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
Chief Business Development Officer at Redis Labs