How to Make the Jump from
Batch to Real-Time Machine Learning
Real-time ML is increasingly being adopted to power new applications across use cases like fraud detection, recommender systems, pricing, loan and insurance underwriting, and marketing optimization.
But for most companies, the move to real-time ML is hard. It requires a shift from traditional batch analytics, to building and deploying models and data pipelines in production. This transition is hard, and requires new processes and tooling.
If you’re a data scientist or ML engineer, the conversation about making that jump to real-time ML might come up sooner than you expect.
Listen to Claypot AI CEO Chip Huyen and Tecton CTO Kevin Stumpf as they discuss how companies can make the jump from batch to real-time ML as easy as possible. They cover how to:
- Assess the incremental value of real-time ML for your use cases
- Weigh it against the cost and challenges of deploying real-time ML
- Evaluate if your tech stack is “real-time ready” and how to get there if not
- Make the transition as easy as possible
They also answer some great questions from the audience. You don't want to miss this lively conversation!