Detaillierter Kursinhalt
Foundations
Git
- Introduction to Version Control: Understanding the importance of version control in collaborative environments
- Git Basics: Initialization, cloning, committing, pushing, and pulling
- Branching and Merging Strategies: Efficient collaboration techniques
- Hands-on: Creating and managing repositories
CI/CD
- Introduction to CI/CD Concepts: Continuous integration and deployment fundamentals
- Tools Overview: GitHub Actions
- Hands-on: Working with GitHub Actions
- Hands-on: Building a CI/CD pipeline with GitHub Actions
Docker
- Introduction to Containerization: Understanding container technology
- Docker Architecture and Components: Key elements of Docker
- Creating and Managing Docker Images and Containers: Practical usage
- Dockerfile Basics: Writing Dockerfiles
- Hands-on: Containerizing a simple application
Kubernetes
- Introduction to Container Orchestration: Kubernetes basics
- Kubernetes Architecture and Components: Core concepts
- Hands-on: Deploying Applications on Kubernetes: Practical deployment
Cloudera AI and MLflow
Introduction to Cloudera AI
- Overview of Cloudera AI: Introduction to key features and capabilities
- Navigating Cloudera AI Environment
- Hands-on: Creating and managing projects in Cloudera AI
Experiments in Cloudera AI
- Overview of MLflow: Key concepts and integration within Cloudera AI
- Experiments in Cloudera AI
- Hands-on: MLOps with MLflow
AI Registry
- Introduction: Overview of AI registry concepts
- Onboarding Walkthrough: Step-by-step guide to onboarding models
- Architecture Overview: Understanding the AI registry architecture
Working with Cloudera AI API
- Cloudera AI API Overview: Programmatically interacting with the Cloudera AI platform
- Using the Cloudera AI API: Managing projects, jobs, models, and applications via API
- Hands-on: Working with the Cloudera AI API Python client
Advanced MLOps in Cloudera
MLOps in Cloudera AI
- Introduction to MLOps: Key concepts and principles
- MLOps Workflow: From development to production
- Challenges and Best Practices
- Hands-on:
- Getting Connected and Set Up
- Data Ingest, Exploration, and Model Training
- Model Deployment and Model Operations
- Model Registry and Model APIs
- Model Management with Model Metric Store.
Monitoring ML Systems
- Continuous Model Monitoring with Evidently AI: Tracking model performance and detecting data drift
- Why Monitor Models?: Importance of model monitoring
- Fundamentals of Monitoring ML Systems: Core principles and best practices
- A Blueprint with Evidently & Cloudera AI
- Hands-on: Continuous model monitoring with Evidently AI
Configuring and Managing AI Workbenches
- Provisioning a Cloudera AI Workbench
- Cloudera AI Workbench Administration
- Cloudera AI Auto-Scaling
- Hands-on: Using Grafana dashboards for operational oversight
Advanced Topics in MLOps and Cloudera AI
Data Access and Lineage
- SDX Overview
- Data Catalog
- Authorization
- Lineage
- Hands-on: Data Access
Data Visualization in Cloudera AI
- Data Visualization Overview
- Cloudera Data Visualization Concepts
- Using Data Visualization in Cloudera AI
- Hands-on: Build a Visualization Application
Introduction to AMPs and the Workbench
- Editors and IDE
- Git
- Embedded Web Applications
- AMPs
- Hands-on: Streamlit
Autoscaling, Performance, and GPU Settings
- Autoscaling Workloads
- Working with GPUs
- Hands-on: Deep Learning with GPUs