AIML Full Stack Development

Generative AI Full Stack Pro. ( XER – 102 )

Generative AI Full Stack Pro.. END-TO-END DEVELOPMENT (XER - 102 )

LEVEL - INTERMEDIATE   | FORMAT - INSTRUCTOR  LED TRAINING | Days : 5 Days

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Prerequisites

  • Proficiency in Python (required for AIML Development).
  • Understanding of Supervised and Unsupervised Learning.
  • Experience with data preprocessing (cleaning, normalizing and transforming data).
  • Familiarity with Deep Learning Concepts.

Objectives

  • Generative AI, GANs, VAEs and Transformers.
  • Backend AI model with real-time facing frontends stream.
  • Docker, Kubernetes and Cloud Services.
  • Bias and Compliance with Global Standard.
  • real-world projects text, image and video generation.
  • Securing the model - DevSecOps.

Datasheet

Generative AI - XER 102

Description

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Training Objectives

  • Learn to develop, train, and optimize Generative AI models using modern framework using OpenAI etc
  • Build end-to-end generative AI applications, including both backend model training/inference and frontend deployment with real-time user interaction using streamlit.
  • Understand the full cycle of ML model lifecycle management, including versioning, monitoring, and orchestration using MLFlow.
  • Implement real-world use cases, including text generation, image creation, and video synthesis, integrating these solutions into a full-stack environment.
  • Leverage the Orchestration Platforms like MLflow, Monitor, Scale, CI/CD in Production Environment.

Target Audience

  • AIML Engineer/Software Developers.
  • Data Scientist/Tech Entrepreneur.
  • IT Professional/Cloud Engineer/Cloud Developer.
  • Cyber Security Expert with Programming Knowledge.
  • Academics/Researchers/Student Graduates.

Course Module

  1. Generative AI Overview.
  2. GANs, VAEs and Transformers.
  3. The art of Crafting Prompts.
  4. Prompt with LangChain.
  5. Pre-Training Large Language Models
  6. Git/DVC/Github.
  7. Structured, Unstructured and Semi Structured Data.
  8. LLM & Graph Integration for Info Retrieval.
  9. RAG Architecture and Components.
  10. Model Orchestration using MLFlow.
  11. Docker and Kubernetes.
  12. Data Modelling for VectorDB and MongoDB.
  13. Front End Design - Django Framework.
  14. Cloud Deployments - AWS.
  15. DevOps - Github Action.
  16. Security Scan - Trivy and SonarQube.
  17. Overview of DevSecOps.
  18. What is next in your training and certification Journey.

 

Scope 

  • Level - Intermediate
  • Duration : 5 Days
  • Format : Lecture and Hands-On Lab
  • Platform Support : On-Prime Data Center / Cloud Platform
  • Programming Language : Python Programming

Lab Requirements

  • Cloud Platform - AWS Services - S3, EKS, RDS, EC2
  • Windows OS
  • OpenAI - Access
  • Github Account
  • Models : PaLM, GPT-3.5, GPT-4, DALL-E, Codey, Imagen, Gemini.

Contact Us

  • WhatsApp : +919164315460
  • Email : info@xerxez.in