Course Outline
Foundations of Containerization for MLOps for Government
- Understanding the Requirements of the Machine Learning Lifecycle for Government
- Key Docker Concepts for ML Systems in Government Operations
- Best Practices for Reproducible Environments for Government Applications
Building Containerized ML Training Pipelines for Government
- Packaging Model Training Code and Dependencies for Government Use
- Configuring Training Jobs Using Docker Images in Government Systems
- Managing Datasets and Artifacts in Containers for Government Projects
Containerizing Validation and Model Evaluation for Government
- Reproducing Evaluation Environments for Government Models
- Automating Validation Workflows for Government Applications
- Capturing Metrics and Logs from Containers for Government Monitoring
Containerized Inference and Serving for Government
- Designing Inference Microservices for Government Use
- Optimizing Runtime Containers for Production in Government Systems
- Implementing Scalable Serving Architectures for Government Operations
Pipeline Orchestration with Docker Compose for Government
- Coordinating Multi-Container ML Workflows for Government Projects
- Environment Isolation and Configuration Management for Government Systems
- Integrating Supporting Services (e.g., Tracking, Storage) for Government Applications
ML Model Versioning and Lifecycle Management for Government
- Tracking Models, Images, and Pipeline Components for Government Use
- Version-Controlled Container Environments for Government Operations
- Integrating MLflow or Similar Tools for Government Applications
Deploying and Scaling ML Workloads for Government
- Running Pipelines in Distributed Environments for Government Projects
- Scaling Microservices Using Docker-Native Approaches for Government Systems
- Monitoring Containerized ML Systems for Government Operations
CI/CD for MLOps with Docker for Government
- Automating Builds and Deployment of ML Components for Government Use
- Testing Pipelines in Containerized Staging Environments for Government Projects
- Ensuring Reproducibility and Rollbacks for Government Applications
Summary and Next Steps for Government
Requirements
- An understanding of machine learning workflows for government applications.
- Experience with Python for data or model development in governmental projects.
- Familiarity with the fundamentals of containers for efficient deployment and management.
Audience
- MLOps engineers supporting government initiatives.
- DevOps practitioners working on public sector projects.
- Data platform teams focused on governmental data management.
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.
