Building a Real-Time ML System Together

Source: https://realtimeml.carrd.co/

Learn to design, develop, deploy, and scale end-to-end real-time ML systems using Python, Rust, LLMs, and Kubernetes.

What awaits you:

1. More than 150 hours of recorded sessions from the previous 4 cohorts.
2. Complete source codes of projects: a cryptocurrency price prediction system and a credit card fraud detection system.
3. hours of live coding and practice in each cohort.

In this interactive practical course, we will create a real-time machine learning system from scratch, deploy it, and scale it. In previous cohorts, we built a cryptocurrency price predictor, while in the next one, we will create a transaction fraud detection system.

The course is suitable for ML engineers, data scientists, and developers who are already familiar with the basics of ML (at least one trained model) and are ready to move from theory to practice.

You will learn to:

• Develop microservice architectures with real-time ML.
• Apply a universal approach: Feature → Training → Inference Pipeline.
• Use modern tools: Kafka, Feature Store, Experiment Tracker, Model Registry, and Kubernetes.

This is not theory or “passive learning”. This is a chance to build a working system and boost your career.

Password: cms.ddpanda.org