MLOps & Model Deployment

Ship, monitor and maintain machine learning and LLM systems in production.

Machine Learning in Production (DeepLearning.AI)

Explore production ML with Andrew Ng, covering deployment choices, error analysis, data quality, and monitoring across three focused modules.

Python TensorFlow

Ultimate DevOps to MLOps Bootcamp - Build ML CI/CD Pipelines

MLOps & Model Deployment

Build a house-price prediction pipeline with MLflow, FastAPI, Docker, and Kubernetes while learning CI/CD workflows for machine learning.

Python

Most models never reach production, and many that do quietly degrade. MLOps courses close that gap. Topics include experiment tracking and model registries with MLflow or Weights & Biases, reproducible training pipelines, containerisation with Docker and orchestration with Kubernetes, CI/CD adapted for machine learning, feature stores, and serving patterns for batch and real-time inference. Monitoring gets serious treatment, covering data and concept drift, performance decay, and retraining triggers. LLMOps material extends this to generative systems with prompt versioning, evaluation harnesses and cost observability.