TCS has made a big move into AI infrastructure through its subsidiary HyperVault. On September 5, 2026, the company said it will develop a large-scale AI data center campus in Hyderabad, Telangana, with a planned capacity of up to 1 gigawatt. The project will be built in phases on 264 acres of land and may involve investment of up to Rs. 70,000 crore.

The campus is being built for the fast-growing world of AI companies and hyperscalers, which are the large cloud and tech firms that need huge computing power. In simple terms, this is the kind of infrastructure that helps AI models train faster, run better, and scale more easily.

Why this project matters

The biggest reason this announcement stands out is scale. AI is hungry for power, cooling, and space. Traditional data centers were built for regular cloud and business workloads. AI systems need far more electricity and far more advanced cooling, especially when large numbers of GPUs are packed into one place.

HyperVault says the Hyderabad campus will use high-density, liquid-cooled infrastructure. That matters because liquid cooling can handle the heat from powerful AI servers more efficiently than older air-based setups. The company also says the campus will be designed around green energy and water-neutral principles, which is important in a market where energy use and sustainability are becoming major concerns.

For Telangana, the project is also a signal. It strengthens Hyderabad’s position as a serious digital infrastructure hub, not just a software services city. The company expects the build-out to create several thousand direct and indirect jobs across power, cooling, networking, construction, engineering, and operations.

What the startup wants to achieve

HyperVault’s aim is not only to host servers. It wants to become part of the foundation layer for AI in India. That means helping companies train models, run inference, and scale advanced computing without having to build everything from scratch.

This is a practical business move. For example, an AI startup working on voice tools or image recognition may not want to spend years setting up its own huge facility. A campus like this can give it ready infrastructure, faster deployment, and room to grow.

The idea also fits TCS’s broader strategy. The company has said HyperVault supports its “Infrastructure-to-Intelligence” approach, which links physical compute capacity with its cloud, engineering, and AI services.

Who else is in the race

HyperVault is entering a field that is getting crowded quickly.

CtrlS is one of the clearest competitors. It is expanding aggressively into AI-ready data center campuses and says it is building toward multi-gigawatt capacity, including major projects in Hyderabad and other Indian cities.

Yotta is another important player. It has positioned itself around sovereign cloud and AI infrastructure, with GPU-heavy offerings and large hyperscale data center sites.

AdaniConneX is also moving fast, with major data center plans in Hyderabad, Navi Mumbai, Noida, Chennai, and Pune. Its Hyderabad site alone is positioned for very large capacity.

What makes the race interesting is that it is no longer only about storage and uptime. The new competition is about AI density, liquid cooling, power access, speed of deployment, and the ability to support huge model-training workloads.

Conclusion

TCS HyperVault’s Telangana campus is not just another data center project. It is a sign that India’s AI era will need massive physical infrastructure, not just smart software. If the project moves as planned, Hyderabad could become even more important in the country’s digital economy.

The larger message is simple. The AI boom is now touching land, power, cooling, and construction, not just code. And in that race, the companies that build the strongest infrastructure may end up shaping the future of AI in India.

Key takeaways –

• HyperVault, a TCS subsidiary, plans a 1 GW AI data center campus in Hyderabad.
• The project will be built in phases across 264 acres and may involve Rs. 70,000 crore in investment.
• The campus is designed for AI companies and hyperscalers that need high-density GPU infrastructure.
• Telangana gains a major boost as an AI and digital infrastructure hub.
• CtrlS, Yotta, and AdaniConneX are among the other major players in this growing race.

Facts Input and Image Credit- Press Release

Data science is one of the fastest growing fields of expertise at the moment. We know that data science involves machine learning, but what’s the difference between these two fields of expertise?

In short, machine learning involves complex algorithms that “learn” from data in order to predict future trends and system behaviors. On the other hand, data science is the process of tackling and making sense of large collections of data. This includes data cleansing, preparation and analysis, which is, in part, machine learning.

In this article, we will unpack these two concepts, aiming to bring an understanding of what each term means and how they relate to each other.

Data Science vs. Machine Learning

Data Science

Data science as a field is difficult to define since it draws from so many different fields of knowledge.

Most data scientists know machine learning and understand multiple analytical functions. This person usually has experience in SQL database coding and a strong knowledge of various coding languages, such as Python, SAS, R and Scala. Added to this, they are usually able to use unstructured data in order to extract useful information. Other fields that are sometimes included in data science are bioinformatics, information technology, simulation and quality control, computational finance, epidemiology, industrial engineering and number theory.

As you can see, data science covers a very wide spectrum of knowledge and skills. Depending on which side of this spectrum you are, you may or may not use programming and complicated mathematics, but you will definitely use large sets of data, usually in an unstructured format. Due to the broad nature of this field, it is hard to define and to find one person capable of doing everything involved needed for a successful data science project. Usually, data scientists would work as a team where each would focus on a specific subset of the field.

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Here, you would see titles such as “Machine Learning Engineer”, “Analyst” or “A/B Test Expert” indicating which area of work they focus on. Tools used by data scientists include, but are not limited to, data cleansing, preparation, predictive analytics, machine learning and sentiment analysis. These experts are tasked with making sense of large collections of data, extracting useful information from it and translating that into actionable goals. A data science team would understand how data relates to business and uses this to enable executives to make informed decisions based on solid science in order to propel their businesses forward.

Data science involves processing vast amounts of unstructured data in automated ways in order to extract logical, useful information from it and make prediction regarding future trends and system behaviors. Unstructured data comes from video, audio, social media, manual surveys, clinical trials and many other sources. This can be lumped together as human consumable data, which can be read and analyzed in tabular form, by humans. The amount of data is so vast, though, that this is entirely impractical, hence the need for automating and speeding up the process. Here, the data scientists will have to borrow techniques from related fields, as is done in most practical applications of science.

As time progresses, these predictions must be updated and the system re-calibrated using new data. Data scientists must also understand and decide which analytics tools to use for their specific purposes and applications, since this would affect the type of information that they would be able to extract from a specific set of data. Real world problems are tackled in data science. This field is incredibly complex due to the complex nature of the world we live in.

In data science, unsupervised clustering can be used. Here, an algorithm is used to find clusters or cluster structures without having been given a training set of data. These clusters must be labelled by a data scientist; thus, some human interaction is necessary.

Here, the major complexity of the system is due to the nature of the data (unstructured and vast). It is necessary to synchronize and schedule tasks in a logical manner in order to render the data useful and extract as much information from it as possible.

Simply put, data science is a vast field encompassing many disciplines, of which machine learning is one.

Machine Learning

Machine learning is a subset of data science. Arthur Samuel defines it as “a field of study that gives computers the ability to learn without being explicitly programmed”.

An expert in machine learning requires in-depth knowledge of computer fundamentals and must be excellent in data modelling and evaluation skills. Knowledge of probability and statistics is needed and in-depth programming skills and knowledge is essential.

In machine learning, large collections of data are mined in order to find patterns, learn from it and predict future behaviors of systems. It basically “teaches” a system how to behave under certain circumstances. A prime example of this is Facebook’s algorithm. Here, the algorithm observes various users on the social media platform in order to determine patterns of user behavior and interactions. This information is used in order to tailor the user’s news feed to articles that they are likely to enjoy. Amazon uses a similar principle to suggest products in their “you might also like” category. YouTube, Netflix and a myriad of other media platforms and online retailers work on the same principle to suggest your next view, article or purchasing suggestions.

In finance, machine learning is used to predict whether a prospective client applying for a loan is a good or bad prospect based in historical data. This takes the guess work and “gut feel” factors out of financial decision making.

Another example of sophisticated machine learning is the autocomplete or predictive text functionality on your smartphone or search engine. The software is programmed to collect data as you type in order to better predict what you are likely to type next in order to fill in the blanks faster and more accurately as time progresses. This has become so entrenched in our daily lives that few people stop to think about it.

Machine learning is a subset of artificial intelligence (AI). Here, a problem is defined in finite terms and the algorithm is programmed to know the “right” decision. Now, it trawls through the data at hand to learn which parameters are needed in order to get to that decision.

Basically, the computer is given the ability to learn new things and complete complex tasks without being explicitly programmed. When developing a machine learning algorithm, a training set of data would be used to “teach” the algorithm to perform a specific function. This would be fine tuned and can later be re-calibrated using a new set of data. On the long run, this would lead to a highly sophisticated algorithm that can accurately predict future trends and system behavior and can also make complex decisions in an unsupervised manner. This eliminates the need for regular human interference. Here, regression and naive Bayes or supervised clustering could be used.

Machine learning would not include unsupervised clustering, as is the case with the broader data science discipline. Data used in machine learning must be structured in a way that the specific algorithm would understand. Here, feature scaling, word embedding and adding polynomial features are some of the tools that can be used to render data useful and understandable for each specific application. In machine learning, the main complexity is in the algorithm itself. In some cases, an ensemble algorithm would be used, which is a combination of various machine learning algorithms. Here, the contribution from each algorithm would be weighted in order to obtain the desired results.

In short, machine learning is where practical statistics and highly sophisticated programming skills meet.

Overlap Between Machine Learning and Data Science

In machine learning, concepts that are used in data science career, such as regression and supervised clustering, are also used. In contrast to this, data science uses data that may or may not be originated in an actual machine or mechanical process. Both these fields use large collections of data in order to learn from it and arrive at logical actions in order to add financial benefit to an organization.

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Data science is a much broader term that machine learning. Machine learning focuses mainly on statistics and algorithms, while data science encompasses anything related to collecting, analyzing and processing data. Data science is multi-disciplinary. In a data science team context, each person would have a specific role to fulfill. Here, a machine learning expert would work to automate as many tasks as possible, breaking down code in order to simplify and reuse as many components as possible. Statisticians would ensure that the information teased out of data makes sense and is usable. Economic experts would optimize the system responses to ensure economic viability. Machine learning is crucial to data science and should be used in conjunction with other disciplines in order to complete the data science picture.

If you have a high level of knowledge on mathematics and statistics combined with hacking skills, you are able to program in the field of machine learning. Pair these skills with a large portion of substantive expertise, and you have a highly skilled data scientist.

In short, machine learning is one of many tools used by data scientists in order to extract useful information from large collections of data.