Edureka! Masterclass

Description

This hands-on session walks you through fine-tuning Meta's LLaMA 3.2 3B on a custom instruction dataset using QLoRA + Hugging Face PEFT - live on a free Google Colab T4 GPU. No expensive hardware. No black-box magic. Just clean, reproducible code and a real understanding of why it works.We'll break down the core trade-offs between full fine-tuning, LoRA/QLoRA, and RAG - so you always know which approach to reach for. You'll learn how LoRA adapters work in plain English (yes, the math too), how

Agenda

• • Full fine-tuning vs PEFT vs RAG: when to use what
• • LoRA & QLoRA: the math in plain English
• • Dataset prep: formatting instruction data
• • Fine-tuning LLaMA 3.2 on Google Colab
• • Evaluation & deploying fine-tuned models

Description

Step into the AI-native future of DevOps in this hands-on bootcamp on AIOps, GitOps, and Platform Engineering. Learn how modern engineering teams are using AI-driven operations, GitOps automation, and Internal Developer Platforms (IDPs) to accelerate software delivery and reduce operational complexity. In this live session, you’ll install Backstage from scratch, register microservices into a developer catalog, scaffold a new Node.js service in seconds, and integrate a GenAI-powered plugin to q

Agenda

• 1. The AI-Native DevOps Era
• 2. AIOps in Modern Infrastructure
• 3. GitOps with ArgoCD & Flux CD
• 4. Platform Engineering with Backstage
• 5. Self-Service Infrastructure Demo
• 6. GenAI-Powered Developer Experience
• 7. Live Q&A + Career Guidance

Description

Go from zero to a deployed AI agent in 2 hours. In this live, hands-on bootcamp by Edureka, you'll build a production-ready AI assistant using the Claude Sonnet API, complete with multi-turn memory, real-time web search, MCP server integration, and a FastAPI deployment- all with code you write live. Calling Claude API Live in Python Building an AI Assistant with Memory Giving Claude Real-Time Search Abilities Connecting Claude to Files & GitHub via MCP Shipping Your AI Agent as a REST API Advanc

Agenda

• Live: Call Claude API
• Multi-Turn AI + Memory
• Tool Use: Real-Time Web Search
• MCP Server: Files & GitHub
• Deploy as FastAPI Endpoint
• Prompt Engineering

Description

This hands-on session walks you through fine-tuning Meta's LLaMA 3.2 3B on a custom instruction dataset using QLoRA + Hugging Face PEFT - live on a free Google Colab T4 GPU. No expensive hardware. No black-box magic. Just clean, reproducible code and a real understanding of why it works.We'll break down the core trade-offs between full fine-tuning, LoRA/QLoRA, and RAG - so you always know which approach to reach for. You'll learn how LoRA adapters work in plain English (yes, the math too), how

Agenda

• • Full fine-tuning vs PEFT vs RAG: when to use what
• • LoRA & QLoRA: the math in plain English
• • Dataset prep: formatting instruction data
• • Fine-tuning LLaMA 3.2 on Google Colab
• • Evaluation & deploying fine-tuned models

Webinar Facts

• There will be 11.5 million jobs in the Data Science field by 2026

• A Senior Data Scientist is expected to earn over 20 to 25 Lacs a year

• Data Analytics Market is expected to grow a CAGR of 30.08%

Description

Go from zero to a deployed AI agent in 2 hours. In this live, hands-on bootcamp by Edureka, you'll build a production-ready AI assistant using the Claude Sonnet API, complete with multi-turn memory, real-time web search, MCP server integration, and a FastAPI deployment- all with code you write live. Calling Claude API Live in Python Building an AI Assistant with Memory Giving Claude Real-Time Search Abilities Connecting Claude to Files & GitHub via MCP Shipping Your AI Agent as a REST API Advanc

Agenda

• Live: Call Claude API
• Multi-Turn AI + Memory
• Tool Use: Real-Time Web Search
• MCP Server: Files & GitHub
• Deploy as FastAPI Endpoint
• Prompt Engineering

Webinar Facts

• There will be 11.5 million jobs in the Data Science field by 2026

• A Senior Data Scientist is expected to earn over 20 to 25 Lacs a year

• Data Analytics Market is expected to grow a CAGR of 30.08%

Description

Step into the AI-native future of DevOps in this hands-on bootcamp on AIOps, GitOps, and Platform Engineering. Learn how modern engineering teams are using AI-driven operations, GitOps automation, and Internal Developer Platforms (IDPs) to accelerate software delivery and reduce operational complexity. In this live session, you’ll install Backstage from scratch, register microservices into a developer catalog, scaffold a new Node.js service in seconds, and integrate a GenAI-powered plugin to q

Agenda

• 1. The AI-Native DevOps Era
• 2. AIOps in Modern Infrastructure
• 3. GitOps with ArgoCD & Flux CD
• 4. Platform Engineering with Backstage
• 5. Self-Service Infrastructure Demo
• 6. GenAI-Powered Developer Experience
• 7. Live Q&A + Career Guidance

Webinar Facts

• The DevOps market size is projected to reach $9,407 million by 2023

• ADP, BBC News, eBay, GE, ING, Intuit, PayPal, Splunk, Uber & other MNCs worldwide use DevOps

• The average salary given to a DevOps Engineer is around Rs. 617K per annum - Indeed.com

Description

This hands-on session walks you through fine-tuning Meta's LLaMA 3.2 3B on a custom instruction dataset using QLoRA + Hugging Face PEFT - live on a free Google Colab T4 GPU. No expensive hardware. No black-box magic. Just clean, reproducible code and a real understanding of why it works.We'll break down the core trade-offs between full fine-tuning, LoRA/QLoRA, and RAG - so you always know which approach to reach for. You'll learn how LoRA adapters work in plain English (yes, the math too), how

Agenda

• • Full fine-tuning vs PEFT vs RAG: when to use what
• • LoRA & QLoRA: the math in plain English
• • Dataset prep: formatting instruction data
• • Fine-tuning LLaMA 3.2 on Google Colab
• • Evaluation & deploying fine-tuned models

Webinar Facts

• The value of the global machine learning market is likely to reach USD 117 billion by the end of 2027 at a CAGR of 39%. (GlobeNewswire)

• 65% of companies who are planning to adopt machine learning say the technology helps businesses in decision-making.

• The average salary for a machine learning engineer is $150,099 per year in the United States.- Indeed.com

Description

Go from zero to a deployed AI agent in 2 hours. In this live, hands-on bootcamp by Edureka, you'll build a production-ready AI assistant using the Claude Sonnet API, complete with multi-turn memory, real-time web search, MCP server integration, and a FastAPI deployment- all with code you write live. Calling Claude API Live in Python Building an AI Assistant with Memory Giving Claude Real-Time Search Abilities Connecting Claude to Files & GitHub via MCP Shipping Your AI Agent as a REST API Advanc

Agenda

• Live: Call Claude API
• Multi-Turn AI + Memory
• Tool Use: Real-Time Web Search
• MCP Server: Files & GitHub
• Deploy as FastAPI Endpoint
• Prompt Engineering

Webinar Facts

• The value of the global machine learning market is likely to reach USD 117 billion by the end of 2027 at a CAGR of 39%. (GlobeNewswire)

• 65% of companies who are planning to adopt machine learning say the technology helps businesses in decision-making.

• The average salary for a machine learning engineer is $150,099 per year in the United States.- Indeed.com