Deeptech: Emerging Frontiers — Mini Blog #5
AI Infrastructure
While there has been a lot of buzz around generative AI, a critical segment that is often overlooked is AI infrastructure or AIOps. AI infra refers to the set of tools that power the entire AI model lifecycle from training, building to deployment & monitoring in production.
There has been an exponential growth in organizations implementing AI solutions. However, the transition from research to production remains a critical challenge for enterprises. 90% models fail to make it to production!
Compared to DevOps tools, MLOps tools need to deal with a lot more complexity as ML models continue evolving over time due to constant updates with new data or algorithm improvements. Hence, it becomes crucial for organizations to adopt robust MLOps tools that enable seamless integration of these changes.
AI infra is a rapidly evolving space that is currently fragmented because of diversity in data types and use cases that these tools have to support. While there are a lot of tools that are offering workflow solutions, we believe tools that have a core IP combined with an engineering workflow solution will emerge as winners in the long run.
Over the last few years, we have seen a multitude of tools emerge to help businesses realize the benefit of AI. MLOps started with a focus on data collection, model development & deployment processes & has gradually delved into more complex things.
Here are some of the deeptech investible themes that we are excited about:
- AI Observability Platform — It is difficult to train models on all possible real world scenarios in the lab. This is one of the major challenges that leads to a gap in the performance of models in the lab vs in production. Apart from data drift, concept drift can also occur over time, resulting in a drop in accuracy. With a good observability platform, teams can automatically keep their training data as close to the real world data as possible.
2. Real Time ML Platform — Real time ML allows organizations to process and analyze data as it is generated, drastically reducing latency compared to traditional batch processing. This is particularly crucial for mission critical use cases like fraud detection & cybersecurity. For example, real time ML enhances fraud detection by identifying suspicious behaviors instantaneously as well as continuously learning & adapting based on fresh data.
3. Synthetic Data Generation Platform — The growing demand for training data in ML has led to the emergence of synthetic data as a viable solution. Getting quality real world data is a challenge due to its paucity, cost, privacy & ethical concerns. Synthetic data can help comply with regulations like GDPR, improve model accuracy & reduce bias, while enabling faster access to datasets at a fraction of the cost.
