India’s AI tech leaves something to be desired. Ironically, a sizable chunk of engineers working for tech companies like Google, Microsoft, Apple, Facebook and Amazon are Indians. According to Seattle Times, close to 70% of Indian H1B visa holders in the US work in the tech industry, from just under 40% in 2003. 

The International Monetary Fund (IMF) has ranked India as the seventh-largest economy, down from the sixth position in 2020 and fifth in 2019. The relegation is chalked up to the pandemic crisis. Now, with the rising number of COVID-19 cases and deaths, India’s future looks bleak. 

It is high time Indian companies and industry leaders started focusing on enhancing tech capabilities and developing core artificial intelligence (AI) products in India instead of relying on foreign firms.

Today, most tech companies have the money to acquire startups making strides in AI. Unfortunately, India does not have a tech giant, nor a billionaire agent provocateur like Elon Musk.

AI-focused API/coding platforms 

As companies began developing digital applications for customers during the pandemic, the interest in application programming interface (API) and low-code, no-code technology platforms grew significantly. Today, such solutions are mainly offered by technology companies such as Microsoft, Amazon, Appian, Pega, OpenAI and Service Now.

In India, however, very few companies, including Infosys, HCL Technologies and Tech Mahindra, are working on low-code, no-code technology platforms. Indian companies can add value by developing state-of-the-art APIs and coding platforms that are easy to use and scalable.

According to Gartner, no-code or low code platforms will be responsible for over 65% of application development by 2024. 

ML notebooks & infrastructure tools 

For machine learning engineers and data scientists, notebooks have become an integral tool. Notebooks are highly interactive multi-purpose tools to write and execute code and analyse intermediate results to gain insights while working on a project. Notable tools include Jupyter Notebook, Kaggle Notebook, Colab, Gradient, Deepnote, Saturn Cloud, and Polynote. Unfortunately, there aren’t any machine learning notebooks from India. 

Data labelling & datasets marketplace 

With supervised learning being the most common form of machine learning technique used by companies today, there is a need for labelled or annotated data across images, audio, video and text formats across sectors, and companies developing data labelling tools and feature stores (open-source as well proprietary) becomes crucial for enterprises’ machine learning strategy. 

Also, the data labelling service providers or datasets marketplace that caters to enterprises and government agencies for machine learning processes need to be unbiased and offer high-quality data. 

According to Grandview Research, the global data labelling market is expected to touch $8.2 billion by 2028, from $1.3 billion in 2020. The market is expected to grow at a CAGR of 25.6% from 2021 to 2028. 

ML frameworks & tools  

Tech giants like Google, Facebook and Microsoft offer the machine learning frameworks and tools AI researchers and developers use globally. Most open-source frameworks are available on GitHub. Notable open-source machine learning frameworks include Google’s TensorFlow and Facebook’s PyTorch.

However, significantly few Indian companies are developing ML frameworks and tools, such as Gramener, Auquan etc.

According to the latest market estimates, the global AI business operations revenue is expected to touch $10.8 billion by 2023, and the NLP market size is expected to reach $43.3 billion by 2025. 

AutoML platforms 

AutoML has become the go-to method for building computer vision systems. Today, most of the AutoML platforms come from cloud service providers such as Google’s Cloud AutoML, Microsoft’s Custom Vision, and Amazon SageMaker Autopilot. Only a handful of companies work in India’s AutoML domain, including H2O.ai and TransOrg Analytics. 

According to Research and Markets, the global AutoML market is expected to reach $15 billion by 2030, growing at a CAGR of 44% during the forecast period (2020-2030). Nearly 65% of the AutoML market is most likely to be in North America and Europe. 

ML hackathon platforms 

With COVID-19 in the picture, offline hackathons have taken a hit. Today, online machine learning hackathon platforms are all the rage. Machine learning hackathon platforms are quite different from traditional coding and product-focused hackathons. 

Moreover, it has become a go-to platform for tech companies to scout top talent and help employees and individuals test their skills for deploying machine learning models with higher accuracy and interoperability. While Kaggle continues to be developers’ favourite, some of the notable Indian hackathon platforms include MachineHack and DataHack. 

According to BlueWeave Consulting, the global hackathon management software market is expected to touch $292.2 million by 2026, growing at 8% CAGR during the forecast period (2021-2026). 

AI-focused online learning platforms 

While YouTube and social media platforms have become the primary source for learning AI/ML concepts for beginners and machine learning enthusiasts, the industry lacks personalised content that caters to learners across age groups.

Right now, India has platforms like Great Learning, UpGrad and Simplilearn for specialised content. However, we need more platforms to curate high-quality content to learn AI/ML basics, experiment with AI/ML models, solve complex problems and apply them to develop next-gen AI applications. 

According to MarketsAndMarkets, the AI in the education market is expected to touch $3.68 billion by 2023, growing at 47 percent CAGR during the forecast period (2018-2023). 

AI chips/CPUs/GPUs 

Hardware plays an equally important role in developing next-level artificial intelligence and machine learning applications. For instance, AI chips (also known as AI hardware or AI accelerator) are specially designed accelerators for artificial neural network (ANN) based applications. The most commercial ANN applications are deep learning applications. 

Companies like Google, Amazon and Microsoft are doubling down on developing AI chips and applications.

Moreover, the emergence of quantum computing and the increase in the implementation of AI chips in robotics are fueling the growth of the artificial intelligence chip market. 

AI-focused semiconductor startups in India include Manjeera Digital Systems and Provino Technologies (acquired by Google). According to Allied Market Research, the AI chip market is expected to touch $91 billion by 2025, growing at 45.2% CAGR from 2019-2025. 

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