function while easing workflows for others.

In the vast landscape of e-commerce, where industry giants dominate and small businesses often struggle to find their footing, a beacon of hope has emerged—the Open Network for Digital Commerce (ONDC). Endorsed by the Indian government, ONDC aims to level the playing field by providing small merchants and mom-and-pop stores with the technological capabilities typically reserved for their larger counterparts such as Amazon.

Around the world, businesses have been revving their implementation of technologies that leverage Artificial Intelligence (AI) in some way, shape, or form. While there are several enterprise benefits to the forward momentum of AI tech adoption, there is a laundry list of regulations and compliances to adapt alongside it. Organizations must demonstrate a readiness to adopt AI responsibly.

Monitoring application performance has always been a crucial aspect of software development. However, with the rise of distributed systems and cloud-native architectures, it has become increasingly challenging. The complexity of modern software systems has made it difficult to collect and analyze telemetry data effectively. This has led to a growing need for a standardized observability framework that can provide deep insights into system behavior. This is where OpenTelemetry comes into play.

In today's interconnected world, it has become crucial for brands to maintain relevance and uniqueness across all communication channels while ensuring a consistent and seamless customer experience. However, despite having advanced analytics systems and marketing automation programs, a significant challenge remains—connecting the dots and unifying data from various disparate systems.  

In the current healthcare landscape, personalization of patient care has become a top research priority and finds a place in active discussions around the improvement of healthcare workflows. The Digital Twin concept is at the forefront of these discussions as it is paving the way for the creation of groundbreaking digital applications that facilitate enhanced precision medicine, clinical trials, and population health.

The extent of innovation and advancement in Augmented Reality (AR) and Virtual Reality (VR) wearable technology has been explosive in the last five years alone. The latest entrant in this domain of technological disruption comes in the form of the Apple Vision Pro; a spatial computer vision-enabled headset that is far more than what meets the eye.

Running an e-commerce business is more than just having a website and selling products online. To keep pace with the competitive landscape, it’s essential to track and analyze key performance metrics to make informed decisions and drive business growth. 

Software applications have become the backbone of most modern businesses, powering everything from customer-facing applications to internal operations. However, as the complexity and scale of software applications continue to grow, so do the challenges that organizations face in delivering reliable and scalable software solutions. In response, platform engineering has emerged as a key solution.

User data and intellectual property in the IT industry have traditionally been protected behind a layer of perimeter security strategies. However, the global movement of development environments to hybrid cloud-based architectures has proven perimeter security measures to be inadequate. Increasingly IT companies are shifting to zero-trust network security measures with the globally recognized motto of "Never trust, always verify".

Every new technological innovation in any industry is dependent on the availability of infrastructure to support said innovation. For instance, Electronic Medical Records (EMR) could not have been a reality without the requisite facilitation of storage and performance-enhancing tools. Solutions implementing Software-as-a-Service (SaaS) is one such requirement for innovations in healthcare.

From its early days as a concept rooted in science fiction to the practical applications we witness today, AI has come a long way. This evolution has been driven by the desire to create intelligent systems that can simulate human-like cognitive abilities, learn from data, and adapt their behavior in response to new information and changing environments.

As the world around us changes rapidly, our aging populations, as well as public health crises ramp up the workload of providers, causing patient safety risks and negatively impacting patient satisfaction.

There exists an untapped potential for modern technology to propel changes in the healthcare system to increase productivity, improve the efficiency of care delivery and enhance patient satisfaction. And, Artificial Intelligence (AI) is one such technology that has offered a real opportunity for medical organizations to deliver better quality care.

Compared to traditional brick-and-mortar retail stores, digital retail or e-commerce apps and websites are at an advantage because of the vast amounts of data they produce and also work with. So, e-commerce companies have immense potential to quantify the effectiveness of their strategies for growth and customer acquisition by availing the benefits of reliable Business Intelligence (BI) tools.

The arrival of Industry 4.0 heralds a new era of the industrial revolution, where rapid advancements in connectivity, mobility, AI, and ML are reshaping the business landscape. However, amidst this swift progress, we must also navigate the risks that arise from increasingly complex and automated systems that demand unwavering reliability and stability.

In today’s expanding global marketplace, companies face constant pressure to maximize efficiency, reduce costs, and enhance customer satisfaction. One methodology that has proven effective in achieving these goals is lean manufacturing, which focuses on the elimination of non-value-added activities and the continuous improvement of processes.

As healthcare progresses towards digitization, patients increasingly take control of their treatment decisions. To meet the needs of these empowered patients, the healthcare industry is turning to digital health solutions. Digital health refers to the use of technology to improve the delivery of healthcare, and one important subset of this field is digital therapeutics (DTx).

FinTech solutions have been revolutionizing financial services as we know them, thanks to their pairing with AI, the blockchain, IoT, and other inventive technologies. The latest technological entrant that is disrupting the FinTech paradigm is Embedded Finance. Embedded finance is increasingly helping simplify the payment processing workflow and opening up newer opportunities for better customer service.

With the rise of digital platforms and the increasing demand for online services, companies are now under pressure to deliver high-quality software products at lightning speed. However, traditional approaches are no longer enough to ensure the desired level of quality. This is where TestOps swoops in as the key solution to address this challenge.

The latest concept taking the world of Artificial Intelligence (AI) by storm is the development of intelligent systems that can be trained to perform a vast array of tasks instead of just one. These are collectively referred to as Artificial General Intelligence (AGI) and recent innovations have been inching toward the end goal of surpassing natural or human intelligence.

As a healthcare provider, you know that having the right tools in your toolkit can make all the difference when it comes to delivering top-notch patient care. But with technology constantly evolving, it can be tough to keep up with the latest advancements – and to make sure that the tools you're using are up to par.  

In the world of business, the race to stay ahead is a never-ending one. And in the technology landscape, it's a full-on sprint! That's why CIOs are constantly on the lookout for emerging technologies to give their organizations the edge.

Healthcare providers and their recipients are globally welcoming the increased adoption of Artificial Intelligence (AI), Virtual Reality (VR), blockchain, and so on. The field of healthcare has always been at the forefront of technological innovation, with advancements constantly being made to improve patient outcomes and quality of care. 

To succeed in today's competitive business environment, it's critical to provide superior customer service. One of the keys to delivering top-notch customer service is to enable your customers to search for their preferred product or service quickly and easily. However, this can be a daunting task if your organization has an enormous product base, making it challenging to access relevant information quickly.

The frequency of patients seeking access to telehealth services has risen dramatically since the pandemic in 2020. Consequently, there has been a consistent increase in the development of two-way telecommunications technologies to enable better Remote Patient Monitoring (RPM) and care delivery. 

With the recent advancements in chatbots enabling unprecedented levels of precision and innovative problem-solving, several verticals of the technology sector have been reaping the benefits. The wildly popular domain of DevOps is the newest entrant into the slew of sub-sectors leveraging the immense potential of chatbots.

Shopping has always been about the experience. Whether it's the thrill of discovering a new product, the satisfaction of snagging a good deal, or the convenience of finding everything you need in one place, retailers have always sought to provide customers with a memorable experience. And now, with the help of cutting-edge technologies like artificial intelligence, they're taking that experience to the next level.  

In the intensely competitive business landscape of today, organizations need end-to-end automated workflow management to give themselves a fighting chance. Intelligent Automation is the one-stop solution to ensure increased agility, enhance customer experience, and boost overall productivity for your business. However, intelligent automation without the necessary orchestration leaves much to be desired with regard to holistic success.

In the current market scenario, every day tends to bring a brand new innovation in Artificial Intelligence (AI) and Machine Learning (ML) to the forefront. Beyond their impact on various industries, these radical changes in AI and ML are affecting our daily lives as well. The most significant branches of AI such as Speech Recognition, Voice Synthesis, Intelligent Assistants, etc. are going through a ton of pioneering innovation at this very moment.

The growing use of artificial intelligence(AI) in sensitive domains such as healthcare, hiring, and criminal justice has sparked a debate about fairness and bias. However, it's worth noting that human decision-making in these areas is also susceptible to biases, which can be influenced by societal norms and unconscious beliefs.

From social media to mobile apps, technology has become an integral part of our daily lives. With such widespread use, it's no wonder that businesses are investing more than ever in digital products and experiences. However, with great power comes great responsibility, and businesses must ensure that the digital products they create are designed with ethical considerations in mind.

Electronic Health Records (EHR) have completely transformed the way hospitals operate and enhanced the quality of care delivery. Scheduling appointments, allocating prescriptions, and viewing diagnostic data have all become simple processes because of EHR-based digitalization. However, there remain many bottlenecks that have stood in the way of seamless industry-wide adoption of EHR.

While observability is an increasingly crucial part of DevOps-based software development, the associated costs are growing faster than infrastructure costs. IT organizations today face the challenge of balancing innovative observability strategies with maintaining sustainable business models.

Schools deal with multiple parallel workflows and involve various interconnected stakeholders such as teachers, students, and administrative staff. While serving students with optimal education, schools also need to optimize the management of the institution's business side to better serve everyone, which necessitates the need for School Management Software (SMS) and systems.

Edge computing has become increasingly popular in the digital landscape and taken several industries by storm, especially due to the surge of data generated by IoT devices, smartphones, and other endpoints.

The DevOps paradigm has predominantly been focused on delivering high-quality software products to customers faster through shortened release cycles. Ensuring continuous security for these products is an essential aspect that is implemented by the DevSecOps domain. But when it comes to security in the product's design and architecture you need a more dedicated system such as that offered by Threat Modeling.

Security breaches have become increasingly common, causing organizations to seek a proactive approach to protect their sensitive information. The increasing number of cyber attacks, data breaches, and security vulnerabilities have exposed organizations to significant financial, legal, and reputational risks.

Advancing user requirements and the need for tighter security measures across the internet is making it crucial to shift from monolithic to DevOps-based software development. Additionally, microservices architecture is finding an essential role to play in this shift. Amidst all these developments, it is important to consider the need for boosting transparency through better DevOps observability for microservices.

From natural language processing to computer vision and beyond, there is a wide range of AI technologies that are being used to transform businesses across various industries. One of the latest development in this space is ChatGPT, which has recently made significant strides in advancing AI capabilities. With its recent updates i.e., GPT-4, enterprises are able to unlock new possibilities and stay ahead of the curve in an ever-changing business landscape.

The unprecedented proliferation of data generated by various digital sources has left businesses grappling with the complexities of managing large volumes of data. According to a report by IDC, the digital universe data is expected to reach 180 zettabytes by 2025. This massive amount of data presents significant challenges for organizations in terms of storage, processing, and analysis.

The emergence of the Continuous Integration/Continuous Deployment (CI/CD) pipeline has been a major game-changer in the software development sphere. It allows businesses to meet rapidly-evolving requirements and ensure steady growth by enabling continuous innovation at scale.

Generative Artificial Intelligence (AI) has revolutionized the way businesses interact with their customers, and Salesforce is no exception. As one of the world's leading CRM platforms, Salesforce has been leveraging generative AI technology to enhance its features and capabilities.

Enterprise software systems that help manage the workflows of large-scale organizations need Site Reliability Engineering (SRE) for ensuring their availability, resilience, and of course, their reliability. SRE involves monitoring the software system's performance and performing code fixes whenever problems arise. So, it is no surprise that observability is a crucial aspect of SRE and requires apt methodologies and automation tools to enforce.

With the growing need to understand customer needs and preferences, organizations are increasingly relying on CRM data to improve their sales and marketing strategies, manage customer interactions, and gain a competitive edge.

ChatGPT, the conversational Artificial Intelligence (AI) developed by OpenAI and backed by Microsoft, has taken several industries by storm. A number of AI solutions are set to change how businesses function and ChatGPT is expected to rule the roost for the next decade or so. Whether engaging the customer in new ways, enhancing workflows, or assisting with coding, the potential applications of ChatGPT are endless.

The integration of digital technology has become an integral part of every industry, whether it is for conducting business, medical implementations, or customer engagement. When it comes to medicine, EHR (Electronic Health Record) systems have become the backbone of healthcare organizations.  The implementation of EHR systems is now no longer seen as a luxury but a necessity for those looking to tap into the digital health space.

The heightening investment in the retail industry has been increasing the size of the convenience stores market in recent years. Leading the growth spurt is the 7-Eleven chain of 70,000 convenience stores spread across 17 countries in the Asia Pacific and Atlantic markets. The convenience store giant has been leveraging data analytics through a variety of approaches that ensure steady growth and proper convenience for its customers.

With the advent of Artificial Intelligence (AI), businesses have dramatically changed their operations and processes. As AI applications and tools have developed, businesses have been able to make more informed decisions and automate repetitive tasks, resulting in more efficient and effective operations. One of the most advanced AI algorithms that have gained prominence across various industries is Generative AI.

Having a robust sales strategy is the key to the success of every business. Therefore, in order to maximize the sales teams’ success, organizations have become increasingly dependent on their sales operations.

Conversational Artificial Intelligence (AI) is unarguably the most talked about subdomain of AI today. While ChatGPT's introduction by Microsoft in November last year shifted the world's attention to AI more than ever before, the discussion is further being ignited with Google's answer to it, termed Bard.

Conversational Artificial Intelligence (AI) has been making waves in the technology industry, especially with the introduction of Microsoft's ChatGPT. However, this chatbot has now found stiff competition with the announcement of a new rival that has been developed by Google, known as Google Bard. The next few months will witness how Bard will fare against ChatGPT in producing optimized, relevant results to all kinds of user queries.

It is well known that Electronic Health Records (EHR) arose out of the need for a more portable and integrable alternative to paper-based clinical documentation. While it is a no-brainer for enterprise healthcare organizations to opt for EHR, a fast-increasing number of small practices are also catching up to this trend. 

With the latest advancements in Natural Language Processing(NLP), generative Artificial Intelligence (AI) and other foundation models have completely changed the flow of business processes. By automating tasks that were previously done by humans, generative AI services have increased productivity and efficiency, reduced costs, and created new growth opportunities.

Every organization seeking to boost its business revenue needs a highly dependable and well-informed sales team. To maximize its potential, the sales team needs access to holistic and accurate customer data with considerable hygiene to rationalize account planning and increase Return on Investment (ROI). Simply put, a robust strategy for enriching the organization's Customer Relationship Management (CRM) data must be in place.

Daffodil Software, a leading software engineering company based in India, has achieved the ISTQB® (International Software Testing Qualifications Board) Silver Partner status. This ISTQB®'s Partner Program acknowledges Daffodil’s impeccable software quality assurance capabilities and affirms its commitment to bringing customers trust and satisfaction.

Is your team struggling to gain visibility into the performance, availability, and health of cloud applications & infrastructure? Do you need to automate cloud monitoring so that your team focuses on its core competency? If yes, then this guide will introduce you to the business-critical components of cloud monitoring and how you can leverage them to ensure automation & efficiency in operations. 

With the rise of cyber attacks, every organization knows that cybercriminals are becoming smarter and more malicious. They also have more resources at their disposal and can better hide their traces and evade detection. Therefore, having anti-virus software or a firewall is no longer enough for your business data security. Modern businesses require advanced approaches to cybersecurity and one of the effective solutions that play a major role in reinforcing system security is Pentesting or Penetration testing.

The key to steady growth and lasting success is building a positive relationship with your customers. Therefore, organizations are leveraging CRM software that streamlines consumer interactions. And, one of the most powerful CRM platforms in the market is Salesforce.

UX design is a broad and inclusive field and every organization in the world is affected. Modern design sensibilities permeate many industries and sectors. Good UX design enhances profits, client and user retention, satisfaction, and loyalty. With expert UX designers a business can improve the quality, measurement, and usability of its goods and services. 

Daffodil Software, a global provider of software engineering solutions, has been featured yet again in Everest Group’s PEAK Matrix® Assessment 2022. Daffodil has been recognized for the second time since its debut assessment in 2021. The provider has been positioned as an 'Aspirant' in Software Product Engineering Services for BFSI, Healthcare, and Retail sectors.

Data is a valuable asset for any organization, and using a range of dependable data sources can help improve the reliability and accuracy of the data. Data sources provide a way to enrich a dataset with additional data, which ultimately enhances the CRM data quality.

A large chunk of software development today takes place in cloud-based environments, with cross-organizational networks for resource sharing and data exchange. This opens up the applications and resources to several cyber threats that exploit common vulnerabilities and attack routes. Therefore, solutions for application security also referred to as AppSec, have become a necessary component of the software development lifecycle.

The current IT landscape necessitates faster development and delivery of software products as well as better collaboration between IT and operations teams. While DevOps offers the exact methodology for this, its implementation for the full automation of the software deployment pipeline requires optimal management of the processes and resources involved.

Drupal is amongst the most popular, open-source content management platforms. It has established itself in the market and has been growing since its launch. Community support for Drupal has made great strides and is always on track to stay up to date with the latest technology integrations. 

Customer Relationship Management (CRM) is a tool that serves as the single source of truth for all your organizational data while enabling sales teams to drive better performance. Bad CRM data, if utilized for your business initiatives, can cost your organization severely in terms of client relationships, loss of the market base, and ultimately, sales revenue.

Businesses are shifting their IT infrastructure to the cloud for the certain benefits that this new era of computing has. While cost, compliance, and performance are some of the obvious reasons for the change, a large share of the business community has failed to realize these gains. 

Business scalability, accessibility, and cost efficiency are just some of the often-touted economic benefits of cloud computing. Furthermore, these benefits transcend economic turbulence to a great extent, simply by enabling businesses to access computing resources whenever they need them. It also explains why cloud computing is the fastest-growing computing model, with global revenues expected to reach $1 trillion by 2026.

Your organization's sales team deals with large stores of data around customers and prospects captured in your Customer Relationship Management (CRM) system. Say your customer changes their role in their organization, their contact details, or other aspects of their profile - their data in your CRM can become stale if not updated and enriched periodically.

Technologies have become the primary vehicle of progress, which is true. However, there’s also the flip side of the coin: early subpar technological choices, if left unpruned, can result in technical debt. It may even lead to higher development costs, long time-to-market, and reduced productivity. With so much at stake, managing technical debt should be a priority both for CTOs and software developers.

The digital revolution has brought disruption in every field and mental healthcare is no exception. COVID-19 has provided us with an opportunity to improve the access to healthcare services.

Customer experience is a core focus area for businesses today. The perception of your brand or organization is an essential differentiator to achieving ideal outcomes in today's customer-centric business landscape. Accordingly, only with Customer Relationship Management (CRM) software that ensures efficient Customer Experience Management (CXM) can you build a brand or enterprise holistically.

As organization infrastructure and its processes get more automated, data quality becomes the differentiating factor between a successful business and a failure. The accuracy and timeliness of CRM data become pivotal for the organization, be it for creating customer personas or optimizing the outbound sales strategy – the success of your business all rides on data and its quality.

The operational and infrastructural overhead involved in running and maintaining cloud-based solutions is often massive. There is considerable effort and expenditure in the construction and scaling of storage systems for tons of user metadata and other in-house files. Dropbox, the file hosting pioneer, is a success story about how cloud costs can be minimized by intelligently managing your infrastructure.

The proliferation of digital transformation has made system reliability an essential component of any organization's bottom line. These rapid changes have become a new benchmark for success and efficient incident response is key in sustaining site reliability through these changes.

Increasing a website's visibility in search engine results is the goal of search engine optimization (SEO) for SERPs. Increasing your medical website's authority and credibility is a key part of medical SEO.

The automation of time-consuming tasks involved in marketing campaigns using Customer Relationship Management (CRM) software is an emerging trend. In particular, there has been an increase in the adoption of Salesforce for marketing automation because the resultant benefits far outweigh the associated costs and efforts involved in its integration.

With the continuous development in DevOps practice and its unparalleled benefits, now every organization wants to embed it in their software development lifecycle to make the deployment of software even faster and more efficient.

As software development gets complex, we see cross-functional teams collaborating to enable faster product delivery, optimize cost, manage infrastructure, and for several business benefits. An example of such a joint effort is ‘Cloud FinOps’ wherein finance, engineering, technology, and business teams aim at cloud financial management. 

Technology is embedded in every aspect of our lives. We rely on it for almost everything, especially to communicate with others. Simply, take the example of Siri or Alexa that we use in our day-to-day lives. Ever wondered how these virtual assistants understand and interpret our commands? How do they pick up terms and carry out the tasks according to our instructions? For example, when we ask Alexa to set an alarm for us.

Rolling out a new software system, infrastructure, or technology solution for an industry such as healthcare or personal finance has critical end-user outcomes. Technology Readiness Levels (TRL) refers to a robust scale often used by pioneering software organizations throughout their development efforts.

PyTorch and TensorFlow are two of the most widely relied-upon frameworks for Deep Learning (DL) research and industry implementation. While both these DL development frameworks haven't been around too long, they have made a significant mark in AI/ML research and innovation.  However, when it comes to which between PyTorch and TensorFlow is the better framework, there has not been a clear consensus.

We are living in an information-driven era. Millions of bytes of data get generated every second, and it becomes more challenging for organizations to keep vital information, software, and operating systems up and running on conventional in-house servers. That’s where cloud computing services come into play. 

Behind the success of any business, there lies a critical element and that is customer service, a vital factor that can make or break an entire enterprise. With everything being digitized in this new era, customers are no more dependent on the brand or company for services. Why? because they have a myriad of options to choose from for a single product or service. And if they aren't satisfied with the experience, they are just a click away from the next available option.

Big data adoption is increasing rapidly across organizations of all sizes. However, the method and distinction between obtaining Business Intelligence (BI) and employing Data Analytics (DA) to make actual business decisions with an impact are getting lost in translation. While both terms are used interchangeably, BI and DA are essentially distinct in many ways.

Personalized marketing campaigns are proven to drive performance and better customer outcomes. A Mckinsey report says that customers expect businesses they buy from to recognize them as individuals and their interests. 

Businesses around the world spend billions of dollars every year on cloud infrastructure services, storage, and computing resources. They can gain an edge over each other with effective cloud infrastructure without any wasteful cloud spending. There are two rising approaches towards cloud cost optimization today that they can choose from; Capital Expenditure (CapEx) and Operational Expenditure (OpEx).

The healthcare systems market has witnessed a large-scale shift towards more patient-centered care models in the recent past. The Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) is one of the most reliable ways to measure patient satisfaction which can help digital patient-facing and patient-monitoring doctors' apps meet leading market standards.

Personalized customer experiences are increasingly in demand. Brands must develop relationships with consumers and deliver experiences that go above and beyond and that is where Salesforce can help. To manage the whole customer journey, Salesforce Experience Cloud supports the development of interconnected and personalized digital experiences.

The rapid rise of Artificial Intelligence (AI) and Machine Learning (ML) technologies has led to the growing adoption of TensorFlow, an end-to-end ML platform. TensorFlow enables AI developers to accelerate ML tasks at every step of the workflow but also has certain shortcomings and understanding both sides is very essential to fully leverage this platform. 

An essential component of textual and verbal data is context and understanding the context requires some amount of sentiment analysis. To comprehend and categorize subjective feelings from communications data, Natural Language Processing (NLP) and Machine Learning (ML) methods have been used in the past. Sentiment analysis is frequently used in professional settings to comprehend customer evaluations, identify email spam, etc.

The domain of Artificial Learning (AI) known as Deep Learning (DL) is fast gaining acceptance as a go-to technology for a number of use cases. When it comes to use cases where image data makes up most of the input fed to a system, a DL technique known as semantic segmentation offers accurate implementation outcomes. 

More than ever, marketers realize the importance of tracking metrics to measure the performance of their campaigns. These values give an idea of how aligned the marketing efforts are with the business goals, be it customer experience, acquisition, lifetime value, or brand awareness. 

There are a variety of routes a customer may take to get to the service or product offered by your organization. As a customer embarks on this journey, a sales representative has the task of finding instances to interact with them and find insights about their likes and preferences. An effective customer journey map can give you these insights promptly to forecast your customer's path to your organization.

Custom user services are at the core of the success stories of a majority of internet companies like Netflix, Facebook, and Amazon today. This is why online platforms vying to stay at the pinnacle of their respective sectors need to build effective recommendation systems or recommendation engines.

During the COVID-19 pandemic, we have seen intense pressure on the healthcare management sphere. The chaos that the pandemic brought resulted in many challenges for the healthcare system. Therefore, healthcare providers had no choice but to adjust their methods. Instead of in-person services, remote healthcare delivery was adopted to avoid transmission.

For all types of businesses, be it storefront establishments or online retailers, generating buyer interest is not enough. You need to develop a robust sales funnel to attract visitors first and then ultimately convert them to loyal customers who provide repeat business. There is a series of thought processes that customers go through before they buy a product or avail of a service which constitute a sales funnel.

Quintillion bytes of data get generated in a day, now imagine handling those data manually! Impossible right? It would require immoderate manpower with no surety of accuracy. As we all are aware of the fact that organizations these days run on data and thus it is a vital task to manage and organize it. The data is received from multiple sources and stored at a centralized repository for usage, such as a data warehouse.

With the pandemic at bay, we have witnessed the emergence of digital transformation almost everywhere. Especially, in the IT industry where the organization was still stuck with the legacy system using outdated technology and generating unlimited data but not utilizing it properly. Managing data day in and day out is just not something that immediately derives value to an organization. But, with Data Modernization, it can do wonders for the company. 

The SaaS software development domain is perpetually transforming in terms of environment, orchestration, scaling, and management. One of the most important developments to occur in this field is the introduction of the "run anywhere" paradigm that came with virtual machines, followed by Containers, and further revolutionized by Container-as-a-Service (CaaS) solutions.

With healthcare organizations (HCOs) shifting toward patient-centered care, it is important to consider and measure the factors that impact or influence the quality of care. That is why, HCOs value patient satisfaction, as much as they value patient safety and clinical workflow improvements. 

Dated or legacy tools, systems, and operational methods are not enough to deliver the quality of optimized and innovative financial services that today's digitally savvy customers expect. So, multitudes of financial organizations are leveraging Artificial Intelligence to raise the growth rate and dynamism of IT operations through AIOps.

Nowadays, businesses can produce data analytics based on big data from numerous sources. Once they acquire access to all of the requisite data sources for analytics and business intelligence in order to make better decisions. The transfer of this data is facilitated in a streamlined manner by different data ingestion strategies.

Businesses can save plenty of time and millions of dollars when they use data science to better understand and improve their processes. With the age of the democratization of data, there have been several emerging trends defining enterprise data manipulation and data engineering.  

Artificial Intelligence (AI) and data engineering are closely interlinked. On one hand, making sense of unstructured data is the process known as data science or data engineering. On the other side of the same coin, AI-programmed computers have the ability to learn as they go, getting better at solving particular sorts of problems as they accumulate more data. So one cannot exist without the other.

Machine Learning (ML) is an application of Artificial Intelligence that has the maximum number of use cases, in almost every industry. Healthcare, automobile, marketing, finance, agriculture, retail- all of them are leveraging the power of ML to automate tasks and bring agility to operations. 

Migrating to the cloud is the new normal for businesses of all sizes. Based on availability, compliance, performance, sovereignty, and other technical factors, businesses are making a choice for a relevant cloud services provider. 

In an SDLC, there are several testing frameworks that help to assess if the application is progressing in the right direction. It could be in terms of usability, security, compatibility, performance, integrations, etc.

Speech recognition is a technology that has been going through continuous innovation and improvements for almost half a century. It has led to several successful use cases in the form of voice assistants such as Alexa, Siri, etc., voice biometrics, official transcription software, and the list goes on. So what really is Automatic Speech Recognition and what are the underlying technologies that enable it?

The business landscape of the modern world is heavily customer-centric with marketing, sales, and customer support at the center of operations. Salesforce is the most sought-after Customer Relationship Management (CRM) software intended for these purposes. There are a variety of roles and responsibilities in an enterprise's Salesforce team and the Salesforce Administrator is usually at the helm of it.

Increasingly today, Financial Technology (FinTech) companies have been using mobile devices and applications as promotional platforms. Biometrics technologies are a necessary addition to the FinTech domain and have the critical task of enhancing security through the accurate identification and verification of the customer's identity.

A majority of healthcare data today, about 80% of it, happens to be unstructured. Most sources used for aggregating Electronic Health Records (EHR) require a considerable amount of pre-processing because most of them are not machine-readable. Natural Language Processing (NLP) and various Machine Learning (ML) techniques can, however, unlock all the potential enmeshed in unstructured data.

When it comes to seeking app-based medical solutions, physical health is often given precedence over cognitive or mental health. However, millions of people suffer silently from unseen, yet debilitating effects of mental illnesses. That is the pain area that the creators of the research-backed mental wellness app Wysa have chosen to address. 

Artificial Intelligence, in practice, is complex. There are several technologies and training methodologies involved. There is a high risk involved in algorithm failure. With this,  businesses want nothing but the best of AI practitioners when it comes to executing the use of AI technology. That’s when the Artificial Intelligence (AI) Center of Excellence (CoE) comes into the picture. 

Post-pandemic, the retail sector has witnessed several transformations in the way it works, but the biggest paradigm shift has been the increased adoption of self-checkout systems. These systems give retailers the option of saving costs while enhancing the customer experience so that in-store resources can be better allocated.

Value-based care is the new normal in the healthcare industry. 

Healthcare stakeholders- patients, providers, payers, and suppliers are realizing the benefits of value-based care, and adopting it has become their major goal.

Innovative medical interventions that are new to the healthcare industry need to be evaluated through clinical trials before they are rolled out for real-time implementation. The field of clinical trials is going through a paradigm shift with digitalization. And electronic Trial Master File (eTMF) systems are a clinical trial documentation necessity for digitalization.

With a diverse portfolio of 15,00,000 companies, Salesforce has been holding strong in the cloud CRM market. The platform's uniqueness in reporting, visual data presentation, enhanced efficiency with automation, proactive service, etc. is some of the reasons why the CRM holds 20% of the overall market share in the domain. 

Organizations whose bottom line depends on quality software development need a robust methodology in place to assure bug-free product delivery for their clients. They must integrate a means to monitor the productivity and quality of software testing with a standard set of appropriate software testing metrics.

Most software systems of contemporary large-scale businesses function at full capacity as they have to deal with complex computations across distributed system architectures. In these cases, system failures have a high likelihood with the cause of failure remaining largely elusive. Chaos engineering is a field that is finding widespread application as a reliable means to be prepared for such failures in advance.

Vulnerabilities in software applications are common. 85% of security incidents happen at the application layer. With the right security measures, the operations and development team can discover these vulnerabilities and fix them at an early stage in an SDLC. 

Organizations irrespective of industry accumulate data across categories such as purchase history, customer contacts, lead journey statistics, buyer behavior, and so on. The most preferred mechanism to track and leverage this data today is through Customer Relationship Management (CRM) software such as Salesforce CRM. What makes Salesforce a highly viable CRM solution is the suite of low code application development tools it provides.

There are 400 million active websites on the internet and all these websites are susceptible to vulnerabilities of one kind or the other. Even the microscale misconfigurations, such as improper validation, disclosure of server versions, and using vulnerable software libraries can lead to drastic security issues. To avoid the consequences, there is Application Security Testing. 

An organization’s data is its most essential resource and its day-to-day operations produce data in abundance. But to derive actionable insights that help make data-driven decisions, it is important to remove inefficiencies in the way the data is consumed and visualized. Business Intelligence, or BI, is a group of services that empower businesses to leverage their data for driving enterprise excellence.

Salesforce has been knocking its competition in the CRM world. Despite the existence of several industry-specific CRM solutions, Salesforce has been standing strong. One of the prime reasons for its success is its flexible integration services. 

Cybercriminals are constantly waiting for opportunities to exploit your organization's system security flaws. While the motivations of these hackers vary, be it political, financial, or just to gain notoriety, they all constitute a severe threat to your company's confidential data. There are several points of entry known as security vulnerabilities that are defined by the Open Web Application Security Project (OWASP) community.

Automation is driving the decline of mundane and repetitive tasks. And this holds absolutely true for a software development cycle. 

There is a provision to automate almost every aspect of a software development cycle- coding, testing, and even deployment.

Over the last half-decade, Artificial Intelligence solutions has made incredible advances in pivotal areas such as vision, video generation, linguistic processing, trend prediction, and so on. While AI attempts to make giant leaps toward matching human intelligence, industry insiders forecast the emergence of Artificial General Intelligence (AGI) as the next obvious step.

The global technology ecosystem has an insatiable demand for AI today but without the right training data and efficient AI model development, this domain cannot be utilized to its full potential. With fully actualized AI models that arise from high-quality build lifecycles, continuous innovation in AI fields such as Natural Language Processing (NLP) or predictive analytics can be ensured.

If you’re leading the marketing campaigns of an organization, you could relate to this! The prospect data received from website forms, surveys, web browsing, a list of event attendees, or advertising is not enough to create opportunities and convert a lead. 

Taking a business to a global scale involves navigation, not just through untraversed markets but also across language-based hurdles. Natural Language Processing (NLP) offers the utility of Machine Translation, wherein a source language is converted to a target language automatically with significant accuracy. It is the most viable solution to a fast and effective translation process without human intervention.

The accuracy of deep learning models depends upon the quality, quantity, and contextual meaning of the training data set. However, the scarcity of this up-to-the-market data set is one of the major challenges in building DL models. 

Salesforce is the world’s leading CRM with over 150,00 customers that include businesses, non-profits, and institutions. The cloud-based CRM unites sales, marketing, eCommerce, service, IT, etc. under a roof, enabling businesses to drive growth. 

Every business needs to continuously find ways to utilize a workday to make the most of it. So, workflow automation in various business areas is the go-to alternative for a lot of enterprises. The Customer Relationship Management (CRM) workflow for businesses, handled by marketers, customer service executives, and sales reps, also derives cost and time efficiency-related benefits from automation.

In today's advanced competitive world, the most valuable commodity is data. Data has many forms, from pictures of people and places to patient records and banking transactions. Machine Learning is a branch of Artificial Intelligence that can fully leverage this data to learn from it and enhance a business' bottom line.

As a Chief Technology Officer (CTO) of an organization, who is either driving the technology team of a startup or leading IT operations in a megacorp; continuously innovating is the goal. 

If you’re leading the software engineering cycle of a product or a software development firm, then the struggle to get answers to the following questions must be constant.

The software development and delivery pipeline of DevOps, also referred to as the CI/CD pipeline, ensures that market-ready software products are released for public use faster. Continuous Testing is a set of evaluation procedures in the DevOps pipeline meant to ensure the product's quality and to enhance the reliability of the outcome.

There are some key DevOps practices that enable organizations to automate and streamline the software development processes. Continuous Integration and Delivery (CI/CD), Continuous Deployment, Microservices, Infrastructure as Code (IaC), Containerization, Monitoring & Logging are a few of them. 

Today, we are at a point in the software delivery paradigm where most IT enterprises have implemented DevOps in their workflow in some way, shape, or form. DevOps teams are guided by various operating principles, the most prominent of which are Continuous Integration (CI) and Continuous Delivery (CD), or CI/CD.

A modern infrastructure that’s agile, flexible, and scalable is on the mind of every infrastructure & operations (I&O) leader. In a cloud-first and automation world, I&O leaders are introducing new ways to utilize and manage infrastructure. 

For technology organizations, cloud adoption is a go-to strategy for cost reduction, risk mitigation, and a more increased scope of scalability. Enterprise-ready cloud infrastructure highlights the maturity of a company's depth of technological prowess. Therefore, it is important to conduct periodic cloud readiness assessments.

The software development business is witnessing an ever-increasing adoption of various cloud-native methods for deploying applications, and containerization is one of the most sought-after of these methods. This has, however, opened up organizations using containers to potential cyber-attacks and other vulnerabilities, therefore promoting the need for container security. 

Containers have been solving real problems for IT operations teams. They are enabling faster deployment of applications, ease migration to the cloud, maximize scalability, reduce environment variables, improve code reusability, and a lot more. 

In today's competitive business landscape, success is directly affected by the accuracy that goes into your decision-making process. The best business decisions are driven by data aggregated from a vast array of white papers, market research reports, surveys, and domain experience. Augmented analytics is a leading data analytics subdomain emerging in the current market.

Experiencing the ill effects of the pandemic, the healthcare industry encountered a lot of changes. Transitioning with the industry was the way patients were engaged. Healthcare professionals wanted to have a more meaningful and optimal method to onboard patients in their healthcare journey. The focus today is on patient engagement. Why you might ask? 

Daffodil Software, a leading software engineering company based in India, announced that it has been featured in Everest Group’s PEAK Matrix® Assessment 2021. The company is positioned as an ‘Aspirant’ under the Digital Product Engineering Services category. 

As cloud adoption becomes a widely accepted norm amongst organizations, effective management of the cloud infrastructure is now at the forefront. Efficient management of the cloud leads to holistic resource optimization and the option of keeping a close eye on cloud infrastructure spending.

The new norm of practicing DevOps has recorded success in streamlining and improving the deployment cycle. With benefits like automation, faster-time-of market & incident responses, DevOps has had a positive impact on the overall delivery of an application. 

The industry standard amongst IT and cloud computing-based organizations dictate the need for rapid testing, deployment, and scalability of applications. At the moment, the best option at the disposal of these companies is container orchestration. The most popular and sought-after container orchestration tool in the market today is the Azure Kubernetes Service (AKS). 

DevOps introduced a change in the IT culture with the adoption of agile, lean practices for development. It emphasized people, tools, methodologies, and discovered how a balance between the development and operations team could bring a significant shift in a software development cycle. DevOps leverage automation tools to build a dynamic infrastructure. 

Digital transformation and cloud migration were the survival doors for businesses to escape the uncertainty of the COVID-19 pandemic. 

The healthcare industry is on a constant lookout for digital solutions to conduct the financial, administrative, and clinical operations of a hospital. These days, these are largely carried out by means of a set of informatics tools, collectively known as Hospital Information Systems (HIS). In addition to a  patient's health information, inventory and personnel data are also stored, managed, and transmitted across a HIS.

In the Banking, Financial Services, and Insurance (BFSI) sector every single transaction is of significance. Fintech solutions that allows the tracking and management of expenses, revenues, inventories, and personnel must be highly reliable. That is why financial institutions invest in Document Management Systems (DMS) or Enterprise Content Management (ECM) systems as a priority, so as to keep a secure record of each financial event and activity.

The latest innovation in AI, known as deep learning, helps AI models learn from a fairly large dataset of examples. This type of learning, however, does not generalize for conditions that were left out or missed out during the training process. The alternative to this is Transfer Learning, which is extensively witnessing its application in the field of Natural Language Processing (NLP).

The personal financial planning software market is witnessing the entry of a new generation of planning software. While many existing clients of the leading financial advisory firms are reasonably satisfied, there is a need for fintech software solution that helps serve more specific client needs. Incorporating uncertainties and building a robust financial plan are not the only requirements for reliable financial planning software.

Individuals and companies have been changing the way they manage their finances through WealthTech or wealth management technology. A digital ecosystem of finances consisting of technologies such as AI, big data, and SaaS, is governing the global flow of money. The trends surrounding this set of technologies are transforming and being reimagined continuously to meet global changes in financial governance.