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Advance Your Professional Edge with Generative AI Training

Advance Your Professional Edge with Generative AI

Advance your tech career with comprehensive Generative AI training led by seasoned industry experts. Our instructors are highly skilled in cutting-edge AI frameworks and bring deep, hands-on experience building real-world AI solutions used by leading global organizations.

With practical labs, real-world AI projects, and step-by-step professional guidance, you’ll gain the exact expertise needed to master Large Language Models (LLMs), advanced prompt engineering, neural networks, and AI-driven automation.

Start your journey with engineering professionals who develop and work with these technologies every day get certified, get specialized, and move ahead in the rapidly evolving world of Artificial Intelligence.

Bestseller

4.5
  • Vendor Neutral
  • English
  • 4 Months

Classes are held two hours long and twice a week

  • Available Timings (Monday to Friday) 6PM - 10PM
  • Available Timings (Saturday) 10AM - 10PM​
  • Available Timings (Sunday ) 10AM - 4PM​
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Revolutionize Innovation with Generative AI Expertise

Mastering Generative AI Architectures & Solutions

The Generative AI Certification Exam is a 120-minute comprehensive assessment designed to validate your advanced technical knowledge and practical skills across critical artificial intelligence domains. This exam covers:

  • Introduction to Generative AI (Core concepts of deep learning, neural network architectures, and the evolution of foundation models)
  • Planning and Preparing for Generative AI Projects (Defining project scope, data pipelines, data preprocessing, and ethical AI governance framework selection)
  • Model Development (Building, training, and fine-tuning open-source models using advanced optimization techniques)
  • Language Models  (Mastering transformer architectures, prompt engineering, tokenization, and context window management)
  • Multimedia Generation (Deploying state-of-the-art diffusion models for advanced synthetic data, image, and video generation) 
  • Integration of Generative AI (Integrating models into production via REST APIs, microservices, and secure enterprise cloud architectures)

Generative AI exam details

Price: $US300 paid directly to Artificial Intelligence. 
Duration: 120 minutes
Languages: English.
Results: Pass/fail results are typically available online within 48 hours.  
Recertification: This exam can be used toward recertification requirements. 
Question Types: Expect a variety of formats, including performance-based questions, multiple choice, and drag-and-drop.

1.1 Overview of Generative AI

  • Definition and key concepts

  • Types of generative models (GANs, VAEs, Transformers)

  • Applications: text, images, audio, code, simulations

  • Ethical considerations and AI safety

1.2 Core Technologies

  • Neural networks and deep learning fundamentals

  • Transformer architecture and attention mechanisms

  • Large language models (LLMs)

  • Diffusion models and image generation

1.3 Generative AI Platforms & Tools

  • OpenAI API, ChatGPT, DALL·E

  • Google Bard, Vertex AI, PaLM

  • Hugging Face Models and Transformers

  • Local deployment frameworks (PyTorch, TensorFlow)

2.1 Project Scoping & Strategy

  • Identifying use cases and objectives

  • Feasibility assessment and ROI considerations

  • Dataset requirements and sources

2.2 Data Management & Preparation

  • Data collection and cleaning

  • Preprocessing for text, images, and multimodal datasets

  • Data augmentation techniques

2.3 AI Governance & Ethics

  • AI model bias and fairness

  • Privacy and data protection regulations

  • Responsible AI policies

 

3.1 Model Selection & Architecture

  • Choosing between GANs, VAEs, and Transformers

  • Fine-tuning vs training from scratch

  • Model size and compute considerations

3.2 Training & Optimization

  • Training pipelines and frameworks

  • Hyperparameter tuning

  • Evaluation metrics for generative models

3.3 Model Management

  • Versioning and reproducibility

  • Model monitoring and lifecycle management

  • Continuous learning and updates

4.1 LLM Fundamentals

  • Tokenization and embeddings

  • Prompt engineering techniques

  • Fine-tuning LLMs for domain-specific tasks

4.2 Practical Applications

  • Chatbots and conversational AI

  • Content generation (articles, code, reports)

  • Summarization, translation, and Q&A systems

4.3 Policies & Safety

  • Moderation and safety filters

  • Handling hallucinations and misinformation

  • Responsible use of AI-generated content

 

5.1 Image Generation

  • Diffusion models and GANs for images

  • Text-to-image generation and prompts

  • Style transfer and creative AI tools

5.2 Audio & Music Generation

  • Speech synthesis and voice cloning

  • Music composition using AI models

  • Audio data preprocessing

5.3 Video & Multimodal AI

  • AI-generated video basics

  • Combining text, image, and audio for multimedia outputs

  • Multimodal applications and tools

6.1 Deployment Strategies

  • Cloud deployment (Azure, AWS, GCP)

  • On-premise vs hybrid AI models

  • APIs and model serving

6.2 Application Integration

  • Embedding AI in web and mobile apps

  • Chatbots, assistants, and interactive tools

  • Automation of business workflows

6.3 Performance Monitoring

  • Real-time monitoring of AI outputs

  • User feedback integration

  • Continuous model improvements

7.1 Reinforcement Learning with AI

  • RLHF (Reinforcement Learning from Human Feedback)

  • Reward models and fine-tuning

  • Applications in alignment and AI safety

7.2 Explainability & Interpretability

  • Understanding model decisions

  • Visualization techniques

  • Trustworthy AI

7.3 Cutting-edge Trends

  • Foundation models and multimodal AI

  • Self-supervised learning

  • Future directions and research areas

8.1 Model Evaluation & Metrics

  • Evaluating text, image, and audio outputs

  • Precision, recall, FID, BLEU, ROUGE metrics

8.2 Troubleshooting AI Systems

  • Debugging model outputs

  • Handling low-quality or biased results

  • Fine-tuning and retraining strategies

8.3 Governance & Compliance

  • Ethical review of outputs

  • Regulatory compliance for AI deployment

  • Best practices for enterprise AI adoption

Generative AI Professional Pathways & Career Tracks

Generative AI Engineer

A Generative AI Engineer demonstrates deep proficiency in cutting-edge AI frameworks, programming languages, and advanced data science techniques to build and deploy production-ready models

Generative AI Scientist

A Generative AI Scientist brings a strong academic background in artificial intelligence, a deep understanding of machine learning theory, and proven research experience to pioneer next-generation models

Generative AI Strategist

A Generative AI Strategist combines sharp business acumen and strategic thinking with deep knowledge of generative AI technologies to design scalable potential applications for modern enterprises

Alumni testimonials

The Generative AI course at HMT WAY exceeded my expectations. The curriculum was thorough and the instructors were experts in the field. I feel well-prepared to tackle AI projects in my organization.
Azohani Bernal
Azohani BernalAI Researcher
After completing the Strategic AI course, I can now implement AI strategies that drive business growth for my clients. The course content was comprehensive and very relevant to today’s market.
Mia Clark
Mia ClarkIT Consultant