Duration
MLops using Python Programming
3 days
Overview
This three-day training program on MLOps using Python is designed to provide participants with the foundational knowledge and practical skills necessary to streamline the deployment and management of machine learning models in production environments. Participants will gain an understanding of the MLOps lifecycle, including data management, model development, version control, continuous integration, deployment, and monitoring. Through a combination of theoretical concepts and hands-on exercises, attendees will be equipped to implement MLOps practices effectively and improve collaboration between data scientists and operations teams.
Learning Outcomes
By the end of this training, participants will be able to:
- Understand and apply MLOps principles in real-world scenarios.
- Set up and manage the development environment for machine learning projects.
- Utilize version control systems for collaboration and data versioning.
- Develop and deploy machine learning models using scalable solutions.
- Implement monitoring and reporting tools to ensure model performance.
- Automate workflows and integrate continuous integration/continuous deployment (CI/CD) pipelines for machine learning.
Course Outline
Day
Time
Topic
Description
Day 1
9 AM - 10 AM
Introduction to MLOps
Overview of MLOps, Importance, and Key Concepts
10 AM - 12 PM
Setting Up the Environment
Installation of Python, Jupyter Notebooks, and Required Libraries
12 PM - 1 PM
Lunch Break
1 PM - 3 PM
Version Control with Git
Basics of Git, Creating Repositories, and Collaboration
3 PM - 5 PM
Data Management
Data Collection, Storage Solutions, and Versioning of Datasets
Day 2
9 AM - 10:30 AM
Model Development
Introduction to scikit-learn and Building ML Models
10:30 AM - 12 PM
Experiment Tracking
Using MLflow for Experiment Tracking and Logging
12 PM - 1 PM
Lunch Break
1 PM - 2:30 PM
Continuous Integration (CI)
Setting Up CI Pipelines for ML Models
2:30 PM - 5 PM
Model Deployment
Techniques for Deploying Models Using Docker and Flask
Day 3
9 AM - 10:30 AM
Monitoring & Logging
Monitoring Models in Production and Key Metrics
10:30 AM - 12 PM
Automation of MLOps
Introduction to Airflow and Automating Workflow
12 PM - 1 PM
Lunch Break
1 PM - 3 PM
Scalability & Performance
Scaling Models and Improving Performance
3 PM - 5 PM
Final Project & Review
Hands-on project incorporating learned concepts, Review & Q&A
Introduction to MLOps
Setting Up the Environment
Version Control with Git
Data Management
Model Development
Experiment Tracking
Continuous Integration (CI)
Model Deployment
Monitoring & Logging
Automation of MLOps
Scalability & Performance
Final Project & Review
Programme scope, delivery arrangements and suitability are confirmed during enquiry.