When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
Machine Learning for Operations
Machine Learning and AI represent a great opportunity. All too often, taking a Machine Learning prototype to production makes a difference between success and failure in the AI strategy of a company....
Read MoreMachine Learning and AI represent a great opportunity. All too often, taking a Machine Learning prototype to production makes a difference between success and failure in the AI strategy of a company. This need is fulfilled by Machine Learning Engineers who apply the rules of DevOps to AI.
Skills Gained
Solutions
- This course teaches how to take Machine Learning and AI and reduce it to practice. Thus, the name: ML Ops.
Who Can Benefit
The audience for this class is Engineers, Developers, DevOps, Architects and any other IT personnel interested in learning practical usage of Machine Learning and Artificial Intelligence.
Prerequisites
- Comfortable developing code
Course Details
Outline
Rise of the Machine Learning Engineer and MLOps
- What is MLOps
- DevOps and MLOps
- A MLOps Hierarchy of Needs
- Implementing DevOps
- DataOps and Data Engineering
- Platform Automation
- MLOps
- Where Can You Deploy?
- Conclusion
MLOps Foundations
- Bash and the Linux Command Line
- Cloud Shell Development Environments
- Bash Shell and Commands
- List Files
- Run Commands
- Files and Navigation
- Input/Output
- Configuration
- Writing a Script
- Cloud Computing Foundations & Building Blocks
- Machine Learning Key Concepts
- Build an MLOps Pipeline from Zero
MLOps for Containers and Edge Devices
- Containers
- Serving a trained model over HTTP
- Edge Devices
- Coral
- Azure Percept
- TFHub
Continuous Delivery for Machine Learning Models
- Packaging for ML Models
- Infrastructure as Code for Continuous Delivery of ML Models
- Using Cloud Pipelines
- Controlled Rollout of models
- Testing techniques for Model Deployment
Monitoring and Logging
- Introduction to Logging
- Logging in Python
- Modifying log levels
- Logging different applications
- Monitoring Drift with AWS SageMaker
- Monitoring Drift with Azure ML
MLOps for Azure
- Azure CLI and Python SDK
- Authentication
- Service Principal
- Authenticating API Services
- Compute Instances
- Deploying
- Registering Models
- Versioning datasets
- Deploying Models to a Compute Cluster
- Configuring a Cluster
- Deploying a Model
- Azure ML Pipelines
Machine Learning Interoperability
- Why interoperability is critical
- ONNX: Open Neural Network Exchange
- Convert PyTorch into ONNX
- Convert TensorFlow into ONNX
- Apple Core ML
