📅 Date: 13 April

⏰ Time: 4PM

🌐 Location: Virtual

What’s Happening in Week 9?

In this 1-hour session, participants will learn about MLOps (Machine Learning Operations) and its application to Generative AI, focusing on tools, techniques, and frameworks for deploying and managing these models in production. 

After the session, self-paced resources will guide participants through hands-on implementation.

About the Session: MLOps for Generative AI

Topics Covered in the Session:

1️⃣ What is MLOps? – An overview of MLOps principles and their significance in generative AI workflows.

2️⃣ Key Challenges in Generative AI Deployment – Managing model drift, scalability, latency, and versioning for generative AI models.

3️⃣ Tools and Frameworks for MLOps –

• Vertex AI for model training, deployment, and monitoring
• MLflow for experiment tracking
• Kubeflow for pipeline orchestration
• Docker & Kubernetes for containerization and scaling

4️⃣ Integrating Foundation Models – Learn how MLOps practices can be adapted for foundation models like GPT, BERT, and other large-scale generative AI models.

5️⃣ Real-World Applications – Explore case studies on deploying chatbots, content generators, and other generative AI solutions at scale.

Requisites for Week 9:

1️⃣ Completion of Week 8’s Domain-Specific LLMs session 📚

2️⃣ Familiarity with basic MLOps tools and principles from prior knowledge 🌐

3️⃣ Enthusiasm to explore advanced operational workflows and automation ✅

This session equips participants with the practical knowledge to bring their generative AI models to life, ensuring they are robust, scalable, and maintainable in real-world environments.

Complete Series Overview

This program unfolds across multiple weeks, offering foundational insights and advanced explorations into machine learning and generative AI.

ML Study Jam Weekly Plan:

1️⃣ Week 1: Intro to Machine Learning

2️⃣ Week 2: Intermediate Machine Learning

3️⃣ Week 3: Intro to Deep Learning

4️⃣ Week 4: Intro to AI Ethics

5️⃣ Week 5: Foundational Models & Prompt Engineering

6️⃣ Week 6: Embeddings and Vector Stores/Databases

7️⃣ Week 7: Generative AI Agents

8️⃣ Week 8: Domain-Specific LLMs

9️⃣ Week 9: MLOps for Generative AI

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