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An advanced, practical course for teams with solid DevOps, ML, and Data Science experience. Participants align on modern MLOps goals, methods, tools, and the end to end lifecycle, then translate them into realistic operating practices with clear handoffs between data, model, and platform teams.
You will connect business goals to an MLOps operating model, standardize the lifecycle from data to deployment, and establish versioning, automation, and monitoring that keep models reliable in production. You will review best practice architectures and apply them to your environment.
After this training you will be confident in:
• Explaining MLOps goals, benefits, and common challenges
• Choosing concepts, methods, and tools that fit your stack and constraints
• Running an end to end lifecycle across data, training, packaging, deployment, and monitoring
• Applying best practices with model registry, CI or CD, feature management, and observability
• Strong DevOps background and practical ML or DS project experience
• Familiarity with containers, Git, and a CI platform
• Access to a non sensitive example service or pipeline is helpful
*We know each team has their own needs and specifications. That is why we can modify the training outline per need.
Module 1: Introduction to MLOps goals, benefits, and challenges
• Why MLOps now and how it differs from traditional DevOps
• Success criteria across accuracy, latency, cost, and risk
• Typical pain points data drift, handoffs, shadow IT, brittle notebooks
• Roles and responsibilities across data, model, platform, and product
Module 2: Core concepts, methods, and tools
• Reproducibility and lineage datasets, code, parameters, environments
• Versioning and registries data, models, and artifacts
• CI or CD for ML tests, checks, and promotion policies
• Tooling landscape feature stores, experiment tracking, orchestration, model serving
Module 3: MLOps lifecycle blueprint
• From problem framing to deployment and feedback
• Data contracts, validation, and schema evolution
• Training pipeline design configuration as code and dependency isolation
• Packaging models for serving batch, real time, and streaming patterns
Module 4: Reliability, security, and governance by design
• Observability for ML systems telemetry for data, models, and infra
• Model risk management approvals, audit trails, and rollback plans
• Security and privacy secrets, PII handling, and isolation boundaries
• Cost awareness right sizing, autoscaling, and workload placement
Module 5: Production deployment patterns
• Online inference canary, blue green, and traffic shadowing
• Batch or streaming inference schedulers and backfills
• Multi model routing, A or B tests, and champion or challenger setups
• Edge and hybrid considerations
Module 6: Monitoring, drift, and incident response
• Data quality checks, drift detection, and performance regression signals
• Feedback loops labels, delayed truth, and human in the loop
• Runbooks and playbooks for incidents and degradations
• Post incident learning and continuous improvement loops
Module 7: Feature and experiment management
• Feature stores online or offline parity and freshness SLAs
• Offline to online consistency and embedding reuse
• Experiment tracking metrics, artifacts, and comparison at scale
• Promotion criteria from experiment to production
Module 8: Best practice case studies and operating model
• Review of reference architectures in different stacks
• Choosing build vs buy and integration points in your ecosystem
• Team workflows templates, checklists, and governance touchpoints
• Roadmap to adopt MLOps practices across one pilot and then multiple teams
Hands-on learning with expert instructors at your location for organizations.
Master new skills guided by experienced instructors from anywhere.
