MLOps — Operationalizing ML Models
A three-day, tool-neutral course on deploying an MLOps practice within an organization — maturity model, the process of moving from experiment to production, team roles, and model monitoring (drift) — for organizations building operational capability, not another single-tool course.
MLOps as a process, not just a tool
Many organizations think of MLOps as a tool choice (MLflow, Kubeflow, a cloud platform), skipping the foundation: process, roles, and accountability. The course starts with an MLOps maturity model, helping participants diagnose where their organization stands before any decision about a specific tech stack is made.
The team as the missing link
Even the best CI/CD process for ML will not work without clearly assigned roles — who is accountable for data quality, who for deployment, and who for monitoring the model once it goes live. The course devotes a dedicated module to this, addressing typical conflicts between the data science team and production engineering.
Monitoring as the last line of defense
A model that performed well on launch day can gradually degrade as input data changes — a phenomenon known as data drift. The final module teaches concrete metrics and processes for detecting this degradation before it affects business decisions made based on the model’s predictions.
Benefits
- Assess an organization's MLOps maturity and plan a development path (maturity model)
- Design a process for moving a model from experiment to production (CI/CD for ML)
- Define MLOps team roles and responsibilities (data scientist, ML engineer, platform team)
- Deploy monitoring for models in production, including drift detection and quality degradation
Who is this training for?
Prerequisites
- Basic understanding of the machine learning project lifecycle (training, evaluation, deployment)
- Experience working with a data science or engineering team helps
Training program
MLOps as an organizational practice — a maturity model
- MLOps maturity phases: from manual process to full automation
- Diagnosing an organization's current state and identifying process gaps
- Building an MLOps rollout roadmap suited to the organization's scale
- Organizational barriers: data science vs. engineering silos, lack of shared metrics
The process: from experiment to production
- CI/CD for ML models — how it differs from classic code CI/CD
- Versioning data, code, and models as a coherent practice (reproducibility)
- Model registries and managing the model lifecycle
- Testing models before production: validation, A/B tests, canary deployment
MLOps team and roles
- Division of responsibility: data scientist, ML engineer, platform/infra team
- The collaboration model between the data science team and production engineering
- Model governance: who is accountable for quality, compliance, and decommissioning decisions
- Building a culture of accountability for a model after it goes live
Monitoring models in production
- Data drift and concept drift — how they differ and how to detect them
- Metrics for monitoring model quality in real time
- Alerts and retraining processes after degradation is detected
- A landscape overview of tools (MLflow, Kubeflow, managed platforms) without recommending a single vendor
Delivery Methods
Online
- Convenience of participating from anywhere
- Interactive live sessions with trainer
- Materials available for 30 days
- No travel costs
On-site
- Direct contact with trainer and group
- Intensive hands-on workshops
- Networking with other participants
- Full focus on learning
Frequently asked questions
How is this different from MLOps: From Experiment to Production (MLflow/Kubernetes), MLOps CI/CD, MLOps for Azure ML, or MLOps on Google Vertex AI in our catalog?
Those four courses are deep, tool-specific courses — they teach a concrete MLOps implementation on a chosen tech stack (MLflow+Kubernetes, Azure Machine Learning, Vertex AI) or a specific slice of the process (CI/CD). This course is deliberately tool-neutral: it teaches how to deploy an MLOps practice as an organizational process — maturity model, team roles, governance, and monitoring — independent of whichever tech stack the organization ultimately adopts. It is a starting point for companies still deciding how to organize MLOps before choosing tools.
Is this course for managers or for ML engineers?
Both at once — the program deliberately combines an organizational perspective (maturity model, roles, governance, aimed at leaders and managers) with concrete process practices (CI/CD for ML, drift monitoring, aimed at engineers). Teams get the most value by sending both a leader and the engineer responsible for implementation.
What is data drift and why does it matter so much for production models?
Data drift is when the distribution of production input data gradually diverges from the distribution the model was trained on — the model, despite no code changes, starts producing worse predictions because the world around it has changed. Without drift monitoring, an organization only discovers model degradation after it has affected business decisions. The course teaches concrete metrics and processes for detecting this phenomenon.
Do I need to know a specific ML tool (MLflow, Kubeflow) before this course?
No — the course deliberately presents the tool landscape at a conceptual level (what a model registry is for, pipeline orchestration, monitoring) without requiring prior hands-on knowledge of any of them. Participants who already know MLflow or Kubeflow can deepen their tool-specific knowledge in dedicated, separate courses in our catalog.
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