MLOps and Model Deployment

As machine learning (ML) continues to evolve from experimental research into large-scale production, the need for robust deployment, monitoring, and lifecycle management has never been greater. This course introduces participants to MLOps—a set of practices that bridge the gap between data science and operations to ensure models are not only accurate but also scalable, reliable, and maintainable in real-world environments.

Participants will explore the end-to-end ML workflow with a focus on deployment strategies, CI/CD pipelines, infrastructure automation, and model governance. The course emphasizes collaboration between data scientists, engineers, and DevOps teams to build production-ready ML systems that are efficient, traceable, and adaptable.

Section 1: Introduction to MLOps and Production ML

• What is MLOps? Definitions, components, and business value.
• Comparison of traditional DevOps vs MLOps.
• The ML lifecycle: from data ingestion to retraining.
• Challenges in deploying ML models at scale.
• Benefits of automation and monitoring in ML workflows.
• Key tools and platforms in the MLOps ecosystem (MLflow, Kubeflow, DVC, etc.).
• Case study: MLOps in a real-world product environment.

Section 2: Building and Automating ML Pipelines

• Designing reproducible ML workflows using pipeline tools.
• Data validation, feature engineering, and model training pipelines.
• Experiment tracking and parameter logging.
• Version control for datasets, code, and models.
• Using MLflow or DVC for tracking and reproducibility.
• Containerization using Docker for ML applications.
• Best practices for modular pipeline design.

Section 3: Model Deployment Strategies and Infrastructure

• Deployment types: batch, real-time (online), edge, and hybrid.
• Model serving tools: TensorFlow Serving, TorchServe, FastAPI, BentoML.
• API integration and microservice architecture.
• Model packaging and container orchestration with Kubernetes.
• Choosing the right cloud or on-prem environment.
• Hands-on example: deploying a model via REST API.
• Rollout strategies: blue-green, canary, and shadow deployments.

Section 4: Monitoring, Testing, and Model Governance

• Importance of monitoring models in production.
• Key metrics: latency, prediction accuracy, drift detection.
• Automating testing: unit tests, integration tests for ML models.
• Alerting and rollback strategies for failed deployments.
• Model governance: auditing, compliance, and reproducibility.
• Toolkits for drift detection and quality assurance.
• Logging and observability frameworks for ML services.

Section 5: CI/CD for Machine Learning

• Continuous integration and delivery pipelines for ML.
• Integrating Git, Jenkins, GitHub Actions, or GitLab CI.
• Automating retraining and redeployment on new data.
• Scheduling and orchestration using Airflow or Prefect.
• Building end-to-end automated workflows.
• Cross-functional collaboration and workflow ownership.
• Scaling MLOps for multiple models and business use cases.

Upon successful completion of this training course, delegates will be awarded a Holistique Training Certificate of Completion. For those who attend and complete the online training course, a Holistique Training e-Certificate will be provided.

Holistique Training Certificates are accredited by the British Accreditation Council (BAC) and The CPD Certification Service (CPD), and are certified under ISO 9001, ISO 21001, and ISO 29993 standards.

CPD credits for this course are granted by our Certificates and will be reflected on the Holistique Training Certificate of Completion. In accordance with the standards of The CPD Certification Service, one CPD credit is awarded per hour of course attendance. A maximum of 50 CPD credits can be claimed for any single course we currently offer.