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Course Outline
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
Requirements
- 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 Hours
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €6840 online delivery, based on a group of 2 delegates, €2160 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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Public Training
Please see our public courses
Testimonials (5)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life TM
Course - Kubeflow
It gave a good grounding for Docker and Kubernetes.
Stephen Dowdeswell - Global Knowledge Networks UK
Course - Docker (introducing Kubernetes)
I generally liked the trainer knowledge and enthusiasm.
Ruben Ortega
Course - Docker and Kubernetes
I generally enjoyed the content was interesting.
