Subcribe and Access : 5200+ FREE Videos and 21+ Subjects Like CRT, SoftSkills, JAVA, Hadoop, Microsoft .NET, Testin5g Tools etc..
Batch
Date: Nov
17th @9:00PM
Faculty: Mr. Maha (15+ Yrs of Exp,..)
Duration: 2 Months 15 Days
Venue
:
DURGA SOFTWARE SOLUTIONS,
Flat No : 202,
2nd Floor,
HUDA Maitrivanam,
Ameerpet, Hyderabad - 500038
Ph.No: +91 - 8885252627, 9246212143, 80 96 96 96 96
Syllabus:
AI/MLops with AWS
(Build, Deploy, and Automate ML with Data Science Projects)
Welcome to the Complete MLOps Bootcamp, your one-stop guide to mastering MLOps from scratch! This course is meticulously designed to equip you with the skills and knowledge necessary to implement and automate the deployment, monitoring, and scaling of machine learning models using the latest MLOps tools and frameworks.
Why MLOps is Essential :
In today’s fast-paced world, simply building machine learning models is not enough. To thrive as a data scientist, machine learning engineer, or DevOps professional, you need to understand how to transition your models from development to production while ensuring scalability, reliability, and continuous monitoring. This is where MLOps (Machine Learning Operations) comes into play, effectively combining the best practices of DevOps with ML model lifecycle management.
Course Overview :
This bootcamp will not only introduce you to the concepts of MLOps but will also guide you through real-world, hands-on data science projects. By the end of the course, you will be able to confidently build, deploy, and manage machine learning pipelines in production environments.
- 8 - 12 Weeks
- 20+ hands-on exercises
- Real-world projects
- Course Length: 8-12 weeks
- Certificate: Yes - Completion Certificate
Who this Course is for :
- Data Scientists and Machine Learning Engineers looking to scale and deploy ML models.
- DevOps professionals wanting to integrate ML pipelines.
- Software Engineers interested in transitioning to MLOps.
- Beginners with basic ML knowledge aiming to learn end-to-end deployment.
- IT professionals eager to understand MLOps tools and practices for real-world projects.
Tools and Technologies Covered :
What You’ll Learn :
- Introduction to the AWS MLOPS Course
- Get acquainted with the course objectives and the experienced instructor leading the way.
- Understanding MLOps
- Delve into the core concepts of MLOps, understanding its significance and application.
- DevOps Principles for Data Scientists
- Explore the principles of DevOps tailored for data scientists, bridging the gap between development and operations.
- Getting Started with AWS
- Acquaint yourself with the AWS platform, laying the foundation for subsequent sections.
- Python Prerequisites
- Brush up on essential Python programming skills needed for building data science and MLOps pipelines.
- Version Control with Git & GitHub
- Understand how to manage code and collaborate on machine learning projects using Git and GitHub.
- Docker & Containerization
- Learn the fundamentals of Docker and how to containerize your ML models for easy and scalable deployment.
- 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.
- DVC for Data Versioning
- Learn Data Version Control (DVC) to manage datasets, models, and versioning efficiently, ensuring reproducibility in your ML pipelines.
- DagsHub for Collaborative MLOps
- Utilize DagsHub for integrated tracking of your code, data, and ML experiments using Git and DVC.
- Apache Airflow with Astro
- Automate and orchestrate your ML workflows using Airflow with Astronomer, ensuring your pipelines run seamlessly.
- ETL Pipeline Implementation
- Build and deploy complete ETL (Extract, Transform, Load) pipelines using Apache Airflow, integrating data sources for machine learning models.
- CI/CD Pipeline with GitHub Actions
- Implement a continuous integration/continuous deployment (CI/CD) pipeline to automate testing, model deployment, and updates.
- 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.
- 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.
- AWS SageMaker for ML Deployment
- Learn how to deploy, scale, and monitor your models on AWS SageMaker, integrating seamlessly with other AWS services.
- Gen AI with AWS Cloud
- Explore Generative AI techniques and learn how to deploy these models using AWS cloud infrastructure.
- Monitoring with Grafana & PostgreSQL
- Monitor the performance of your models and pipelines using Grafana dashboards connected to PostgreSQL for real-time insights.
Join us in this comprehensive journey to mastering MLOps, and elevate your career in the ever-evolving world of data science and machine learning!
