MLOps and Machine Learning Model Deployment Training Courses

This module establishes the operational framework of MLOps and examines how machine learning development connects with software engineering, data engineering and IT operations. Participants explore the machine learning lifecycle from data preparation and experimentation through deployment, monitoring and maintenance.

The module focuses on corporate requirements such as reproducibility, deployment consistency, collaboration, governance and operational control. It also examines the differences between developing a machine learning model in an isolated environment and operating that model as a production service.

Key areas include:

• MLOps principles and operating models
• Machine learning lifecycle management
• Development to production workflows
• Cross-functional machine learning teams
• Operational risks in machine learning
• Reproducibility and automation
• Production readiness considerations

This module examines how organisations can manage machine learning experimentation in a controlled and reproducible manner. Experiment tracking allows teams to maintain records of models, parameters, datasets, metrics and results throughout development.

Participants explore how experiment tracking supports collaboration and helps teams compare different approaches without losing historical information.

Key areas include:

• Experiment management
• Parameter and metric tracking
• Dataset tracking
• Model artefact management
• Reproducible experimentation
• Experiment comparison
• Development workflow standardisation
• Connecting experimentation with deployment processes

Model versioning is essential when multiple machine learning models are being developed, tested and deployed across corporate environments. This module focuses on managing model versions and establishing controlled transitions between development, testing and production.

Participants examine how version control can help organisations identify which model is active, which version was previously deployed and which model should be promoted or rolled back when operational requirements change.

Key areas include:

• Model versioning strategies
• Model registries
• Version identification
• Model promotion workflows
• Production model management
• Rollback procedures
• Model lineage
• Lifecycle governance

This module addresses the management of machine learning features across development and production environments. Feature stores provide a structured approach to storing, managing and serving features consistently for different machine learning applications.

Participants examine how feature management affects model reliability and deployment consistency. The module considers reusable features, feature availability, data consistency and the relationship between feature engineering and production inference.

Key areas include:

• Feature engineering workflows
• Feature stores
• Feature lifecycle management
• Feature reuse
• Training and serving consistency
• Feature quality management
• Online and offline feature requirements
• Feature governance

This module focuses on preparing machine learning models for production deployment. Participants examine how models can be packaged with their dependencies and operational requirements so they can be deployed consistently across target environments.

The module considers different deployment approaches and the operational requirements associated with moving models from development into production.

Key areas include:

• Model packaging
• Dependency management
• Deployment environments
• Containerised model services
• Deployment automation
• Testing before production
• Production configuration
• Release management

Inference endpoints provide controlled access to deployed machine learning models and enable applications to submit data for predictions. This module examines the operational structure of model serving and the requirements for maintaining reliable inference services.

Participants explore endpoint design, request handling, scalability, availability and performance considerations. The module also considers how inference services integrate with existing corporate applications and technology platforms.

Key areas include:

• Inference endpoints
• Model serving architectures
• Prediction request management
• API-based model access
• Endpoint performance
• Scalability requirements
• Availability and reliability
• Production inference operations

This module examines how automation can connect machine learning development, testing and deployment. Participants explore pipeline structures that reduce manual intervention and create repeatable deployment processes.

The focus is on establishing controlled workflows where code, data, features and models can progress through defined operational stages.

Key areas include:

• MLOps pipeline architecture
• Continuous integration practices
• Continuous deployment workflows
• Automated testing
• Pipeline orchestration
• Deployment approvals
• Release automation
• Production workflow management

Production models can experience changes in data, user behaviour and business conditions. This module focuses on monitoring model performance and identifying changes that may reduce reliability.

Drift monitoring is examined as an important component of production machine learning operations. Participants explore how data drift, feature drift and changes in model behaviour can be identified and incorporated into operational monitoring processes.

Key areas include:

• Production model monitoring
• Drift monitoring
• Data drift
• Feature drift
• Model performance monitoring
• Monitoring metrics
• Alerting mechanisms
• Operational response procedures

This module focuses on the operational processes required when a production model needs to be updated. Retraining pipelines can automate the movement from newly available data to model evaluation and controlled redeployment.

Participants examine how organisations can define retraining triggers, validate new models and introduce updated versions without disrupting production services.

Key areas include:

• Retraining pipelines
• Retraining triggers
• Automated data preparation
• Model evaluation
• Model comparison
• Automated validation
• Model promotion
• Continuous improvement workflows

The final module brings together the operational elements of MLOps and focuses on maintaining reliable machine learning systems over time. Participants examine how organisations can establish governance structures, operational controls and performance requirements for deployed models.

The module considers how machine learning operations can be aligned with corporate technology standards while maintaining visibility across models, data, features, deployments and monitoring systems.

Key areas include:

• Production reliability
• Operational governance
• Model lifecycle controls
• Deployment documentation
• Performance management
• Incident response
• Auditability and traceability
• Machine learning operations management
• Long-term model maintenance

These courses focus on managing the machine learning lifecycle in corporate environments, including model versioning, deployment, monitoring, experiment tracking, inference endpoints and retraining pipelines.

The training is suitable for machine learning engineers, data scientists, data engineers, DevOps professionals, software engineers, cloud engineers, technology managers and professionals responsible for production machine learning systems.

The course covers model packaging, deployment workflows, model versioning, inference endpoints, automated pipelines, production monitoring and operational controls for managing deployed machine learning models.

Drift monitoring helps organisations identify changes in production data or model behaviour that can affect prediction quality. It supports timely investigation, model evaluation and retraining decisions.

Retraining pipelines provide structured processes for updating models when new data or changing production conditions require a model refresh. They can connect data preparation, model training, validation and controlled redeployment.