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 More
Course Code INNO-MLforOps
Duration 3 days
Available Formats Classroom
6708 Reviews star_rate star_rate star_rate star_rate star_half
Course Image

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. 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
Contact Us 1-800-803-3948
Contact Us
FAQ Get immediate answers to our most frequently asked qestions. View FAQs arrow_forward