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$199.00 USD
$199.00 USD
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Note: This new course is coming soon and is currently available for pre-order.

The Generative AI Engineering course covers a wide range fundamental and advanced AI engineering topics specific to the unique requirements of generative AI systems and on-demand content creation. Topics include generative neural network design, model training approaches, creative content manipulation, model evaluation, validation, scaling, optimization, data bias and concept drift avoidance, and many more.

Complete the Generative AI Engineering course and, optionally, get accredited as a Certified Generative AI Engineer by passing the certification exam. You can purchase the course now and get the exam later, or you can get them together at a discount as part of the Certification Bundle.

Upon completing the course you will receive a digital certificate of completion, as well as a digital training badge from Acclaim/Credly. Upon getting certified you will also receive an official Generative AI Engineer digital accreditation certificate and certification badge from Acclaim/Credly, along with an account that can be used to verify your certification status.

If you already completed the Generative AI Specialist course modules, you can purchase a partial course (or a partial bundle) with only the modules specific to the Generative AI Engineer track here.

Download Guide &
Course Outlines (PDF)

The Generative AI Engineering course is comprised of the following 5 course modules, each of which has an estimated completion time of 10 hours:

  • Module 4: Fundamental Generative AI
  • Module 5: Advanced Generative AI
  • Module 10: Fundamental Generative AI Engineering
  • Module 11: Advanced Generative AI Engineering
  • Module 12: Generative AI Engineering Lab

Choose the Certification Bundle to receive the entire course together with the online-proctored certification exam and a set of practice exam questions, all at a bundle discount.

Exam Details

Upon purchasing this course, you will automatically receive access via the Online Interactive eLearning platform. To provide you with the greatest flexibility, you will also have the option to access the course materials via two additional eLearning formats, at no extra cost. All three eLearning formats are briefly described below. A more detailed comparison can be found here.
  1. For everyday learning: An online interactive eLearning platform with individual lessons, as well as interactive and automatically graded exercises and practice questions.
  2. For learning on-the-go: A study kit platform with access to full course documents that support online/offline synching, annotations, comments, custom bookmarks and cross-document searches.
  3. For your reference: A set of printable watermarked PDF documents that you can keep (for all course workbooks and posters).
All three forms of access are subject to Arcitura’s *. Upon purchase, access to the online interactive eLearning platform (1) is provided within one business day. Access to the study kits (2) and the PDF documents (3) is provided upon request.

The course is comprised of a set of modules. Each module has a set of lessons and is further supplemented with exercises to help reinforce your understanding of key topics. Shown below are the digital contents and the topic outline for each course module:


Module 4: Fundamental Generative AI

This course module explores the application of generative AI within a range of business scenarios and provides fundamental coverage of generative AI concepts, models, best practices and neural networks, including Generative Adversarial Networks (GANs), Variational Encoders (VAEs) and Transformer models. All of the content is authored in easy-to-understand, plain English.


Course Module Contents

  • Workbook Lessons (100+ pages)
  • Interactive Exercises
  • Mind Map Poster

  • Symbol Legend Poster
  • Supplement
  • Practice Exam Questions
  • PDFs of Workbook and Posters (printable)

Topics Covered

  • Generative AI Business and Technology Drivers
  • Generative AI Benefits
  • Common Risks and Challenges of Using Generative AI
  • Business Problem Categories Addressed by Generative AI
  • Understanding Models, Algorithms and Neural Networks
  • Types of Generative AI

  • Training Generative Models and Understanding the Training Loop
  • Understanding Generative Adversarial Networks (GANs)
  • Understanding Variational Encoders (VAE)
  • Understanding Transformers
  • Steps to Building AI Systems
  • Generative AI Best Practices

Module 5: Advanced Generative AI

This course module covers a range of common generative AI networks, models and techniques, including specialized neural networks and practices for managing and optimizing generative AI systems and model training processes. The course module does not cover any mathematical formulas or programming and is intended for general IT professionals.


Course Module Contents

  • Workbook Lessons (100+ pages)
  • Interactive Exercises
  • Mind Map Poster

  • Practice Exam Questions
  • PDFs of Workbook and Poster (printable)

Topics Covered

  • Ethical Guardians and Output Translators
  • Pre-Trained Language Models (PLMs) and Transfer Learning
  • Noise Injection, Temperature Adjustment and Random Sparks
  • Working with Generative Adversarial Networks (GANs)
  • Working with Variational Encoders (VAE)
  • Working with Transformers
  • Working with Conditional Generative Adversarial Networks (cGAN)

  • Working with Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
  • Large Language Models (LLM) and Natural Language Processing (NLP)
  • Model Evaluation and Training Performance Evaluation
  • Baseline Modeling and Model Optimization
  • Overfitting Avoidance

Module 10: Fundamental Generative AI Engineering

This course module provides in-depth coverage of essential engineering practices for training and operating generative AI systems, including various data processing, filtering and management techniques specific to creative content generation. The module further covers commonly related topics, such as natural language processing (NLP), transfer learning and the use of pre-trained models.


Course Module Contents

  • Workbook Lessons (100+ pages)
  • Interactive Exercises
  • Mind Map Poster

  • Symbol Legend Poster
  • Practice Exam Questions
  • PDFs of Workbook and Posters (printable)

Topics Covered

  • Data Representation and Encoding
  • Latent Space and its Manipulation
  • Prompt Engineering
  • Metrics for Evaluating Generative Models
  • Generative AI Model Selection and Hyperparameter Tuning
  • Generative AI Model Deployment, Monitoring and Maintenance
  • Generative AI Bias Detection and Mitigation
  • Generative AI Model Explainability and Interpretability
  • Model Evaluation and Validation Techniques for Generative AI
  • Data Preprocessing Techniques, Overfitting and Regularization

  • Performance Optimization Techniques for Generative AI Models
  • Understanding Generative Neural Networks and Models
  • Neural Network Types, Neurons, Layers, Links, Weights in Generative AI
  • Loss, Hyperparameters, Learning Rate, Bias, Epoch in Generative AI
  • Activation Functions (Leaky ReLU, Tanh, ReLU, Softmax, Sigmoid, Softplus)
  • Neuron Cell Types (Input, Backfed, Noisy, Hidden, Probabilistic, Spiking, Recurrent, Memory, Kernel, Convolution, Pool, Output, Match Input, etc.)
  • Common Neural Network Architectures for Generative AI Systems

Module 11: Advanced Generative AI Engineering

This course module covers a series of techniques for working with GANs, VAEs, diffusion models, autoregressive models, transformers, as well as transfer learning and reinforcement learning. Topics include managing large datasets, managing iterative model training cycles, fostering creative content output and working with different data formats for content generation purposes. The techniques can be applied individually or in different combinations to address a range of common generative AI system problems and requirements.


Course Module Contents

  • Workbook Lessons (100+ pages)
  • Interactive Exercises
  • Mind Map Poster

  • Practice Exam Questions
  • PDFs of Workbook and Poster (printable)

Topics Covered

  • Data Wrangling for Preparing Data for Generative Model Input
  • Feature Encoding for Optimizing Generative Model Input
  • Feature Imputation for Enhancing Data for Generative Model Input
  • Feature Scaling for Optimizing Data for Generative Model Input
  • Text Representation for Converting Data for Generative Model Input
  • Dimensionality Reduction to Reduce Feature Space for Generative Model Input
  • Unsupervised Learning for Training Generative Models
  • Generative Model Configuration for Defining the Number of Neurons in Network Layers
  • Image Generation for Using a Generative Adversarial Network
  • Sequence Generation for Using a Long Short Term Memory Neural Network
  • Supervised Learning Patterns for Training Generative Models

  • Identifying Patterns via a Generative Adversarial Network
  • Content Filtering for Removing Unsafe Content
  • Model Evaluation Patterns for Measuring Generative Model Performance
  • Training Performance Evaluation for Assessing Generative Model Performance
  • Content Generation Performance Evaluation for Predicting Generative Model Performance in Production
  • Baseline Modeling for Assessing and Comparing Complex Generative Models
  • Model Optimization Patterns for Refining and Adapting Generative Models
  • Overfitting Avoidance for Tuning a Generative Model
  • Frequent Model Retraining for Keeping a Generative Model in Synch with Current Data
  • Transfer Learning for Accelerating Generative Model Training
  • Reinforcement Learning for Generative Models

Module 12: Generative AI Engineering Lab

This course module provides a series of case-study driven, lab-style exercises and problems that are designed to test your ability to apply your knowledge of topics covered in previous modules. Completing this lab helps reinforce understanding of preceding topics and further demonstrates how different practices and technologies can be applied together as part of greater solutions.


Course Module Contents

  • Lab Exercise Booklet
  • Mind Map Poster

  • Practice Exam Questions
  • PDFs of Exercise Booklet and Poster (printable)

Learn About Arcitura: Take the Video Tour

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About Arcitura

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About Arcitura Certifications

What’s in an Arcitura Course

Comprehensive
Coverage

Each course provides a comprehensive curriculum with 2-3 modules and 20-40 hours of training.

More Than Just
Video Lessons

In addition to standard video lessons, courses include full-color workbooks and reference posters for all lessons.

Interactive & Graded
Challenges

Courses also include interactive and graded exercises, interactive and graded self-tests and other supplements.

The Arcitura Difference

EACH COURSE

  • is authored by a dedicated courseware development team
  • has a self-test, accreditation exam and professional certification
  • is available via two different eLearning platforms

ALL COURSES

  • undergo a common development process
  • are authored to be consistent in quality, structure and style
  • share a common vocabulary and symbol notation
  • are authored in collaboration with subject matter experts

Take Your Skills Anywhere

Regardless of whether you are an individual looking to boost your career or an organization looking to up-skill a team, Arcitura courses and certifications provide a sound investment.

Because both courses and accreditations are vendor-neutral, they empower you with skills and credentials that you can take to wherever you need to go.

Professional Instructor-Led Training & Coaching

 

Lifetime Access Guarantee

Access to the interactive eLearning platform (1) and the study kit platform (3) is dependent on third-party platform providers. As a result, Arcitura cannot make any guarantees or warranties regarding the behavior or availability of the platform, nor can Arcitura guarantee a specific duration for which these platforms will remain available. When your course is set up, your access will have no expiration date and will be provided for a minimum of one year and indefinitely thereafter, for as long as the platforms remain available under the conditions by which they were contracted by Arcitura. The PDF documents (3) that will be made available for you to download are yours to keep forever.

QUESTIONS?

Contact info@arcitura.com or 604-904-4100 during PT working hours.