Training
DSCI-272: Predicting with MLOps on Cloudera AI
Overview
Enterprise data science teams need collaborative access to business data, tools, and computing resources required to develop and deploy machine learning workflows. Cloudera AI, part of the Cloudera platform, provides the solution, giving data science teams the required resources.
This four-day course covers machine learning workflows and operations using Cloudera AI. Participants explore, visualize, and analyze data. You will also train, evaluate, and deploy machine learning models.
The course walks through an end-to-end data science and machine learning workflow based on realistic scenarios and datasets from a fictitious technology company. The demonstrations and exercises are conducted in Python (with PySpark) using Cloudera AI.
What you'll learn
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 models
What to expect
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.
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Course Details
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
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
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
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