感谢您发送咨询！我们的团队成员将很快与您联系。
感谢您发送预订！我们的团队成员将很快与您联系。
课程大纲
Foundations of Containerization for MLOps
- Understanding ML lifecycle requirements
- Key Docker concepts for ML systems
- Best practices for reproducible environments
Building Containerized ML Training Pipelines
- Packaging model training code and dependencies
- Configuring training jobs using Docker images
- Managing datasets and artifacts in containers
Containerizing Validation and Model Evaluation
- Reproducing evaluation environments
- Automating validation workflows
- Capturing metrics and logs from containers
Containerized Inference and Serving
- Designing inference microservices
- Optimizing runtime containers for production
- Implementing scalable serving architectures
Pipeline Orchestration with Docker Compose
- Coordinating multi-container ML workflows
- Environment isolation and configuration management
- Integrating supporting services (e.g., tracking, storage)
ML Model Versioning and Lifecycle Management
- Tracking models, images, and pipeline components
- Version-controlled container environments
- Integrating MLflow or similar tools
Deploying and Scaling ML Workloads
- Running pipelines in distributed environments
- Scaling microservices using Docker-native approaches
- Monitoring containerized ML systems
CI/CD for MLOps with Docker
- Automating builds and deployment of ML components
- Testing pipelines in containerized staging environments
- Ensuring reproducibility and rollbacks
Summary and Next Steps
要求
- An understanding of machine learning workflows
- Experience with Python for data or model development
- Familiarity with the fundamentals of containers
Audience
- MLOps engineers
- DevOps practitioners
- Data platform teams
21 小时
客户评论 (3)
培训师如何如此有效地传授知识
Vu Thoai Le - Reply Polska sp. z o. o.
课程 - Certified Kubernetes Administrator (CKA) - exam preparation
机器翻译
培训师知识渊博，耐心地向我们分享了很多内容
Bogdan Olaru
课程 - Introduction to Docker
机器翻译
与Augustin的知识交流
Laurent - L'Office national des vacances annuelles (ONVA)
课程 - Docker and Kubernetes
机器翻译
