From Data to Action: Real-Time ML’s Relevance in Modern India’s Digital Sphere

By Aditya Nambiar

With its vibrant digital ecosystem, burgeoning startup scene, and growing internet penetration, India stands at the cusp of a digital revolution. India is arguably the most promising internet market in the world.

As more users gravitate towards digital platforms, the sheer volume of data being generated is staggering. This boom not only presents an unparalleled opportunity to harness data for enhanced user experiences but also poses a challenge: the development of robust technologies capable of scaling with this data surge.

Furthermore, as a multitude of companies continually set their sights on India’s promising digital landscape, the need to effectively leverage this data in real-time intensifies. For these businesses, efficiently harnessing the power of data isn’t just about gaining a competitive edge anymore—it’s about survival. The rapid integration and interpretation of data have shifted from being a “nice-to-have” technology to an absolute necessity, underscoring the critical role of real-time ML in the modern Indian digital arena.

Hence, we are seeing a very powerful trend playing out right now — more and more top tech companies are making a larger part of their machine learning as real-time as possible.

But what is Real-time ML? Real-time machine learning uses real-time data to generate more accurate predictions and adapt models to changing environments.

This could start with having a rule-based system that uses some heuristics over real-time features or could involve deploying ML models in production environments powered by a real-time feature engineering platform. At its most advanced stage, it takes the form of an online learning system where the model is continuously trained on incoming data.

Such an approach brings immense value in scenarios where patterns undergo rapid changes and swift decision-making is required.

Lets look at how incorporating real-time ML in your organization can be a game changer –

1.Cold Start -In a country like India, where there is so much churn in the user base, we see new users joining and leaving any platform every day. The initial experiences of users or new inventory items are pivotal for long-term engagement and retention.  Offline ML systems, which use stale data, fall short in offering personalization for new users or promoting fresh content like news stories, resulting in potential lost opportunities. On the contrary, real-time ML systems can quickly adapt and provide immediate personalization, enhancing user satisfaction and potential revenue.

2. ​​Long Tail Predictions – Even for users who aren’t cold per se, a vast majority of them will not be shopping on your e-commerce website every day. Most consumer internet products/services have a power law distribution where on one side, some people are a lot more active than others, and yet the bulk of the activity lies in the long tail. Hence having a real timl system that can quickly respond to activity becomes critical.
3. Cost – Offline systems require pre-computing features for every user. But for most products, a heavy majority of their registered users don’t show up every day — in fact, a majority of them don’t show up for days/weeks in a row. So all this computation/storage that was done to precompute results for such users was wasteful — and even though each unit of offline computation is “cheaper” than each unit of online computation, the much higher volume of offline computation more than makes up for it.
4. In-Session Personalization & Catering to Logged-Out Users: With ever-changing micro-intents, users require adaptable platforms. For example, a user’s music mood can shift rapidly from calm jazz to energetic pop. Platforms like TikTok showcase the power of real-time personalization, adjusting instantly to user preferences and enhancing the user experience. This immediacy also benefits logged-out users. While they don’t have historical data, real-time systems can still tailor their current session, increasing the chances of them registering. As the digital landscape evolves, more products will likely adopt such nimble personalization strategies to bolster user engagement and growth.
5. Experimentation Velocity – Success in ML strongly depends on the speed of iteration. The more features/models the engineers can try, the better the predictions. Having a real time platform can bolster the experimentation velocity of the team by making it easier to start new experiments, bring down the cost of running an experiment, and make it much easier to debug the system.

The insights presented underscore the undeniable benefits of real-time strategies for organizations, irrespective of their size. Adopting real-time solutions isn’t merely advantageous for certain industries—it’s imperative.

There are some industries where this is an absolute must –

1. Financial Services & Banking: In the fast-paced realm of the financial sector, even the slightest lag can have substantial repercussions. Real-time ML becomes paramount when considering fraud detection, where spontaneous responses can prevent unauthorized transactions and potential security breaches. Similarly, real-time analysis ensures that lending decisions are made with the most current data for risk assessment, mitigating potential bad debts. Furthermore, in the domain of credit evaluations, real-time systems empower institutions to make immediate lending decisions based on the latest credit behaviors, ensuring optimized lending strategies and bolstered consumer trust.
2. E-commerce & Retail: As customer expectations lean towards instant gratification, real-time inventory updates, dynamic pricing strategies, and swift customer service responses have become indispensable in the retail space.
3. Transportation & Logistics: With real-time GPS tracking, instant traffic alerts, and route optimization, the transportation industry relies heavily on instantaneous solutions for efficient deliveries and safety.
4. Entertainment & Media: As consumers’ demand for content becomes increasingly insatiable, the entertainment and media sector must stay one step ahead. Real-time ML plays a pivotal role in enhancing viewer experiences on streaming platforms by providing personalized recommendations on-the-fly, adapting to users’ changing preferences as they interact with content
5. IoT & Manufacturing: The rise of the Internet of Things (IoT) in manufacturing, necessitates real-time processing. Whether it’s monitoring equipment health, ensuring quality control on production lines, or optimizing resource allocation based on immediate demand, real-time solutions are pivotal in the modern manufacturing landscape.

Conclusion

In conclusion, as India continues its meteoric rise in the digital domain, the role of real-time ML becomes increasingly integral. The multifaceted benefits of real-time solutions, ranging from enhanced personalization, quick decision-making, to cost-efficiency, are reshaping industries and setting new benchmarks for user experiences.

As India’s digital tapestry becomes ever more intricate, organizations that adopt, adapt, and advance with real-time ML will undoubtedly be the frontrunners in this dynamic digital race.

(The author is Aditya Nambiar, a founding member of Fennel.ai, and the views expressed in this article are his own)