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MohammadB88/MLOps-Workshop-Exercises
MLOps Workshop
MohammadB88/MLOps-Workshop-Exercises
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MLOps Overview
What is MLOps?
Hands-on Labs
Hands-on Labs
Workshop Overview
Beginner
Beginner
Wine Quality Classifier
Bike Forecasting
Bike Forecasting
Overview
00 - Environment & Prerequisites
01 - Load, Extract and Clean the Data
02 - Prepare the Data for Training
03 - Model Training and Experiment Tracking
04 - Review Experiments & Select Best Model
05 - Containerize Model Endpoint-API
06 - Deploy on OpenShift Cluster
07 - Test Model Endpoint-API
08 - Model & Data Monitoring
Intermediate
Intermediate
LLM Instruction Tuning
LLM Instruction Tuning
Overview
01 - Setup & Exploration
02 - Data Preparation
03 - LoRA Tuning
04 - Evaluation
05 - Versioning & Packaging
06 - Deployment & Serving
Bike Demand Forecasting Pipeline
Bike Demand Forecasting Pipeline
Overview
00 - Environment & Prerequisites
01 - Dataset Ingestion and Processing
02 - Model Training and Experiment Tracking
03 - Model Registration and Validation
04 - Model Serving Preparation
05 - Model Monitoring with Evidently
Advanced
Advanced
Kubeflow Advanced
Kubeflow Advanced
Overview
01 - Basic Triggers
02 - Advanced Workflows
03 - Scheduling & Automation
04 - Optimization & Scaling
05 - Monitoring & Debugging
ML Security & Compliance
ML Security & Compliance
Overview
01 - Security Basics
02 - Governance Implementation
03 - Audit Logging
04 - Compliance Reporting
Theory
Theory
Introduction to MLOps
Environment & Prerequisites
Environment & Prerequisites
Environment Setup
Environment Setup
Overview
Red Hat Demo Environment
OpenShift AI Installation
Git Cheat Sheet
MLflow Installation
Helm Installation
MLOps Overview
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What is MLOps and why and which tools and workflows.
Introduction
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