Prerequisites:
o Basic understanding of Python programming
o Familiarity with machine learning concepts
o Basic knowledge of Docker and Kubernetes
o Understanding of version control with Git
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: Model Development and Training
4.1 Building a Machine Learning Model
o Introduction to Model Development
o Common Algorithms and Techniques
4.2 Data Preprocessing and Feature Engineering
o Data Cleaning and Transformation
o Feature Selection and Engineering
4.3 Training the ML Model
o Training Strategies and Techniques
o Parameter Tuning and Optimization
4.4 Model Evaluation and Validation
o Metrics and Cross Validation Techniques
o Assessing Model Performance
4.5 Reproducible Environments
o Creating Reproducible ML Environments
o Versioning and Tracking Models
4.6 Preparing Models for Deployment
o Model Serialization
o Packaging and Exporting Models
Module 5: Flask for Model Deployment>
5.1 Introduction to Flask
- What is Flask?
- Benefits of Using Flask for ML Applications
5.2 Setting Up a Flask Application
- Creating a Basic Flask App
- Structuring Flask Projects
5.3 Integrating Machine Learning Models with Flask
- Loading and Using ML Models in Flask
- Creating APIs for Model Predictions
5.4 Deploying Flask Applications
- Running Flask Applications Locally
- Best Practices for Deployment
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: Working with Kubernetes Kind>
8.1 Introduction to Kubernetes Kind
- What is Kind?
o - Use Cases and Benefits
8.2 Setting Up Kind for Local Development
- Installing Kind
- Creating Kubernetes Clusters with Kind
8.3 Deploying ML Applications with Kind
- Running ML Workflows in Kind
- Best Practices for Local Development
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
Module 13: AWS Services for MLOps
13.1 Introduction to AWS EKS
- What is Amazon EKS?
- Benefits of Managed Kubernetes
13.2 Working with AWS ECR
- Overview of Amazon ECR
- Storing and Managing Docker Images
13.3 Deploying ML Applications on AWS EKS
- Setting Up EKS Clusters
- Deploying and Managing ML Workloads