ML Next Step – ML Ops
Enable teams to operationalise Machine-Learning models via MLOps best practices, tooling, and cloud pipelines.
Get Course Info
Audience: Developers, Team Leads, Project Managers
Duration: 3 days
Format: Lectures and hands-on labs (50% lecture, 50% lab)
Overview
Moving from ML prototypes to production requires disciplined engineering processes. This course teaches DevOps principles applied to AI—collectively known as MLOps—covering pipelines, CI/CD, monitoring, and edge deployment.
Objective
Enable teams to operationalise Machine-Learning models via MLOps best practices, tooling, and cloud pipelines.
What You Will Learn
- MLOps foundations & hierarchy of needs
- DevOps & DataOps applied to ML
- Cloud-shell dev environments & Bash scripting
- Building end-to-end MLOps pipelines
- Packaging & continuous delivery of models (IaC, cloud pipelines)
- Monitoring drift & logging
- MLOps on Azure (CLI, SDK, pipelines)
- Model interoperability with ONNX
Course Details
Audience: Developers, Team Leads, Project Managers
Duration: 3 days
Format: Lectures and hands-on labs (50% lecture, 50% lab)
- Comfortable developing code in the target environment
Setup: Zero-install cloud lab • Modern laptop • Chrome browser
Detailed Outline
- DevOps vs. MLOps
- Hierarchy of needs
- Platform automation
- Deployment targets
- Bash & Linux
- Cloud-shell dev environments
- Key ML concepts
- Build a pipeline from scratch
- Serving models over HTTP
- Edge devices (Coral, Azure Percept)
- TFHub
- Packaging
- IaC
- Cloud pipelines
- Controlled rollout
- Testing techniques
- Python logging
- Monitoring drift in AWS SageMaker & Azure ML
- Azure CLI & SDK
- Auth
- Compute instances & clusters
- Versioning datasets
- Pipelines
- ONNX
- Converting PyTorch & TensorFlow models
- Apple Core ML
Ready to Get Started?
Contact us to learn more about this course and schedule your training.