Machine Learning has become an integral part of businesses across all industries, from healthcare and finance to retail and manufacturing. However, developing and deploying Machine Learning models at scale can be a complex and challenging task. This is where Machine Learning Operations (MLOps) comes in. MLOps is an emerging field that aims to streamline the process of developing and deploying Machine Learning models by applying DevOps principles to Machine Learning workflows.
Throughout this 8-hour comprehensive workshop, we’ll guide you into the evolving realm of Machine Learning Operations (MLOps). We’ll delve into a deep understanding of its architecture, demonstrating the blueprint for successful implementation. We’ll shed light on its practical applications, demonstrating hands-on exercises with Natural Language Processing (NLP) and Computer Vision models. Emphasizing on practical knowledge, we will cover the deployment of MLOps using these data models, intended to bolster your productivity. Additionally, we’ll ensure you’re equipped with an MLOps toolkit, knowledge to pick the right platform, and are aware of the standard best practices for MLOps mastery. The workshop also aims to bust myths associated with MLOps, providing a comprehensive and clear understanding of this important field.
Workshop Highlights:
Module 1: Succinct Prelude to MLOps
- What MLOps Is and Why It Matters
- Busting The Buzzwords: DevOps, AIOps, and MLOps
- Lifecycle of a Machine Learning Model
- Different levels in MLOps Deployment
Module 2: The MLOps Sackmesser
- MLOps Toolkits for
- Model Development
- Tuning
- Deployment
- Monitoring
Module 3: MLOps and Real World Scenarios
- Hands-on: MLOps Deployment on a Structured Dataset
- Hands-on: MLOps Deployment with NLP
- Bonus: Brief Introduction to Foundation Models and Prompt Engineering
- Hands-on: MLOps Deployment with Computer Vision
Module 4: MLOps Governance
- Feedback Loop
- Maturity
- Data and Process Governance
Module 5: From Here to Where
- Power Up Your Productivity
- Learn to Pick the Right Platform
- Standard Practices for MLOps Mastery
Prerequisites:
- System Requirement and Setup
- Laptop with at least 8 GB RAM
- Linux OS (preferred OS)
- GitHub Account
- Google Colab
- Docker Desktop installation (install the package as per your OS)
- Will be good to have 8 GB RAM available and 8 GB Storage
- Google Cloud Platform Free Tier
- Pre-reads
- Familiarity with Python
- Understanding of Docker and Containers
Note: These are tentative details and are subject to change.
