First principles in building a real-time AI platform

As the business demand for real-time AI/ML driven applications and use cases are gaining momentum including fraud detection, real-time product recommendations, predictive maintenance, dynamic pricing, chatbots, and more. But operationalizing AI/ML is challenging; from preparing the data for feature engineering to training models, and then deploying and monitoring them.

These challenges have created a new data-layer and data-transformation challenge for organizations and AI/ML professionals, including handling the proliferation and complexity of real-time feature engineering, continuous training, model serving, and monitoring.

We’ll take a first-principles approach in defining a Real-Time AI Platform drawing from various examples across the industry, and looking at emerging architectures of some early pioneers.

What you’ll learn

• The importance of real-time and first principles in AI

• Few examples of operationalizing ML

• Understanding ML Lifecycle