AI and Data in Business
MLOps for Enterprise AI Products
This in-depth course develops directly applicable capability in MLOps for Enterprise AI Products. It connects ML Product Lifecycle and Governance, Data and Feature Pipelines, and Packaging and Deployment to the decisions, controls, and activities participants need to perform in their workplace.
Overview
Practical learning for workplace transfer.
This in-depth course develops directly applicable capability in MLOps for Enterprise AI Products. It connects ML Product Lifecycle and Governance, Data and Feature Pipelines, and Packaging and Deployment to the decisions, controls, and activities participants need to perform in their workplace. The five-module curriculum progresses toward Production Readiness Workshop, using evidence, scenarios, and work products appropriate to the subject.
Objectives
- Analyze ml product lifecycle and governance, including use-case ownership, model risk tiering, and approval gates.
- Configure or structure data and feature pipelines, including training-serving consistency and feature-store patterns.
- Evaluate packaging and deployment, including model registries, containers, endpoints, and batch scoring.
- Manage monitoring and model operations, including prediction quality, drift, latency, errors, and cost metrics.
- Apply production readiness workshop, including design an mlops control flow for a selected model.
Target audience
- Professionals responsible for this subject area
- Managers, supervisors, and team leaders
- Analysts, specialists, engineers, or coordinators working with the relevant processes
- Project, implementation, assurance, or improvement team members
- Professionals preparing for broader responsibilities in this field
Program outline
A clear structure for the learning journey.
Program outline
Outline points are grouped in one designed block instead of being treated as separate module cards.
Module 1: ML Product Lifecycle and Governance
Use-case ownership, model risk tiering, and approval gates
Experiment tracking, reproducibility, and artifact lineage
Development, validation, staging, and production separation
Module 2: Data and Feature Pipelines
Training-serving consistency and feature-store patterns
Data validation, drift baselines, and pipeline orchestration
Versioned datasets and privacy-aware retention
Module 3: Packaging and Deployment
Model registries, containers, endpoints, and batch scoring
Canary, shadow, blue-green, and rollback strategies
CPU and GPU resource sizing and autoscaling
Module 4: Monitoring and Model Operations
Prediction quality, drift, latency, errors, and cost metrics
Alert thresholds, incident triage, and retraining triggers
Champion-challenger decisions and model retirement
Module 5: Production Readiness Workshop
Design an MLOps control flow for a selected model
Define deployment, monitoring, and rollback evidence
Conduct a model release review and operating handover
Materials provided
- ○ Course-specific presentation slides
- ○ Guided exercises, scenarios, or configured-environment activities appropriate to the subject
- ○ Course-specific worksheets, checklists, or calculation templates
- ○ Applied workplace case materials
- ○ 4D Certificate of Completion issued by 4D Training & Consultancy
- ○ Post-course support for implementation questions
Training Options
Programs can be delivered in-house, online, or in a blended format depending on your team's schedule, location, and learning objectives. When an external certificate or exam is included, certification rules and fees remain under the relevant awarding body's policies, while 4D provides the training and preparation support.
Why choose 4D
4D Training & Consultancy adapts the program to the client’s operating environment. Delivery combines structured explanation with subject-specific analysis, exercises, and implementation decisions so participants can transfer the learning to real responsibilities without implying vendor authorization.
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