DSCI-272CRClouderaCR-DSCI-2721.0<p>Through lecture and hands-on exercises, you will learn how to:</p> <ul> <li>Utilize Cloudera SDX and other components of the Cloudera platform</li><li>to locate data for machine learning experiments</li><li>Use an Cloudera Accelerators for Machine Learning Projects (AMPs)</li><li>Manage machine learning experiments</li><li>Connect to various data sources and explore data</li><li>Deploy an ML model as a REST API</li><li>Manage and monitor deployed ML models</li></ul><p>The course is designed for data scientists who need to understand how to utilize Cloudera AI and the Cloudera platform to achieve faster model development and deliver production machine learning at scale. Data engineers, developers, and solution architects who collaborate with data scientists will also find this course valuable.</p><ul> <li>Projects and Sessions</li><li>ML Runtimes</li><li>Jobs and Pipelines</li><li>Machine Learning Workflow</li><li>Applications and AMPs</li><li>Experiments and Models</li><li>Model deployment</li><li>Data Visualization</li><li>CI/CD workflows</li><li>Cloudera AI API</li><li>Metrics and Monitoring</li><li>Performance and GPUs</li></ul><h5>Foundations</h5><h6>Git</h6><ul> <li>Introduction to Version Control: Understanding the importance of version control in collaborative environments</li><li>Git Basics: Initialization, cloning, committing, pushing, and pulling</li><li>Branching and Merging Strategies: Efficient collaboration techniques</li><li>Hands-on: Creating and managing repositories</li></ul><h6>CI/CD</h6><ul> <li>Introduction to CI/CD Concepts: Continuous integration and deployment fundamentals</li><li>Tools Overview: GitHub Actions</li><li>Hands-on: Working with GitHub Actions</li><li>Hands-on: Building a CI/CD pipeline with GitHub Actions</li></ul><h6>Docker</h6><ul> <li>Introduction to Containerization: Understanding container technology</li><li>Docker Architecture and Components: Key elements of Docker</li><li>Creating and Managing Docker Images and Containers: Practical usage</li><li>Dockerfile Basics: Writing Dockerfiles</li><li>Hands-on: Containerizing a simple application</li></ul><h6>Kubernetes</h6><ul> <li>Introduction to Container Orchestration: Kubernetes basics</li><li>Kubernetes Architecture and Components: Core concepts</li><li>Hands-on: Deploying Applications on Kubernetes: Practical deployment</li></ul><h5>Cloudera AI and MLflow</h5><h6>Introduction to Cloudera AI</h6><ul> <li>Overview of Cloudera AI: Introduction to key features and capabilities</li><li>Navigating Cloudera AI Environment</li><li>Hands-on: Creating and managing projects in Cloudera AI</li></ul><h6>Experiments in Cloudera AI</h6><ul> <li>Overview of MLflow: Key concepts and integration within Cloudera AI</li><li>Experiments in Cloudera AI</li><li>Hands-on: MLOps with MLflow</li></ul><h6>AI Registry</h6><ul> <li>Introduction: Overview of AI registry concepts</li><li>Onboarding Walkthrough: Step-by-step guide to onboarding models</li><li>Architecture Overview: Understanding the AI registry architecture</li></ul><h6>Working with Cloudera AI API</h6><ul> <li>Cloudera AI API Overview: Programmatically interacting with the Cloudera AI platform</li><li>Using the Cloudera AI API: Managing projects, jobs, models, and applications via API</li><li>Hands-on: Working with the Cloudera AI API Python client</li></ul><h5>Advanced MLOps in Cloudera</h5><h6>MLOps in Cloudera AI</h6><ul> <li>Introduction to MLOps: Key concepts and principles</li><li>MLOps Workflow: From development to production</li><li>Challenges and Best Practices</li><li>Hands-on:</li><li>Getting Connected and Set Up</li><li>Data Ingest, Exploration, and Model Training</li><li>Model Deployment and Model Operations</li><li>Model Registry and Model APIs</li><li>Model Management with Model Metric Store.</li></ul><h6>Monitoring ML Systems</h6><ul> <li>Continuous Model Monitoring with Evidently AI: Tracking model performance and detecting data drift</li><li>Why Monitor Models?: Importance of model monitoring</li><li>Fundamentals of Monitoring ML Systems: Core principles and best practices</li><li>A Blueprint with Evidently &amp; Cloudera AI</li><li>Hands-on: Continuous model monitoring with Evidently AI</li></ul><h6>Configuring and Managing AI Workbenches</h6><ul> <li>Provisioning a Cloudera AI Workbench</li><li>Cloudera AI Workbench Administration</li><li>Cloudera AI Auto-Scaling</li><li>Hands-on: Using Grafana dashboards for operational oversight</li></ul><h5>Advanced Topics in MLOps and Cloudera AI</h5><h6>Data Access and Lineage</h6><ul> <li>SDX Overview</li><li>Data Catalog</li><li>Authorization</li><li>Lineage</li><li>Hands-on: Data Access</li></ul><h6>Data Visualization in Cloudera AI</h6><ul> <li>Data Visualization Overview</li><li>Cloudera Data Visualization Concepts</li><li>Using Data Visualization in Cloudera AI</li><li>Hands-on: Build a Visualization Application</li></ul><h6>Introduction to AMPs and the Workbench</h6><ul> <li>Editors and IDE</li><li>Git</li><li>Embedded Web Applications</li><li>AMPs</li><li>Hands-on: Streamlit</li></ul><h6>Autoscaling, Performance, and GPU Settings</h6><ul> <li>Autoscaling Workloads</li><li>Working with GPUs</li><li>Hands-on: Deep Learning with GPUs</li></ul>Through lecture and hands-on exercises, you will learn how to: - Utilize Cloudera SDX and other components of the Cloudera platform - to locate data for machine learning experiments - Use an Cloudera Accelerators for Machine Learning Projects (AMPs) - Manage machine learning experiments - Connect to various data sources and explore data - Deploy an ML model as a REST API - Manage and monitor deployed ML modelsThe course is designed for data scientists who need to understand how to utilize Cloudera AI and the Cloudera platform to achieve faster model development and deliver production machine learning at scale. Data engineers, developers, and solution architects who collaborate with data scientists will also find this course valuable.- Projects and Sessions - ML Runtimes - Jobs and Pipelines - Machine Learning Workflow - Applications and AMPs - Experiments and Models - Model deployment - Data Visualization - CI/CD workflows - Cloudera AI API - Metrics and Monitoring - Performance and GPUsFoundations 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 GPUs4 days3520.003520.00