Generative AI with MLflow Training
Plan Your IT Career with Experts
Generative AI with MLflow Course – CyberMind IT Solution
Learn the Future of AI – Create, Train, and Manage Intelligent Systems
At CyberMind IT Solution Software Training Institute, our Generative AI with MLflow Course is designed to help you master the tools and techniques behind today’s most advanced AI models.From building intelligent chatbots and image generators to managing machine learning pipelines with MLflow, this course prepares you to lead the AI revolution.
Why Learn Generative AI?
Generative AI is transforming industries — from art and design to healthcare, finance, and automation.
It powers innovations like ChatGPT, DALL·E, and Midjourney, enabling machines to create content, code, and insights that were once possible only for humans.By combining Generative AI and MLflow, you’ll learn not just how to train AI models but how to track, manage, and deploy them efficiently in production.
What You’ll Learn
Our course offers a balanced blend of AI theory, Python-based implementation, and MLflow operations, covering:
Introduction to Generative AI
Understanding Large Language Models (LLMs)
- AI Ethics and Responsible AI Practices
- Machine Learning & Deep Learning Foundations
- Supervised vs. Unsupervised Learning
- Neural Networks and Transformer Architectures
- Generative Models Explained
- Variational Autoencoders (VAEs)
- Generative Adversarial Networks (GANs)
- Diffusion Models and Text-to-Image Generation
- Natural Language Processing (NLP) with Transformers
- Using models like GPT, BERT, and LLaMA
- Building custom conversational AI
- MLflow for Model Tracking and Deployment
- Experiment tracking and parameter management
- Model versioning and lifecycle management
- Integration with TensorFlow, PyTorch, and Hugging Face
- Building End-to-End AI Projects
- Text and Image Generation using OpenAI APIs
- Deploying AI Models on Cloud (AWS / Azure / GCP)
- MLOps Workflow using MLflow
- Capstone Project
- Design and deploy a custom Generative AI model with complete tracking and reporting through MLflow.
Course Features
Hands-on practical sessions and real-world projects
Guided mentorship from certified AI and ML Professionals
Access to cloud environments for live deployments
Interview preparation and placement assistance
Certification recognized by top IT employers
Who Can Enroll
This course is ideal for:
- Developers and Data Scientists
- Machine Learning Enthusiasts
- AI & Automation Engineers
- Students or Professionals from IT, Computer Science, or Data backgrounds
- Anyone passionate about building creative AI solutions
Tools Covered
- Git
- MLflow
- TensorFlow
- PyTorch
- Hugging Face
- OpenAI API
- DALL·E
- GANs
- Docker
- Git
- AWS
- Streamlit
Career Opportunities After Generative AI Training
After mastering Generative AI and MLflow, you can explore roles such as:
- AI Engineer / ML Engineer
- Generative AI Developer
- Data Scientist (AI Specialization)
- MLOps Engineer
- Research Associate – Artificial Intelligence
- AI Product Developer
- Build & Release Engineer
Why CyberMind IT Solution?
At CyberMind IT Solution Software Training Institute, we provide future-ready IT training programs that combine innovation, practical knowledge, and mentorship.Our goal is to empower students with the technical and analytical skills required to succeed in the evolving world of Artificial Intelligence and Cloud Technologies.We focus on real-world projects, AI experimentation, and cloud integration — making you job-ready and project-capable by the end of the course.
Course Outline
Course Duration: 2.5 Months
Sessions :
- Weekdays – 4 per week
- Weekends – 2 per week
Prerequisites :
- There is no such Prerequisites for this course.
- Basic computer knowledge will be advantage.
Mr. Sanjeev Kumar
Qualification: B.Tech (CSE)
Certificate: RHCSA, RHCE, CCNA Certified
Specialisations: Cloud Architecture Specialist
Experience: 14+ Years
Course Contents
Introduction to Generative AI
What is Generative AI?
Types of Generative Models (LLMs, GANs, Diffusion, VAEs)
- Use Cases of Generative AI
- Lab: Explore AI-generated content examples using Hugging Face Datasets
MLFlow Fundamentals
- What is MLflow?
- Components of MLflow: Tracking, Projects, Models, Registry
- MLflow Architecture & Workflow
- Lab: Install MLflow on Linux and launch MLflow Tracking UI
- Lab: Log a basic machine learning model to MLflow
Text Generation using LLMs
- Introduction to Transformers and Hugging Face
- Using Pretrained Models like GPT-2
- Fine-tuning GPT-2 on Custom Text Dataset
- Lab: Run inference using GPT-2 with Hugging Face
- Lab: Fine-tune GPT-2 on Linux command dataset
- Lab: Log training metrics and artifacts to MLflow
Serving Generative Models
- MLflow Model Packaging and Signature
- MLflow Model Registry Workflow
- Lab: Register a model in MLflow
- Lab: Serve a model using mlflow models serve
- Lab: Deploy GPT-2 as a REST API and test with curl
Image Generation using Diffusion Models
- Introduction to Diffusion Models (Stable Diffusion Overview)
- Using Pretrained Diffusers Library
- Lab: Generate images from text using Stable Diffusion
- Lab: Track prompts and images in MLflow
- Lab: Run comparative image generation experiments and track results
Custom Datasets and Prompt Engineering
- What is Prompt Engineering?
- Impact of Prompts on Generated Output
- Creating a Domain-Specific Dataset
- Lab: Prepare a CSV dataset for fine-tuning
- Create multiple prompt styles and evaluate outputs
- Lab: Log datasets and prompt
outputs to MLflow
Containerization and Deployments
- Dockerizing MLflow Models
- Hosting with Streamlit / FastAPI
- Lab:Build Docker image with MLflow model
- Lab:Create a simple Streamlit app for text generation
- Lab:Deploy MLflow model container behind NGINX reverse proxy
Monitoring, Evaluation and Performance
- Metrics to Evaluate Generative Models
- Using MLflow to Track Inference Metrics
- Lab: Log custom metrics like BLEU, ROUGE in MLflow
- Lab: Monitor GPU vs CPU inference times using nvidia-smi
- Lab: Track API response time, error rates, and logs in MLflow
Frequently Asked Questions
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