“Our future success is directly
proportional to our ability to
understand, adopt, and integrate
new technology into our work.”

- Sukant Ratnakar

machine learning

Machine Learning

Machine learning is the science of getting computers to learn and act without being explicitly programmed. It is one of the most exciting technologies of Industry 4.0, which makes computers very similar to humans by virtue of their ‘ability to learn’ and is considered to be a significant stepping stone to human-level AI (artificial intelligence).

In the past decade, machine learning has given us virtual personal assistants (like Alexa, Siri, and Google Assistant), helped us predict traffic (like Google Maps), transformed how we commute (with Uber or Ola), provided us with social media services that bring us closer (“people you may know” or facial recognition on Facebook), helped us shop better (with platforms like Amazon and Flipkart), given us online customer support (via chatbots), detected online fraud (for e-wallets like like Paypal), built self-driving cars (like the Tesla Model S), and much more. Machine learning is all-pervasive today, and we use it almost throughout the day without even realising it.

Data Engineering & Informatics

During the first wave of digitisation, data was seen purely as a by-product of the functioning of digital applications, operating systems, and platforms. These days, data is a significant asset driving innovation across ancillary fields such as machine learning and artificial intelligence, which in turn improve services through process efficiency, and deliver better results for businesses and their customers.

Data is often referred to as the ‘new oil’ of the global economy and is termed “big data” when referring to large volumes of data. Data scientists and engineers help extract, refine, deploy, interpret, and manage big data. Harvard Business Review called data science “the sexiest job of the 21st century,” and businesses from start-ups to MNCs are scrambling to hire the best data scientists and engineers as big data keeps getting bigger.

machine learning
machine learning

Robotic Process Automation (RPA)

Automation has become an integral component of digital transformation strategies for enterprises around the world. RPA enables businesses to dramatically reduce operating costs by automating high-volume repetitive tasks with zero error rates. As RPA becomes mainstream, more companies will leverage the technology to increase productivity and efficiency.

Blockchain

Blockchain is a new way of storing data in a distributed ledger that allows multiple stakeholders to confidently and securely share access to the same information. It is providing a new infrastructure upon which the next innovative applications will be built, driving profound and positive changes across businesses, communities, and societies.

Blockchain’s transformational potential has been recognised by enterprises and governments across the world. Over 50 countries have already embarked on initiatives to integrate blockchains in their economies and to develop a strong, holistic blockchain ecosystem. This is opening up opportunities for blockchain to scale and create real business value. Globally, enterprises have established the potential of blockchain through proof-of-value engagements and by tracking bellwether implementations of peer firms. Techies savvy with foundational platform programming and blockchain application development are extremely scarce across the globe, and the demand will rise for the foreseeable future.

machine learning
machine learning

Cybersecurity

As technology advances, the need for cybersecurity will continue to increase. Corporates have realised that one cannot buy their way out of cyber challenges or find a silver bullet to remove the threats. Rather, the levels of cyber resilience they truly need can only be built over time, with the help of the right talent.

Cyberattacks are one of the top global risks of highest concern for the next decade. The potential cost of data fraud, theft, and cyberattacks could be up to $90 trillion in net economic impact by 2030 if cybersecurity efforts do not keep pace. Therefore, government and corporate leaders are deeply engaged in promoting effective cybersecurity strategies, and the global spending on security continues to accelerate significantly.

Artificial Intelligence

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with the human mind, such as learning and problem-solving.

AI is often confused with machine learning, but, in reality, they are both important skills with an overlapping, if not common, market. Among other sectors, artificial intelligence also has applications in the financial industry, where it is used to detect and flag activity such as unusual debit card usage and large account deposits—which helps the fraud department of the bank involved.

machine learning
machine learning

Cloud Native Computing

Cloud native technologies help organisations to build and run scalable applications in modern, dynamic environments such as public, private, and hybrid clouds. When combined with other technologies, they have been instrumental in the success of tech powerhouses such as Netflix, Uber, and others, changing business models in highly disruptive ways.