MLOps Made Simple (Model Deployment + Monitoring + CI/CD)

About Course

This module focuses on the deployment and monitoring of machine learning models in production environments. The goal is to equip you with the necessary skills to ensure that machine learning models are deployed effectively, continuously monitored, and integrated into production systems seamlessly. You’ll learn how to implement CI/CD pipelines (Continuous Integration and Continuous Deployment) to automate the process of model deployment and maintain scalability and reliability of your machine learning systems. With hands-on projects, you’ll understand how to manage end-to-end workflows from model creation to deployment and monitoring, making sure your models can serve real-time predictions while ensuring performance stability.

Model Deployment Fundamentals: How to deploy machine learning models to production environments.
CI/CD in Machine Learning: Implementing Continuous Integration and Continuous Deployment pipelines for automation of ML workflows.
Versioning Models and Code: Best practices for versioning models and managing model updates in production.
Monitoring Models: Monitoring model performance over time and detecting model drift.
Containerization with Docker: Using Docker to containerize machine learning models for easy deployment and scalability.
Model Serving and APIs: Building APIs for serving machine learning models for real-time predictions.
Scaling ML Workflows: Implementing scalable architectures for machine learning applications in production.
MLOps Frameworks: Learning popular MLOps tools like Kubeflow, MLflow, and TensorFlow Extended (TFX).

Course Content

Introduction to MLOps

Understanding the importance of MLOps in production environments
Overview of key MLOps tools and frameworks (Kubeflow, MLflow, TensorFlow Extended)
Role of DevOps in machine learning
The MLOps lifecycle

Model Deployment Fundamentals

CI/CD in Machine Learning

Model Versioning and Management

Monitoring and Maintaining Models

Scaling and Serving Models

End-to-End MLOps Pipeline Projects

Security in MLOps

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About Course

What Will You Learn?

Course Content

Introduction to MLOps

Understanding the importance of MLOps in production environments

Overview of key MLOps tools and frameworks (Kubeflow, MLflow, TensorFlow Extended)

Role of DevOps in machine learning

The MLOps lifecycle

Model Deployment Fundamentals

Deploying machine learning models for real-time predictions

Building APIs with Flask/FastAPI for model serving

Introduction to Docker: Containerizing machine learning models

Deploying models to the cloud (AWS, Google Cloud, Azure)

Serverless model deployment with AWS Lambda

CI/CD in Machine Learning

Introduction to CI/CD for machine learning

Building pipelines for automated testing, training, and deployment

Tools for CI/CD: Jenkins, GitLab CI, CircleCI, and GitHub Actions

Automating model training and testing in pipelines

Continuous deployment and automated model rollback

Model Versioning and Management

Versioning models and datasets using tools like MLflow and DVC (Data Version Control)

Managing model registries and metadata

Best practices for model lifecycle management

Rollbacks and managing updates in production

Monitoring and Maintaining Models

Model monitoring techniques: Tracking performance, prediction accuracy, and model drift

Building monitoring dashboards using Prometheus and Grafana

Alerting systems for model performance issues

Handling model drift and updating models based on new data

Managing model retraining pipelines

Scaling and Serving Models

Deploying scalable models using Kubernetes and Kubeflow

Implementing auto-scaling with Kubernetes and cloud platforms

Using TensorFlow Serving and TorchServe for high-performance serving

Model parallelism and distributed inference

End-to-End MLOps Pipeline Projects

Project 1: Deploy a Machine Learning Model using Flask/FastAPI: Implement and deploy a regression model using Flask/FastAPI with Docker containers.

Project 2: Build a CI/CD Pipeline for Automated Model Deployment: Set up a GitLab CI/CD pipeline to automate model training, testing, and deployment.

Project 3: Monitoring and Updating a Deployed Model: Track model performance and implement automated retraining in the cloud.

Project 4: End-to-End MLOps Workflow: Integrate all MLOps steps, including model versioning, deployment, monitoring, and scaling in a single workflow.

Security in MLOps

Securely managing model access and API keys

Authenticating and authorizing users for model APIs

Best practices for securing machine learning pipelines in production

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