This repository is a comprehensive guide to mastering MLOps, focusing primarily on tools and practices for productionizing machine learning models. The primary goal is to equip you with the skills needed to manage the deployment, monitoring, and scalability of ML systems, rather than focusing on data cleaning or model building.
We will first cover essential MLOps tools and workflows, followed by end-to-end projects to integrate these concepts into real-world scenarios.
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Introduction to MLOps
Learn the fundamentals of MLOps, its importance, and the lifecycle of data science projects before and after implementing MLOps. -
Git and GitHub
Understand version control and collaboration using Git and GitHub, essential tools for managing and sharing your codebase efficiently. -
OOP in Python
Learn Object-Oriented Programming principles in Python to write modular, reusable, and maintainable code, including best practices for debugging and logging. -
Data Versioning
Manage and track changes in data using tools like DVC to ensure reproducibility and streamline collaboration in ML pipelines. -
Experiment Tracking
Track and manage ML experiments with tools like MLflow, Neptune, and Kubeflow to optimize models and maintain an organized workflow. -
Continuous Integration (CI)
Automate testing and deployment pipelines with CI tools like GitHub Actions, CircleCI, and TravisCI to ensure a reliable workflow. -
Docker
Learn containerization to package your applications and dependencies, ensuring consistency across development, testing, and production environments. -
MongoDB
Explore MongoDB, a NoSQL database, to store and manage unstructured data for modern applications and ML workflows. -
Data Ingestion
Learn techniques for collecting, loading, and preprocessing data efficiently to feed your ML pipelines. -
Data Validation
Ensure data quality by validating schemas, handling missing values, and checking for inconsistencies to improve model performance. -
Kubernetes
Understand how to use Kubernetes for scaling, managing, and deploying containerized applications in distributed environments. -
Prometheus and Grafana
Monitor and visualize the health and performance of applications and infrastructure using Prometheus and Grafana.
- AWS Services: Explore cloud services like IAM, ECR, S3, and EC2 to build and manage scalable ML workflows.
- All-in-One Services: Learn about comprehensive solutions like AWS SageMaker, Google Vertex AI, and Azure ML for end-to-end ML pipeline management.
After mastering the tools, we will move to hands-on end-to-end projects to integrate MLOps workflows with real-world data science tasks. These projects will provide a complete understanding of managing ML systems in production environments.
This repository is organized into topic-specific folders. Each folder contains hands-on guides, examples, and resources to help you understand the concepts better.
To clone this repository, run the following command in your terminal:
git clone https://github.com/devrahulbanjara/MLOps.gitThis repository is licensed under the MIT License.