Prerequisites:
o Basic understanding of Python programming
o Familiarity with machine learning concepts
o Basic knowledge of Docker and Kubernetes
o Basic knowledge of Docker and Kubernetes
Module 1: Introduction to MLOps Concepts
1.1 Introduction to MLOps
o What is MLOps?
o Importance of MLOps
o MLOps Tools and Techniques
1.2 Overview of Course Structure
o Course Goals and Outcomes
o Tools and Technologies Overview
Module 2: Python for MLOps
2.1 Fundamentals of Python for MLOps
o Key Python Libraries and Frameworks (e.g., NumPy, Pandas, Scikit Learn)
o Python Programming Basics for ML Tasks
2.2 Streamlining ML Processes Using Python
o Automation Scripts for ML Workflows
o Data Pipelines and ETL Processes
2.3 Effective MLOps Practices with Python
o Code Organization and Maintenance
o Best Practices for Python in MLOps
Module 3: Python for Data Science
3.1 Python in the Context of Data Science
o Python’s Role in Data Science
o Key Libraries (e.g., NumPy, Pandas, Matplotlib)
3.2 Data Manipulation and Analysis Techniques
o Data Cleaning and Transformation
o Exploratory Data Analysis (EDA)
3.3 Enhancing the Data Science Lifecycle with Python
o Integrating Python with Data Science Tools
o Optimizing Data Science Workflows
Module 4: Git and GitHub Fundamentals
4.1 Essentials of Version Control with Git
o Git Basics: Commands and Workflow
o Branching and Merging
4.2 Collaborative Development with GitHub in MLOps
o GitHub Features: Repositories, Pull Requests, Issues
o Collaboration and Code Review
4.3 Managing and Tracking Changes in MLOps Projects
o Versioning ML Models
o Tracking Experiment Changes
Module 5: Model Development and Training
o Introduction to Model Development
o Common Algorithms and Techniques
5.2 Data Preprocessing and Feature Engineering
o Data Cleaning and Transformation
o Feature Selection and Engineering
5.3 Training the ML Model
o Training Strategies and Techniques
o Parameter Tuning and Optimization
5.4 Model Evaluation and Validation
o Metrics and Cross Validation Techniques
o Assessing Model Performance
5.5 Reproducible Environments
o Creating Reproducible ML Environments
o Versioning and Tracking Models
5.6 Preparing Models for Deployment
o Model Serialization
o Packaging and Exporting Models
Module 6: Docker for Machine Learning
6.1 Introduction to Docker
o What is Docker?
o Benefits of Containerization
6.2 Containerization Concepts
o Docker Images and Containers
o Docker Registries and Dockerfile
6.3 Docker Builds
o Building Docker Images
o Building Docker Images
6.4 Docker for ML Projects
o Creating Docker Images for ML o Building and Deploying Docker Containers for ML Projects
Module 7: Kubernetes Fundamentals
7.1 Introduction to Kubernetes
o What is Kubernetes?
o Key Concepts and Architecture
7.2 Core Kubernetes Components
o Pods, Services, Deployments
o ReplicaSets, ConfigMaps, Secrets
7.3 Kubernetes Storage
o Volumes, PersistentVolumes, PersistentVolumeClaims
Module 8: Kubeflow for MLOps
8.1 Introduction to Kubeflow for MLOps
o Kubeflow Components Overview
o Integrating Kubeflow into MLOps Pipelines
8.2 Installing and Setting Up Kubeflow
o Installation on Kubernetes
o Basic Configuration
o Basic Configuration
o Building and Running ML Workflows
o Creating and Managing Pipelines
8.4 Hyperparameter Tuning with Katib
o Introduction to Katib
o Configuring and Running Hyperparameter Tuning Jobs
8.5 Model Serving with KFServing
o Deploying Models with KFServing
o Managing Model Endpoints
8.6 Integrating Kubeflow Components
o End to End ML Workflows
o Best Practices for Kubeflow Integration
Module 9: Managing ML Experiments with MLflow
9.1 Introduction to MLflow
o Overview of MLflow and Its Components
o Installing and Setting Up MLflow
9.2 Tracking Experiments
o Logging Parameters, Metrics, and Artifacts
o Comparing and Visualizing Experiments
9.3 Managing Experiment Lifecycle
o Managing Experiment Runs
o Versioning and Tracking Models
9.4 MLflow Models
o Saving and Loading Models
o Packaging Models for Deployment
9.5 MLflow Projects
o Defining and Running MLflow Projects
o Integrating MLflow with CI/CD Pipelines
Module 10: CI/CD for Machine Learning
10.1 Introduction to CI/CD
o CI/CD Concepts and Importance
o CI/CD in the Context of MLOps
10.2 Automating Development and Testing
o Automating ML Workflows
o Testing ML Models and Pipelines
10.3 Deployment of ML Models
o Automating Model Deployment
o Building Robust CI/CD Pipelines
10.4 Jenkins for MLOps
o Introduction to Jenkins
o Setting Up and Configuring Jenkins
o Automating MLOps Workflows with Jenkins
o Enhancing CI/CD with Jenkins
Module 11: Continuous Monitoring with Prometheus and Grafana
11.1 Introduction to Prometheus
o What is Prometheus?
o Prometheus Concepts and Architecture
11.2 Installing and Configuring Prometheus
o Installation Steps
o Configuring Prometheus for ML Monitoring
11.3 Monitoring ML Applications
o Setting Up Metrics Collection
o Analysing and Visualizing Metrics
11.4 Introduction to Grafana
o What is Grafana?
o Installing Grafana
o Integrating Grafana with Prometheus
11.5 Creating Dashboards and Alerting
o Creating and Configuring Dashboards in Grafana
o Setting Up Alerts for ML Applications