Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Foundations of Containerization in MLOps
- Analyzing requirements across the ML lifecycle.
- Essential Docker concepts for ML systems.
- Best practices for establishing reproducible environments.
Constructing Containerized ML Training Pipelines
- Encapsulating model training code and associated dependencies.
- Setting up training jobs via Docker images.
- Handling datasets and artifacts within containers.
Containerizing Validation and Model Evaluation
- Replicating evaluation environments consistently.
- Automating validation processes.
- Extracting metrics and logs from containers.
Containerized Inference and Serving
- Architecting inference microservices.
- Tuning runtime containers for production efficiency.
- Building scalable serving architectures.
Pipeline Orchestration via Docker Compose
- Synchronizing multi-container ML workflows.
- Managing environment isolation and configuration.
- Integrating auxiliary services such as tracking and storage.
ML Model Versioning and Lifecycle Management
- Monitoring models, images, and pipeline elements.
- Maintaining version-controlled container environments.
- Incorporating tools like MLflow for lifecycle tracking.
Deploying and Scaling ML Workloads
- Executing pipelines in distributed settings.
- Expanding microservice capacity using Docker-native methods.
- Observing containerized ML systems.
CI/CD for MLOps Leveraging Docker
- Automating the build and deployment of ML components.
- Validating pipelines in containerized staging environments.
- Guaranteeing reproducibility and facilitating rollbacks.
Conclusion and Subsequent Steps
Requirements
- A solid grasp of machine learning workflows.
- Proficiency in Python for data processing or model development.
- Basic familiarity with containerization principles.
Target Audience
- MLOps Engineers.
- DevOps Practitioners.
- Data Platform Teams.
21 Hours
Testimonials (3)
How trainer deliver knowledge so effectively
Vu Thoai Le - Reply Polska sp. z o. o.
Course - Certified Kubernetes Administrator (CKA) - exam preparation
the trainer had a lot of knowledge and patience to share with us
Bogdan Olaru
Course - Introduction to Docker
The knowledge and exchanges with Augustin
