Drive Team Excellence with Generative AI (GenAI) Corporate Training

Empower your teams with expert-led on-site/in-house or virtual/online Generative AI (GenAI) Training through Edstellar, a premier Generative AI (GenAI) training company for organizations globally. Our customized training program equips your employees with the skills, knowledge, and cutting-edge tools needed for success. Designed to meet your specific training needs, this Generative AI (GenAI) group training program ensures your team is primed to drive your business goals. Transform your workforce into a beacon of productivity and efficiency.

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.

Key Skills Employees Gain from Generative AI (GenAI) Training

Generative AI (GenAI) skills corporate training will enable teams to effectively apply their learnings at work.

  • 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.

Key Learning Outcomes of Generative AI (GenAI) Training Workshop for Employees

Edstellar’s Generative AI (GenAI) training for employees will not only help your teams to acquire fundamental skills but also attain invaluable learning outcomes, enhancing their proficiency and enabling application of knowledge in a professional environment. By completing our Generative AI (GenAI) workshop, teams will to master essential Generative AI (GenAI) and also focus on introducing key concepts and principles related to Generative AI (GenAI) at work.


Employees who complete Generative AI (GenAI) training will be able to:

  • 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

Key Benefits of the Generative AI (GenAI) Group Training

Attending our Generative AI (GenAI) classes tailored for corporations offers numerous advantages. Through our Generative AI (GenAI) group training classes, participants will gain confidence and comprehensive insights, enhance their skills, and gain a deeper understanding of Generative AI (GenAI).

  • Equips teams with foundational and advanced skills in using GenAI tools and models, including LLMs and transformer-based architectures
  • Empowers professionals to generate and fine-tune content text, code, images using AI to support diverse business functions
  • Enhances productivity by automating routine tasks such as reporting, customer responses, and document summarization
  • Helps teams develop intelligent systems like virtual assistants, personalized content engines, and recommendation tools
  • Enables innovation by exposing professionals to the latest trends in GenAI, including multimodal AI, RAG, and instruction tuning
  • Builds awareness around ethical and responsible use of generative technologies
  • Strengthens cross-functional collaboration by bridging technical and domain expertise using AI

Topics and Outline of Generative AI (GenAI) Training

Our virtual and on-premise Generative AI (GenAI) training curriculum is divided into multiple modules designed by industry experts. This Generative AI (GenAI) training for organizations provides an interactive learning experience focused on the dynamic demands of the field, making it relevant and practical.

  1. Overview of GenAI and its evolution
    • What is Generative AI (GenAI)?
    • Evolution from traditional AI to GenAI
    • Milestones in generative modeling (GANs, Transformers, LLMs)
  2. Key applications across industries
    • GenAI in content generation (text, image, code)
    • Use cases in healthcare, finance, retail, and marketing
    • Enterprise adoption and innovation drivers
  3. 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
  4. Introduction to Deep Learning Foundations
    • Deep learning vs. traditional ML
    • Key concepts: layers, activation functions, loss functions
    • Why deep learning matters for GenAI
  5. 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
  6. 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
  1. Fundamentals of transformer architecture
    • Sequence modeling challenges
    • Encoder-decoder structure
    • Positional encoding and scalability
  2. 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
  3. Introduction to Large Language Models (LLMs) in GenAI
    • What qualifies as an LLM?
    • LLM lifecycle: pre-training, fine-tuning, inference
    • Common frameworks and platforms
  4. Prominent LLMs: GPT, BERT, and beyond
    • GPT: autoregressive transformers
    • BERT: masked language modeling
    • Comparison with T5, LLaMA, PaLM, Claude, Gemini, etc.
  5. 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
  1. Scaling Laws in Deep Learning
    • Scaling laws: data, compute, and model size
    • Emergent abilities with size
    • Diminishing returns and limits
  2. Model Size vs. Performance Insights
    • Trade-offs between small, medium, and large models
    • Performance metrics and benchmarks
    • Role of model compression and quantization
  3. Training implications for Large GenAI models
    • Resource requirements: GPUs/TPUs, memory, time
    • Handling large datasets
    • Distributed training and data parallelism
  4. Optimization techniques: backpropagation, gradient descent
    • Core principles of training
    • Loss function selection
    • Challenges in LLM optimization
  5. 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
  1. Preprocessing data for GenAI applications
    • Text cleaning, normalization, and deduplication
    • Importance of quality over quantity
    • Metadata tagging and formatting
  2. Tokenization strategies and dataset augmentation
    • Byte Pair Encoding (BPE), WordPiece, SentencePiece
    • Handling multilingual/multimodal inputs
    • Augmenting low-resource datasets
  3. Training approaches: Pre-training vs. Fine-Tuning
    • Objectives of pre-training
    • Domain adaptation via fine-tuning
    • Parameter-efficient fine-tuning (LoRA, adapters)
  4. 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
  1. Infrastructure and hardware considerations
    • On-premise vs. cloud vs. edge deployment
    • Choosing the right hardware (GPU, TPU)
    • Cost optimization for inference
  2. Integrating GenAI with enterprise systems
    • API access and microservice deployment
    • Real-time vs. batch inference
    • Versioning, monitoring, and model rollback
  3. Real-world applications
    • Text generation and summarization
    • Language translation
    • Conversational AI (Chatbots and Assistants)
  1. Ethical implications of GenAI adoption
    • Deepfakes and misinformation risks
    • AI-generated content disclosure
    • Accountability and decision ownership
  2. Bias, fairness, and responsible GenAI development
    • Understanding training data bias
    • Fairness-aware modeling
    • Mitigation strategies (e.g., debiasing techniques)
  3. Data privacy and societal concerns
    • GDPR and data usage in GenAI
    • Prompt injection and data leakage
    • Anonymization techniques
  4. Emerging research directions
    • Multimodal GenAI (text + image/audio)
    • Retrieval-Augmented Generation (RAG)
    • Self-improving and open-weight models
  5. 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 Can Take the Generative AI (GenAI) Training Course

The Generative AI (GenAI) training program can also be taken by professionals at various levels in the organization.

  • 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

Prerequisites for Generative AI (GenAI) Training

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.

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Corporate Group Training Delivery Modes
for Generative AI (GenAI) Training

At Edstellar, we understand the importance of impactful and engaging training for employees. As a leading Generative AI (GenAI) training provider, we ensure the training is more interactive by offering Face-to-Face onsite/in-house or virtual/online sessions for companies. This approach has proven to be effective, outcome-oriented, and produces a well-rounded training experience for your teams.

 Virtual trainig

Edstellar's Generative AI (GenAI) virtual/online training sessions bring expert-led, high-quality training to your teams anywhere, ensuring consistency and seamless integration into their schedules.

With global reach, your employees can get trained from various locations
The consistent training quality ensures uniform learning outcomes
Participants can attend training in their own space without the need for traveling
Organizations can scale learning by accommodating large groups of participants
Interactive tools can be used to enhance learning engagement
 On-site trainig

Edstellar's Generative AI (GenAI) inhouse training delivers immersive and insightful learning experiences right in the comfort of your office.

Higher engagement and better learning experience through face-to-face interaction
Workplace environment can be tailored to learning requirements
Team collaboration and knowledge sharing improves training effectiveness
Demonstration of processes for hands-on learning and better understanding
Participants can get their doubts clarified and gain valuable insights through direct interaction
 Off-site trainig

Edstellar's Generative AI (GenAI) offsite group training offer a unique opportunity for teams to immerse themselves in focused and dynamic learning environments away from their usual workplace distractions.

Distraction-free environment improves learning engagement
Team bonding can be improved through activities
Dedicated schedule for training away from office set up can improve learning effectiveness
Boosts employee morale and reflects organization's commitment to employee development

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Generative AI (GenAI) Corporate Training

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        Get Your Team Members Recognized with Edstellar’s Course Certificate

        Upon successful completion of the Generative AI (GenAI) 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.

        Edstellar Certificate Template

        We have Expert Trainers to Meet Your Generative AI (GenAI) Training Needs

        The instructor-led training is conducted by certified trainers with extensive expertise in the field. Participants will benefit from the instructor's vast knowledge, gaining valuable insights and practical skills essential for success in Access practices.

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