Complete MLOps Bootcamp With 10+ End To End ML Projects

Course Name : Complete MLOps Bootcamp With 10+ End To End ML Projects
Students : 353
Duration : 47.5 hours & 36 downloadable resources
Avg Rating : 4.1
Original Price : $119.99
Discount Price : $9
Instructor / Provider : Krish Naik, Udemy
Course Type : Self Paced Online Course. Lifetime Access
Coupon : Click on ENROLL NOW to apply discount code

What You’ll Learn:

1. Python Prerequisites: Brush up on essential Python programming skills needed for building data science and MLOps pipelines.
2. Version Control with Git & GitHub: Understand how to manage code and collaborate on machine learning projects using Git and GitHub.
3. Docker & Containerization: Learn the fundamentals of Docker and how to containerize your ML models for easy and scalable deployment.
4. MLflow for Experiment Tracking: Master the use of MLFlow to track experiments, manage models, and seamlessly integrate with AWS Cloud for model management and deployment.
5. DVC for Data Versioning: Learn Data Version Control (DVC) to manage datasets, models, and versioning efficiently, ensuring reproducibility in your ML pipelines.
6. DagsHub for Collaborative MLOps: Utilize DagsHub for integrated tracking of your code, data, and ML experiments using Git and DVC.
7. Apache Airflow with Astro: Automate and orchestrate your ML workflows using Airflow with Astronomer, ensuring your pipelines run seamlessly.
8. CI/CD Pipeline with GitHub Actions: Implement a continuous integration/continuous deployment (CI/CD) pipeline to automate testing, model deployment, and updates.
9. ETL Pipeline Implementation: Build and deploy complete ETL (Extract, Transform, Load) pipelines using Apache Airflow, integrating data sources for machine learning models.
10. End-to-End Machine Learning Project: Walk through a full ML project from data collection to deployment, ensuring you understand how to apply MLOps in practice.
11. End-to-End NLP Project with Huggingface: Work on a real-world NLP project, learning how to deploy and monitor transformer models using Huggingface tools.
12. AWS SageMaker for ML Deployment: Learn how to deploy, scale, and monitor your models on AWS SageMaker, integrating seamlessly with other AWS services.
13. Gen AI with AWS Cloud: Explore Generative AI techniques and learn how to deploy these models using AWS cloud infrastructure.
14. Monitoring with Grafana & PostgreSQL: Monitor the performance of your models and pipelines using Grafana dashboards connected to PostgreSQL for real-time insights.

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