As we move forward to the future where AI has already started reshaping various industries, image recognition technologies stand at the forefront of innovation.
According to a study conducted by Grand View Research, the global image recognition market size was valued at USD 45.02 billion in 2022 and is expected to expand at a CAGR of 13.4% from 2023 to 2030.
As we move into 2025, conversational AI is making waves across various industries. According to a report from MarketsandMarkets, the market for conversational AI is expected to grow from USD 13.2 billion in 2024 to USD 49.9 billion by 2030. This growth with a CAGR of 24.9% highlights the growing dependency on AI-based interactions that improve customer experiences.
According to MarketsandMarkets, with AutoML growing at such a fast pace, its market is set to reach an impressive USD 6.4 billion by the year 2028, at a compound annual growth rate (CAGR) of 44.6%.
This growth reflects the increasing demand for tools that automate the complex and time-consuming aspects of machine learning (ML), making it more accessible to businesses, developers, and analysts across industries. But what exactly is AutoML, and why is it taking the world by storm? In this guide, we’ll dive into the fundamentals of AutoML, explore its real-world applications, and look at the techniques that are driving its rapid adoption.
As per a report by Markets & Markets, the Cloud AI Market is growing rapidly with projections indicating a valuation of USD 274.54 billion by the year 2029, growing at a CAGR of 32.37%. A key driver of this growth is Generative AI, which is taking center stage in a variety of applications.
In general, AI models with their core in Gen AI, attract high cloud costs as they require a large amount of computation power to keep them running. Hence, there arises a need for business leaders to manage and optimize these expenses wisely.
Let’s peek into how adopting cloud cost optimization techniques can ensure the long-term success of your Gen AI-based applications and ways to do so.
Synthetic data is a term that's been buzzing around the tech world, especially in discussions about AI. So, what exactly is it?
Augmented Reality (AR) is the buzzword of tech industry today. The idea of taking digital or computer generated information (audio, video, images, touch, or haptic sensation) and then overlaying them to the real environment has been accepted enthusiastically. While AR can augment all five senses, but its present use cases are mostly visual.
Choosing between a data lake or a data warehouse can feel like trying to pick the perfect storage unit. Do you need a big and messy space where everything fits or a tidy one that only holds what’s important? While this might sound like a difficult decision to make, it doesn’t have to be one. Once these two concepts are understood, it will be easier to decide which is best for your business.
As we enter 2025, the role of restaurant apps has evolved far beyond basic ordering. Today, the best apps create an immersive and interactive experience, meeting diners' expectations for convenience, personalization, and innovation. These apps are reshaping how customers interact with their favorite restaurants from streamlined reservations to dynamic menus and AI-driven recommendations.
Ambient intelligence (AmI) is making our future exciting with smart, context-aware, and seamlessly integrated environments. It brings together artificial intelligence (AI), the Internet of Things (IoT), and context-aware computing, to enhance user experience and make our daily lives easier. AmI anticipates the needs and responds intelligently to the environment around us. It won't be wrong to say that the possibilities of what AmI can do are limitless.
Today, we will be exploring what ambient intelligence is, how it works, and its real-life applications. Also know the key differences between ambient intelligence, AI, and ambient computing, and how it has the potential to reshape our world.
In 2023, Netflix rolled out its new ‘Profiles’ feature where users could add up to 5 other users to the same subscription. This feature, considering its large-scale impact, was not rolled out for all users at once. Instead, they leveraged “Feature Flags”, or “Feature Toggles” to release the feature to a small subset of its users, that too in a phased manner. This gave Netflix the freedom to optimize user experience based on real-user feedback and the flexibility to switch off the toggle in case something didn’t go right.
While there are many other instances of how organizations are leveraging feature flags, it’s important to have a clear picture of what Feature Flags really are. Which is exactly what we’ll be discussing in the blog today.