MLOps Online Training - Abhi Trainings.

MLops Training - Details

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

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

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MLops Training - Details

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