Corporate Generative AI (GenAI) Training Course

Name: Generative AI (GenAI) Training
Availability: InStock

Edstellar’s Generative AI (GenAI) instructor-led training course equips professionals with the skills to build, fine-tune, and deploy GenAI models, including LLMs. Teams explore advanced AI capabilities for content generation, automation, and virtual assistants to boost innovation and productivity.

Duration

24 - 32 hrs

Delivery Type

Instructor-led Group Training
(Virtual / On-site / Off-site)

Training Available in

10 Languages

Multiple Locations

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About Learning Outcomes Key Benefits Course Outline Target Audience Training Modes Certificate Trainers Request a Training Quote

Drive Team Excellence with Generative AI (GenAI) Corporate Training

Generative AI (GenAI) is a specialized branch of artificial intelligence focused on developing models that generate new content, such as text, images, audio, or code, that closely resembles human-created output. As this technology advances, it is rapidly becoming a strategic asset for organizations looking to enhance operational speed, personalization, and intelligence. By adopting GenAI, businesses can automate time-consuming tasks like report writing, customer communication, document summarization, and creative content generation, unlocking significant efficiency gains. A structured Generative AI (GenAI) training program helps teams build tailored AI solutions and use the outputs confidently to support business goals.

The Generative AI (GenAI) instructor-led training course provided by Edstellar can be customized to meet team requirements. The virtual/onsite Generative AI (GenAI) training course, led by expert trainers, enables professionals to prototype ideas faster, make smarter decisions, and deliver improved user experiences.

Get Customized Expert-led Training for Your Teams

Customized Training Delivery

Scale Your Training: Small to Large Teams

In-person Onsite, Live Virtual or Hybrid Training Modes

Plan from 2000+ Industry-ready Training Programs

Experience Hands-On Learning from Industry Experts

Delivery Capability Across 100+ Countries & 10+ Languages

Skills Your Employees Will Gain

These are the core, hands-on capabilities your team builds during the program.

Model Analysis
Model Analysis is the process of evaluating and interpreting predictive models to ensure accuracy and reliability. this skill is important for data scientists and analysts, as it enables informed decision-making and enhances model performance.
Ethical Evaluation
Ethical Evaluation is the ability to assess situations and decisions based on moral principles. This skill is important for roles in management, law, and healthcare to ensure integrity and accountability.
Pipeline Design
Pipeline Design involves creating efficient systems for transporting fluids, gases, or solids. this skill is important for engineers and project managers to ensure safety, efficiency, and compliance.
Performance Optimization
Performance Optimization is the process of enhancing system efficiency and effectiveness. This skill is important for roles in IT, engineering, and data analysis to ensure peak productivity.
Text Generation
Text Generation is the ability to create coherent and contextually relevant written content using algorithms. This skill is important for roles in marketing, content creation, and AI development, as it enhances communication and engagement.
Output Evaluation
Output Evaluation is the ability to assess and analyze the quality and effectiveness of deliverables. this skill is important for roles in quality assurance, project management, and data analysis, ensuring high standards and successful outcomes.

What Your Team Will Achieve After This Training

Understand the architecture and functioning of Generative AI models, including transformers and large language models
Analyze and apply scaling laws, optimization strategies, and fine-tuning techniques for GenAI models
Design and implement data pipelines to train and fine-tune generative models on domain-specific datasets
Generate content including text, summaries, translations, and conversational interactions using GenAI techniques
Evaluate GenAI model performance using both quantitative and qualitative metrics
Integrate GenAI capabilities into business applications such as virtual assistants, document automation, and marketing tools
Identify and address ethical considerations, data biases, and responsible usage of generative technologies
Explore emerging trends such as multimodal models, retrieval-augmented generation, and instruction tuning
Troubleshoot common development and deployment issues to ensure smooth operation in real-world scenarios

Topics & Program Outline

The curriculum is organized into focused modules built by industry experts and delivered virtually or on-premise. Interactive sessions reflect the evolving demands of the workplace, keeping the learning both relevant and practical.

Introduction to Generative AI (GenAI)

Overview of GenAI and its evolution

What is Generative AI (GenAI)?
Evolution from traditional AI to GenAI
Milestones in generative modeling (GANs, Transformers, LLMs)

Key applications across industries

GenAI in content generation (text, image, code)
Use cases in healthcare, finance, retail, and marketing
Enterprise adoption and innovation drivers

Role of GenAI in Natural Language Processing (NLP)

NLP vs. GenAI
NLP tasks enhanced by GenAI (e.g., summarization, Q&A)
Language understanding vs. generation

Introduction to Deep Learning Foundations

Deep learning vs. traditional ML
Key concepts: layers, activation functions, loss functions
Why deep learning matters for GenAI

Neural networks and architectural basics

Types of neural networks: CNN, RNN, MLP
Role of feedforward and recurrent networks
Limitations and rise of Transformer-based models

Understanding the rise of GenAI models

Why LLMs changed the game
Role of big data and computing power
From rule-based systems to self-learning models

Language Models and Transformer Architecture

Fundamentals of transformer architecture

Sequence modeling challenges
Encoder-decoder structure
Positional encoding and scalability

Mechanisms: Attention, Self-Attention, and Multi-Head Attention

The core idea of the attention mechanism
Self-attention for context capture
Multi-head attention for diverse pattern learning

Introduction to Large Language Models (LLMs) in GenAI

What qualifies as an LLM?
LLM lifecycle: pre-training, fine-tuning, inference
Common frameworks and platforms

Prominent LLMs: GPT, BERT, and beyond

GPT: autoregressive transformers
BERT: masked language modeling
Comparison with T5, LLaMA, PaLM, Claude, Gemini, etc.

Components and architecture of LLMs in GenAI systems

Tokenization and embedding layers
Transformer blocks, feedforward layers, output heads
Parameter scaling, context windows, and model depth

Scaling GenAI Models and Optimizing Performance

Scaling Laws in Deep Learning

Scaling laws: data, compute, and model size
Emergent abilities with size
Diminishing returns and limits

Model Size vs. Performance Insights

Trade-offs between small, medium, and large models
Performance metrics and benchmarks
Role of model compression and quantization

Training implications for Large GenAI models

Resource requirements: GPUs/TPUs, memory, time
Handling large datasets
Distributed training and data parallelism

Optimization techniques: backpropagation, gradient descent

Core principles of training
Loss function selection
Challenges in LLM optimization

Learning rate adaptation and fine-tuning best practices

Learning rate schedules and warmups
Transfer learning vs. zero-shot and few-shot
Fine-tuning on custom datasets

Building and Fine-Tuning GenAI Models

Preprocessing data for GenAI applications

Text cleaning, normalization, and deduplication
Importance of quality over quantity
Metadata tagging and formatting

Tokenization strategies and dataset augmentation

Byte Pair Encoding (BPE), WordPiece, SentencePiece
Handling multilingual/multimodal inputs
Augmenting low-resource datasets

Training approaches: Pre-training vs. Fine-Tuning

Objectives of pre-training
Domain adaptation via fine-tuning
Parameter-efficient fine-tuning (LoRA, adapters)

Leveraging transfer learning for domain-specific use cases

Benefits of transfer learning
Case studies in legal, medical, and finance domains
Prompt tuning and instruction tuning

Deploying GenAI Solutions

Infrastructure and hardware considerations

On-premise vs. cloud vs. edge deployment
Choosing the right hardware (GPU, TPU)
Cost optimization for inference

Integrating GenAI with enterprise systems

API access and microservice deployment
Real-time vs. batch inference
Versioning, monitoring, and model rollback

Real-world applications

Text generation and summarization
Language translation
Conversational AI (Chatbots and Assistants)

Ethics, Risks, and the Future of GenAI

Ethical implications of GenAI adoption

Deepfakes and misinformation risks
AI-generated content disclosure
Accountability and decision ownership

Bias, fairness, and responsible GenAI development

Understanding training data bias
Fairness-aware modeling
Mitigation strategies (e.g., debiasing techniques)

Data privacy and societal concerns

GDPR and data usage in GenAI
Prompt injection and data leakage
Anonymization techniques

Emerging research directions

Multimodal GenAI (text + image/audio)
Retrieval-Augmented Generation (RAG)
Self-improving and open-weight models

Industry trends and future applications

Rise of agentic AI systems
GenAI for software development (code generation)
AI copilots and productivity boosters in the enterprise

Who Should Attend?

This program suits professionals at many levels across the organization, including:

AI Researchers
Machine Learning Engineers
Data Scientists
NLP Specialists
Software Engineers
Systems Analysts
Technical Leads
IT Managers
Business Analysts
AI Developers
Generative AI Specialists
Language Model Engineers

What are the Prerequisites?

Professionals with a basic understanding of Python programming, data manipulation, and foundational concepts in machine learning, neural networks, and natural language processing can take up the Generative AI (GenAI) training course.

Request a Quote for your Corporate Training Requirements

Delivering Training for Organizations across 100 Countries and 10+ Languages

Choose the Format That Fits Your Team

We design training your teams actually engage with, and deliver it the way that suits you best. Through a vetted global trainer network, Edstellar runs sessions in 10+ languages with consistent quality anywhere.

Virtual / online: expert-led live sessions delivered anywhere, with consistency and easy scheduling.

We deliver anywhere worldwide

Standardized content for consistent outcomes

Join from own workspace, no travel

We scale to large groups across sites

Interactive tools keep remote learners engaged

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On-site (in-house): immersive, instructor-led learning at your office.

Our trainers run face-to-face at your office

We tailor setup/content to your workplace and tools

Group exercises drive collaboration

Live demos + hands-on practice

Direct trainer access to clarify doubts

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Off-site: focused, instructor-led group learning away from everyday workplace distractions.

We host your teams at a venue of your preferred choice

Built-in group activities for bonding

Full uninterrupted schedule for focus/retention

Boosts morale and signals commitment

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What Sets Edstellar Apart

Experienced Trainers

Our trainers are drawn from a vetted global network and bring years of industry expertise, keeping every session practical and impactful.

Proven Quality

With a strong global track record, Edstellar is known for quality and engaging delivery.

Industry-Relevant Curriculum

Our programs are built by experts to match the demands of today's industry.

Fully Customizable

Every program can be tailored to your organization's goals.

Comprehensive Support

We provide pre- and post-session support for a complete learning experience.

Global Multi-Location & Multilingual Training Delivery

We deliver in multiple languages to support diverse global teams.

Hear from Organizations We've Trained

"The Generative AI (GenAI) training exceeded my expectations in every way. As a Senior Machine Learning Engineer, I gained comprehensive knowledge of industry best practices that transformed my approach to strategic incredibly practical and immediately applicable. I've been able to drive meaningful innovation and improvement within my department. The instructor's expertise in interactive labs made complex concepts crystal clear and actionable.”

Douglas Knight

Senior Machine Learning Engineer,

AI-Powered Automation Company

"This Generative AI (GenAI) course transformed my approach to strategic implementation solutions. The comprehensive modules on expert-led workshops were invaluable for our professional services projects. I can now confidently implement for diverse client requirements. The deep coverage of hands-on exercises gave me advanced skills I immediately applied to We've successfully expanded our service portfolio based on these enhanced capabilities.”

Zhao Dai

Principal Data Analyst,

Artificial Intelligence Platform Provider

"As a Senior Database Administrator leading technical mastery operations, the Generative AI (GenAI) training provided our team with essential practical applications expertise at scale. The comprehensive modules on across our complete operational footprint. We've successfully deployed these methodologies across all regional operations centers. This course has proven invaluable for driving our organizational transformation and sustained excellence.”

Hamza Said

Senior Database Administrator,

Intelligent Systems Provider

“Edstellar’s IT & Technical training programs have been instrumental in strengthening our engineering teams and building future-ready capabilities. The hands-on approach, practical cloud scenarios, and expert guidance helped our teams improve technical depth, problem-solving skills, and execution across multiple projects. We’re excited to extend more of these impactful programs to other business units.”

Aditi Rao

L&D Head,

A Global Technology Company

Recognition That Motivates Your Team

Upon successful completion of the training course offered by Edstellar, employees receive a course completion certificate, symbolizing their dedication to ongoing learning and professional development.

This certificate validates the employee's acquired skills and is a powerful motivator, inspiring them to enhance their expertise further and contribute effectively to organizational success.

Corporate Generative AI (GenAI) Training Course

Drive Team Excellence with Generative AI (GenAI) Corporate Training

Skills Your Employees Will Gain

What Your Team Will Achieve After This Training

Topics & Program Outline

Introduction to Generative AI (GenAI)

Language Models and Transformer Architecture

Scaling GenAI Models and Optimizing Performance

Building and Fine-Tuning GenAI Models

Deploying GenAI Solutions

Ethics, Risks, and the Future of GenAI

Who Should Attend?

What are the Prerequisites?

Choose the Format That Fits Your Team

Get a Proposal Shaped to Your Needs

What Sets Edstellar Apart

Experienced Trainers

Proven Quality

Industry-Relevant Curriculum

Fully Customizable

Comprehensive Support

Global Multi-Location & Multilingual Training Delivery

Hear from Organizations We've Trained

Recognition That Motivates Your Team

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