MLOps for Practitioners Training

MLOps for Practitioners

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.

What will you learn?

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

Requirements:

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

Course Outline*:

*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.

Level:

Advanced

Duration:

Hours (days:

)

Training customized to your needs

Immersive hands-on experience in a dedicated setting

*Price can range depending on number of participants, change of outline, location etc.

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Master new skills guided by experienced instructors from anywhere.

Level:

Advanced

Duration:

Hours (days:

)

Training customized to your needs

Reduced training costs

*Price can range depending on number of participants, change of outline, location etc.

Get Quote

You can participate in a Public Course with people from other organisations.

/per trainee

Thanks for the numbers, they could be going to your emails. But they're going to mine... Thanks ;D

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Level:

Advanced

Duration:

Hours (days:

)

Fits ideally for individuals and small groups

Networking opportunities with fellow participants.

*Price can range depending on number of participants, change of outline, location etc.

Get Quote

MLOps for Practitioners

What will you learn?

Requirements:

Course Outline*:

Module 1: Introduction to MLOps goals, benefits, and challenges

Module 2: Core concepts, methods, and tools

Module 3: MLOps lifecycle blueprint

Module 4: Reliability, security, and governance by design

Module 5: Production deployment patterns

Module 6: Monitoring, drift, and incident response

Module 7: Feature and experiment management

Module 8: Best practice case studies and operating model

Get Quote for:

MLOps for Practitioners

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