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Master in Strategic Generative AI Engineering

Name: Master in Strategic Generative AI Engineering
Rating: 4.5 (508 reviews)

Become Gen AI Engineer through real-world assignments covering AI solution design to deployment strategies

Created byArun Singhal B-Tech MBA With 45+ Exp with Unilever, J&J, Danone, etc.

Last updated 7/2026

English

English [Auto],French [Auto],

What you'll learn

Design end-to-end Generative AI solutions by selecting the right models, architectures and engineering approaches for real business problems.
Develop practical Generative AI engineering skills through real-world assignments covering solution design, model training, optimization, deployment and integra
Evaluate, train and optimize Generative AI models using proven engineering frameworks, performance metrics and implementation strategies.
Build deployment, API integration and governance strategies that enable Generative AI solutions to operate successfully in production environments
Apply ethical AI principles, risk management and responsible AI governance throughout the complete Generative AI engineering lifecycle.
Present Generative AI solutions confidently to business stakeholders by developing implementation roadmaps, business proposals and transformation strategies.

Course content

14 sections • 99 lectures • 10h 2m total length

Introduction4:48
Introduction to the course and instructor
Your First Strategic Generative AI Engineering Challenge
Introduction Case Study11:09
A case study of becoming an Excellent Generative AI Engineer

Overview I- What is Generative AI?6:12
The purpose of this section is to give you an overview of Generative AI
At the end of this lecture, you will learn the following
•What is Generative AI?
Overview I
Overview II- What is role of Generative AI Engineer?6:23
The purpose of this section is to give you an overview of Generative AI
At the end of this lecture, you will learn the following
•What is role of Generative AI Engineer?
Overview II
Overview III- An example of Generative AI5:14
At the end of this lecture, you will learn the following
•An example of Generative AI
Overview IV- How to become a Generative AI Engineer?7:00
At the end of this lecture, you will learn the following
•How to become a Generative AI Engineer?
Overview IV
Analyze a Real-World Generative AI Application

Gen AI Models I- Generative Adversarial Networks (GANs)7:21
At the end of this lecture, you will learn the following
•Generative Adversarial Networks (GANs)
Gen AI Models I
Gen AI Model Examples I- Generative Adversarial Networks (GANs) Example4:28
Generative Adversarial Networks (GANs) Example
Gen AI Models II- Variational Autoencoders (VAEs)8:39
At the end of this lecture, you will learn the following
•Variational Autoencoders (VAEs)
Gen AI Models II
Gen AI Models Examples II- Variational Autoencoders (VAEs) Example8:22
Variational Autoencoders (VAEs) Example
Gen AI Models III- Transformer Models14:34
The purpose of this section is to give you an in-depth view of Generative AI Models
At the end of this lecture, you will learn the following
•Transformer Models
Gen AI Models III
Gen AI Model Examples III- Transformer Models Example5:43
Transformer Models Example
Select the Best Generative AI Model for Business Applications

Designing Models I- How to create and develop new generative models3:53
At the end of this lecture, you will learn the following
•How to create and develop new generative models tailored to specific applications, such as natural language processing (NLP), image generation, music composition, or other creative tasks
Designing Models I
Designing Models II- Gather Domain Knowledge4:51
At the end of this lecture, you will learn the following
How to create and develop new generative models tailored to specific applications, such as natural language processing (NLP), image generation, music composition, or other creative tasks
•Gather Domain Knowledge
Designing Models II
Gather Domain Knowledge- Domain-Specific Strategies: Natural Language Processing5:37
At the end of this lecture, you will learn the following
Gather Domain Knowledge
•Domain-Specific Strategies: Natural Language Processing (NLP)
Designing Models III
Gather Domain Knowledge •Domain-Specific Strategies: Image Generation6:38
At the end of this lecture, you will learn the following
Gather Domain Knowledge
•Domain-Specific Strategies: Image Generation
Designing Models IV
Gather Domain Knowledge •Work with image datasets like CIFAR-10, ImageNet6:11
At the end of this lecture, you will learn the following
Gather Domain Knowledge
•Work with image datasets like CIFAR-10, ImageNet
Designing Models V
Gather Domain Knowledge •Domain-Specific Strategies: Music Composition5:13
At the end of this lecture, you will learn the following
Gather Domain Knowledge
•Domain-Specific Strategies: Music Composition
Designing Models VI
How to tokenize text, remove stop words, and handle special characters5:04
At the end of this lecture, you will learn the following
Data Collection and Preprocessing
•How to tokenize text, remove stop words, and handle special characters
Designing Models VII
How to resize, normalize, and augment images3:18
At the end of this lecture, you will learn the following
Data Collection and Preprocessing
•How to resize, normalize, and augment images
Designing Models VIII
How to convert audio to a suitable format (e.g., MIDI), segment into phrases or4:39
At the end of this lecture, you will learn the following
Data Collection and Preprocessing
•How to convert audio to a suitable format (e.g., MIDI), segment into phrases or bars
Designing Models IX
Choose the Appropriate Model Architecture •Transformer Models11:47
At the end of this lecture, you will learn the following
Choose the Appropriate Model Architecture
•Transformer Models (e.g., GPT-3, BERT)
Designing Models X
Choose the Appropriate Model Architecture •Models like RNNs and LSTMs8:04
At the end of this lecture, you will learn the following
Choose the Appropriate Model Architecture
•Models like RNNs and LSTMs
Designing Models XI
Choose the Appropriate Model Architecture •Image Generation using GANs6:38
At the end of this lecture, you will learn the following
Choose the Appropriate Model Architecture
•Image Generation using GANs
Designing Models XII
Choose the Appropriate Model Architecture •Image Generation using VAEs6:19
At the end of this lecture, you will learn the following
Choose the Appropriate Model Architecture
•Image Generation using Variational Autoencoders (VAEs)
Designing Models XIII
Choose the Appropriate Model Architecture •Music Composition12:38
At the end of this lecture, you will learn the following
Choose the Appropriate Model Architecture
•Music Composition
Designing Models XIV
How to choose the Appropriate Model Architecture •Autoencoders - Creative tasks6:24
At the end of this lecture, you will learn the following
How to choose the Appropriate Model Architecture
•Autoencoders for other creative tasks
Designing Models XV
Diffusion Models for other creative tasks7:27
At the end of this lecture, you will learn the following
•Diffusion Models for other creative tasks
Designing Models XVI
Model Implementation5:54
At the end of this lecture, you will learn the following
•Model Implementation
Designing Models XVII
Design the Model Architecture6:28
At the end of this lecture, you will learn the following
Model Implementation
•Design the Model Architecture
Designing Models XVIII
Techniques for Determining Layers and Units9:18
At the end of this lecture, you will learn the following
Model Implementation
•Design the Model Architecture
Techniques for Determining Layers and Units
Designing Models XIX
How to choose activation functions9:08
At the end of this lecture, you will learn the following
Model Implementation
•Design the Model Architecture
How to choose activation functions
Designing Models XXI ( Designing Models XX intentionally missed)
Model Implementation •Design the Model Architecture Normalization6:20
At the end of this lecture, you will learn the following
Model Implementation
•Design the Model Architecture
Normalization
Designing Models XXII
Model Implementation •Design the Model Architecture Regularization4:16
At the end of this lecture, you will learn the following
Model Implementation
•Design the Model Architecture
Regularization
Designing Models XXIII
Model Implementation •Tailor the Model to the Task5:42
At the end of this lecture, you will learn the following
Model Implementation
•Tailor the Model to the Task
Designing Models XXIV
Model Architecture •Tailoring the Model to task •Loss Function6:24
At the end of this lecture, you will learn the following
Model Architecture
•Tailoring the Model to task
•Loss Function
Designing Models XXV
Tailor the Model to the Task •Optimization Algorithm5:20
At the end of this lecture, you will learn the following
Tailor the Model to the Task
•Optimization Algorithm
Designing Models XXVI
Tailor the Model to the Task •Learning Rate Schedule3:33
At the end of this lecture, you will learn the following
Tailor the Model to the Task
•Learning Rate Schedule
Designing Models XXVII
Tailor the Model to the Task •How to select and tune optimization algorithms3:48
At the end of this lecture, you will learn the following
Tailor the Model to the Task
•How to select and tune optimization algorithms and learning rate schedules?
Tuning
Designing Models XXVIII
Hyperparameter Tuning5:53
At the end of this lecture, you will learn the following
Model Training and Hyperparameter Tuning
•Hyperparameter Tuning
Designing Models XXIX
How to Implement efficient training procedures4:26
At the end of this lecture, you will learn the following
How to Implement efficient training procedures
Designing Models XXX
Model Pruning and Quantization5:13
At the end of this lecture, you will learn the follow
Scalability and Efficiency
•Model Pruning and Quantization
Designing Models XXXI
Distributed Training4:05
At the end of this lecture, you will learn the following
Scalability and Efficiency
•Distributed Training
Designing Models XXXII
Hardware Utilization6:53
At the end of this lecture, you will learn the following
Scalability and Efficiency
•Hardware Utilization
Designing Models XXXIII
Model Architecture Example3:47
At the end of this lecture, you will learn the following
Model Architecture Example
Designing Models XXXIV
Frameworks and Libraries3:27
At the end of this lecture, you will learn the following
Model Implementation
•Frameworks and Libraries
Designing Models XXXV
Designing Models Case Example11:21
Example of creating and developing new generative models tailored to specific applications, such as natural language processing (NLP), image generation, music composition, or other creative tasks
Design a Generative AI Solution for a Business Problem

Training Models I2:47
At the end of this lecture, you will learn the following
•Training these models on large datasets, ensuring they learn to generate high-quality, coherent, and relevant content
Training Models I
Data Collection and Preparation Large Dataset7:39
At the end of this lecture, you will learn the following
Training these models on large datasets, ensuring they learn to generate high-quality, coherent, and relevant content
•Data Collection and Preparation
Large Dataset
Training Models II
Data Collection and Preparation Data Cleaning5:01
Training these models on large datasets, ensuring they learn to generate high-quality, coherent, and relevant content
•Data Collection and Preparation
Data Cleaning
Training Models III
Data Collection and Preparation Preprocessing •Model Selection5:06
At the end of this lecture, you will learn the following
Training these models on large datasets, ensuring they learn to generate high-quality, coherent, and relevant content
•Data Collection and Preparation
Preprocessing
•Model Selection
Training Models IV
Hyperparameter Tuning6:17
At the end of this lecture, you will learn the following
Training these models on large datasets, ensuring they learn to generate high-quality, coherent, and relevant content
•Training the Model
Hyperparameter Tuning
Training Models V
Training the Model - Loss Function5:56
At the end of this lecture, you will learn the following
Training these models on large datasets, ensuring they learn to generate high-quality, coherent, and relevant content
•Training the Model
Loss Function
Training Models VI
•Training the Model - Optimization5:08
At the end of this lecture, you will learn the following
Training these models on large datasets, ensuring they learn to generate high-quality, coherent, and relevant content
•Training the Model
Optimization
Training Models VII
Training the Model - Regularization4:54
At the end of this lecture, you will learn the following
Training these models on large datasets, ensuring they learn to generate high-quality, coherent, and relevant content
•Training the Model
Regularization
Training Models VIII
Evaluation and Fine-Tuning - Validation - Metrics4:46
At the end of this lecture, you will learn the following
Training these models on large datasets, ensuring they learn to generate high-quality, coherent, and relevant content
•Evaluation and Fine-Tuning
Validation
Metrics
Training Models IX
Evaluation and Fine-Tuning - Metrics - Perplexity7:42
At the end of this lecture, you will learn the following
Training these models on large datasets, ensuring they learn to generate high-quality, coherent, and relevant content
•Evaluation and Fine-Tuning
Metrics
Perplexity
Training Models X
valuation and Fine-Tuning - Metrics - BLEU Score8:52
At the end of this lecture, you will learn the following
Training these models on large datasets, ensuring they learn to generate high-quality, coherent, and relevant content
•Evaluation and Fine-Tuning
Metrics
BLEU Score
Training Models XI
Evaluation and Fine-Tuning - Metrics - Human Evaluation5:56
At the end of this lecture, you will learn the following
Training these models on large datasets, ensuring they learn to generate high-quality, coherent, and relevant content
•Evaluation and Fine-Tuning
Metrics
Human Evaluation
Training Models XII
Evaluation and Fine-Tuning - Metrics - Other Metrics7:29
At the end of this lecture, you will learn the following
Training these models on large datasets, ensuring they learn to generate high-quality, coherent, and relevant content
•Evaluation and Fine-Tuning
Metrics
Other Metrics
Training Models XIII
Fine-Tuning4:46
At the end of this lecture, you will learn the following
Training these models on large datasets, ensuring they learn to generate high-quality, coherent, and relevant content
•Evaluation and Fine-Tuning
Fine-Tuning
Training Models XIV
Generating Content7:38
At the end of this lecture, you will learn the following
Training these models on large datasets, ensuring they learn to generate high-quality, coherent, and relevant content
•Generating Content
Training Models XV
Post-Processing5:55
At the end of this lecture, you will learn the following
Training these models on large datasets, ensuring they learn to generate high-quality, coherent, and relevant content
•Post-Processing
Training Models XVI
Develop a Training Strategy for a Generative AI Model

Optimizing Performance I- How to Enhance the efficiency and performance of model7:01
At the end of this lecture, you will learn the following
•How to Enhance the efficiency and performance of models, making sure they run effectively and can generate content quickly and accurately
Optimizing Performance I
Optimize Model Architecture - Quantization3:44
At the end of this lecture, you will learn the following
How to Enhance the efficiency and performance of models, making sure they run effectively and can generate content quickly and accurately
•Optimize Model Architecture
Quantization
Optimizing Performance II
Optimize Model Architecture - Knowledge Distillation4:15
At the end of this lecture, you will learn the following
How to Enhance the efficiency and performance of models, making sure they run effectively and can generate content quickly and accurately
•Optimize Model Architecture
Knowledge Distillation
Optimizing Performance III
Efficient Training Techniques - Gradient Accumulation5:58
At the end of this lecture, you will learn the following
How to Enhance the efficiency and performance of models, making sure they run effectively and can generate content quickly and accurately
•Efficient Training Techniques
Gradient Accumulation
Optimizing Performance IV
Efficient Training Technique - Mixed Precision Training4:58
At the end of this lecture, you will learn the following
How to Enhance the efficiency and performance of models, making sure they run effectively and can generate content quickly and accurately
•Efficient Training Technique
Mixed Precision Training
Optimizing Performance V
Efficient Training Technique - Learning Rate Scheduling5:32
At the end of this lecture, you will learn the following
How to Enhance the efficiency and performance of models, making sure they run effectively and can generate content quickly and accurately
•Efficient Training Technique
Learning Rate Scheduling
Optimizing Performance VI
Optimizing Performance VII- Data Augmentation4:38
At the end of this lecture, you will learn the following
How to Enhance the efficiency and performance of models, making sure they run effectively and can generate content quickly and accurately
•Data Management
Data Augmentation
Optimizing Performance VII
Efficient Data Loading4:31
At the end of this lecture, you will learn the following
How to Enhance the efficiency and performance of models, making sure they run effectively and can generate content quickly and accurately
•Data Management
Efficient Data Loading
Optimizing Performance VIII
Hardware Utilization - Use Accelerators6:06
At the end of this lecture, you will learn the following
How to Enhance the efficiency and performance of models, making sure they run effectively and can generate content quickly and accurately
•Hardware Utilization
Use Accelerators
Optimizing Performance IX
Hardware Utilization - Distributed Training3:50
At the end of this lecture, you will learn the following
How to Enhance the efficiency and performance of models, making sure they run effectively and can generate content quickly and accurately
•Hardware Utilization
Distributed Training
Optimizing Performance X
Hardware Utilization - Memory Management3:14
At the end of this lecture, you will learn the following
How to Enhance the efficiency and performance of models, making sure they run effectively and can generate content quickly and accurately
•Hardware Utilization
Memory Management
Optimizing Performance XI
Model Inference - Batch Inference6:06
At the end of this lecture, you will learn the following
How to Enhance the efficiency and performance of models, making sure they run effectively and can generate content quickly and accurately
•Model Inference
Batch Inference
Optimizing Performance XII
Model Inference - Inference Caching4:12
At the end of this lecture, you will learn the following
How to Enhance the efficiency and performance of models, making sure they run effectively and can generate content quickly and accurately
•Model Inference
Inference Caching
Optimizing Performance XIII
Model Inference - Pipeline Parallelism4:16
At the end of this lecture, you will learn the following
How to Enhance the efficiency and performance of models, making sure they run effectively and can generate content quickly and accurately
•Model Inference
Pipeline Parallelism
Optimizing Performance XIV
Fine-Tuning and Transfer Learning9:05
At the end of this lecture, you will learn the following
How to Enhance the efficiency and performance of models, making sure they run effectively and can generate content quickly and accurately
•Fine-Tuning and Transfer Learning
Optimizing Performance XV
Profiling Tools5:39
At the end of this lecture, you will learn the following
How to Enhance the efficiency and performance of models, making sure they run effectively and can generate content quickly and accurately
•Profiling and Monitoring
Profiling Tools
Optimizing Performance XVI
Real-time Monitoring4:20
At the end of this lecture, you will learn the following
How to Enhance the efficiency and performance of models, making sure they run effectively and can generate content quickly and accurately
•Profiling and Monitoring
Real-time Monitoring
Optimizing Performance XVII
Efficient Libraries5:44
At the end of this lecture, you will learn the following
How to Enhance the efficiency and performance of models, making sure they run effectively and can generate content quickly and accurately
•Software and Frameworks
Efficient Libraries
Optimizing Performance XVIII
Custom Kernels6:36
At the end of this lecture, you will learn the following
How to Enhance the efficiency and performance of models, making sure they run effectively and can generate content quickly and accurately
•Software and Frameworks
Custom Kernels
Optimizing Performance XIX
Optimize the Performance of a Generative AI Solution

API Development I8:59
At the end of this lecture, you will learn the following
•How to build APIs to allow other systems and applications to interact with generative models
API Development I
API Development II- How to Set Up the Environment4:08
At the end of this lecture, you will learn the following
•How to build APIs to allow other systems and applications to interact with generative models
How to Set Up the Environment
API Development II
How to build APIs to allow other systems and applications to interact with GENAI2:21
At the end of this lecture, you will learn the following
•How to build APIs to allow other systems and applications to interact with generative models
API Development Iii
How to build APIs to allow other systems and applications to interact with GenAI3:45
At the end of this lecture, you will learn the following
•How to build APIs to allow other systems and applications to interact with generative models
API Development IV
Design an API Integration for a Generative AI Solution

Deployment I4:34
At the end of this lecture, you will learn the following
•How to deploy models into production environments, ensuring they are accessible, scalable, and maintainable
Deployment I
Model Serving - Orchestration and Deployment7:28
At the end of this lecture, you will learn the following
•How to deploy models into production environments, ensuring they are accessible, scalable, and maintainable
Model Serving
Orchestration and Deployment
Deployment II
How to deploy models into production environments3:53
At the end of this lecture, you will learn the following
•How to deploy models into production environments, ensuring they are accessible, scalable, and maintainable
Deployment III
Develop a Deployment Strategy for a Generative AI Solution

Integration I6:01
At the end of this lecture, you will learn the following
•How to integrate generative models into existing systems, tools, and workflows within an organization
Integration I
Integration II- Work Flow Example3:03
At the end of this lecture, you will learn the following
•How to integrate generative models into existing systems, tools, and workflows within an organization
Work Flow Example
Design an End-to-End Generative AI Integration Workflow

Research and Innovation I6:55
At the end of this lecture, you will learn the following
•How to keep up-to-date with the latest research and advancements in generative AI and machine learning
Research and Innovation I
Research and Innovation II7:26
At the end of this lecture, you will learn the following
•How to experiment with new techniques and methodologies to push the boundaries of what generative models can do
Research and Innovation II
Evaluate an Emerging Generative AI Innovation

Requirements

Willingness to spend 9 hours learning on Generative AI

Description

To become a successful Generative AI Engineer, can you develop the engineering strategies that determine whether a Generative AI solution succeeds or fails?

Most people believe becoming a Generative AI Engineer is about learning AI tools or writing code. In reality, successful Generative AI Engineers are distinguished by their ability to develop the right engineering strategies before a single model is built. They evaluate business problems, recommend the right Generative AI approaches, select appropriate model architectures, develop AI solution strategies, optimize performance, plan deployments, integrate AI into enterprise environments and guide AI initiatives that deliver measurable business value.

This course has been designed to help you build exactly those capabilities.

You will become a Generative AI Engineer through real-world assignments covering AI solution design to deployment strategies, enabling you to progressively develop the same strategic engineering capabilities expected from professionals responsible for delivering successful Generative AI solutions in real organizations.

Unlike courses that primarily focus on AI tools, coding demonstrations or isolated implementation examples, this course develops the engineering thinking required to successfully lead the complete Generative AI engineering lifecycle. Every section has been carefully structured around one of the core responsibilities performed by a Generative AI Engineer, allowing you to progressively build the knowledge, judgement and decision-making capabilities required to succeed in this rapidly growing profession.

You will begin by understanding the role of a Generative AI Engineer and the business opportunities created by Generative AI. You will then learn how to evaluate business problems, select appropriate Generative AI models including GANs, VAEs and Transformer architectures, develop AI solution strategies, design model architectures, create effective training strategies, optimize model performance, develop API strategies, plan enterprise deployment strategies, integrate AI solutions into business processes, evaluate emerging innovations, implement responsible AI governance and communicate engineering recommendations confidently to technical teams, business leaders and executive stakeholders.

Every major stage of the learning journey is reinforced through carefully designed real-world assignments. Rather than simply watching lectures, you will apply proven engineering frameworks to practical business scenarios that simulate the strategic decisions expected from Generative AI Engineers. These assignments progressively strengthen your ability to evaluate alternatives, justify engineering decisions, assess implementation risks, recommend practical AI strategies and solve realistic engineering challenges with confidence.

Throughout the course you will learn not only what engineering decisions need to be made, but also why they matter, when different strategies should be applied and how experienced Generative AI Engineers use structured engineering frameworks to reduce implementation risks while maximizing business value. This strategic decision-making capability is what differentiates professional Generative AI Engineers from individuals who simply know how to operate AI tools.

By the end of this course, you will be able to evaluate business opportunities for Generative AI, recommend appropriate engineering strategies, select suitable AI models, develop end-to-end AI solution strategies, create model training and optimization strategies, plan deployment approaches, integrate AI solutions into enterprise environments, apply responsible AI principles and confidently communicate engineering recommendations to technical teams, management and executive stakeholders.

Whether you are an aspiring Generative AI Engineer, AI Consultant, AI Product Manager, Solution Architect, Technology Professional, Business Transformation Leader or a student preparing for the rapidly evolving AI economy, this course provides the strategic engineering framework required to contribute throughout the complete Generative AI engineering lifecycle.

If your ambition is not simply to learn another AI tool but to become a Generative AI Engineer capable of developing the engineering strategies behind successful AI initiatives, this course will help you build those capabilities through real-world assignments, proven engineering frameworks and practical decision-making that prepare you for the responsibilities of today's Strategic Generative AI Engineers. Enroll Now!

This Course is Part of a Structured Learning Path

Learning Path: TECHNOLOGY PATH (Starter → Builder → Advanced)

This course is your ADVANCED step.

Next Recommended Courses

After completing this course, continue your growth with:

How to become Software Developer (Starter)

Software Development Excellence (Builder)

End to end Solution Design (Builder)

Solution Architecture (Builder)

IT Product Management (Advanced)

Master in AI (Advanced)

Generative AI (Advanced)

Who this course is for:

Aspiring Generative AI Engineers

Master in Strategic Generative AI Engineering

What you'll learn

Explore related topics

Course content

Introduction2 lectures • 16min

Overview4 lectures • 25min

Gen AI Models6 lectures • 49min

Designing Models35 lectures • 3hr 36min

Training Models16 lectures • 1hr 36min

Optimizing Performance19 lectures • 1hr 40min

API Development4 lectures • 19min

Deployment3 lectures • 16min

Integration2 lectures • 9min

Research and Innovation2 lectures • 14min

Requirements

Description

Who this course is for: