Unlock the Power of Large-Scale AI
In today's data-driven world, building effective machine learning models is just the first step. This course dives deep into the principles, architectures, and best practices required to deploy, monitor, and maintain ML systems that can handle massive datasets and high-throughput demands. Learn to build robust, efficient, and production-ready AI solutions.
Core Concepts
- Distributed Training Strategies
- Data Pipelines and Preprocessing at Scale
- Model Serving Architectures (REST, gRPC)
- Containerization with Docker & Kubernetes
- Infrastructure as Code (Terraform, CloudFormation)
Monitoring & Optimization
- Performance Monitoring (Latency, Throughput)
- Drift Detection and Retraining
- Cost Management and Optimization
- A/B Testing and Experimentation
- Security Best Practices
Tools & Technologies
- Cloud Platforms (AWS, GCP, Azure)
- MLOps Frameworks (Kubeflow, MLflow)
- Big Data Technologies (Spark, Dask)
- Databases for ML (Vector DBs, Feature Stores)
- CI/CD for Machine Learning