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Designing and Implementing Enterprise-Grade ML Applications

This advanced Machine Learning (ML) course is designed for Data Science and ML professionals who want to master designing and implementing enterprise-grade ML applications. Attendees learn how to...

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Duration 4 days
Course Code WA3518
Available Formats Classroom

Overview

This advanced Machine Learning (ML) course is designed for Data Science and ML professionals who want to master designing and implementing enterprise-grade ML applications. Attendees learn how to evaluate advanced LLM architectures and dive into advanced topics, such as fine-tuning and quantization techniques, LLM-powered recommender systems, model evaluation, and debugging, as well as ethical considerations and responsible AI practices for enterprise-grade LLMs.

Skills Gained

  • Implement advanced fine-tuning and quantization techniques for domain-specific LLM adaptation and efficient deployment
  • Design and implement LLM-powered recommender systems using hybrid architectures and evaluation techniques
  • Apply advanced model evaluation, interpretation, and debugging techniques for understanding and improving LLM behavior
  • Implement ethical considerations and responsible AI practices for mitigating biases and ensuring fairness in enterprise-grade LLM applications

Prerequisites

  • Practical programming skills in Python and familiarity with LLM concepts and frameworks (3+ Months LLM, 6+ Months Python and Machine Learning)
  • LLM Access via API (OpenAI), Open Source Libraries (HuggingFace)
  • LLM Application development experience (RAG, Chatbots, etc)
  • Strong practical understanding of ML concepts, algorithms, and evaluation
  • Supervised Learning, Unsupervised Learning, and respective algorithms
  • Statistics, Probability, and Linear Algebra (vectors) foundations
  • Experience with at least one deep learning framework (e.g., TensorFlow, PyTorch)

Course Details

Outline

Advanced Fine-Tuning and Quantization Techniques for LLMs

  • Exploring advanced fine-tuning techniques and architectures for domain-specific LLM adaptation
  • Implementing multi-task, meta-learning, and transfer learning techniques for LLM fine-tuning
  • Leveraging domain-specific pre-training and intermediate fine-tuning for improved LLM performance
  • Quantization and compression techniques for efficient LLM fine-tuning and deployment
  • Implementing post-training quantization and pruning techniques for LLM model compression
  • Exploring quantization-aware training and other techniques for efficient LLM fine-tuning
  • Implementing advanced fine-tuning and quantization techniques for a domain-specific LLM
  • Designing and implementing a multi-task fine-tuning architecture with domain-specific pre-training
  • Applying quantization and pruning techniques for fine-tuning

Designing and Implementing LLM-Powered Recommender Systems

  • Exploring advanced architectures and techniques for LLM-powered recommender systems
  • Leveraging LLMs for multi-modal and context-aware recommendation generation
  • Implementing hybrid recommender architectures combining LLMs with collaborative and content-based filtering
  • Evaluating and optimizing LLM-powered recommender system performance
  • Designing and conducting offline and online evaluation studies for LLM-powered recommender systems
  • Implementing advanced evaluation metrics and techniques for assessing recommendation quality and diversity
  • Hands-on: Building an LLM-powered recommender system for a specific domain

Advanced Model Evaluation, Interpretation, and Debugging Techniques

  • Implementing advanced evaluation and benchmarking techniques for LLM-based applications
  • Designing and conducting comprehensive evaluation studies with domain-specific metrics and datasets
  • Leveraging advanced evaluation frameworks and platforms for automated and reproducible evaluation
  • Model interpretation and debugging techniques for understanding LLM behavior and failures
  • Implementing advanced model interpretation techniques, such as attention visualization and probing
  • Leveraging debugging techniques, such as counterfactual analysis and influence functions, for identifying and mitigating LLM failures
  • Conducting an advanced evaluation and debugging study for an LLM-based application
  • Designing and implementing a comprehensive evaluation study with domain-specific metrics and datasets
  • Applying model interpretation and debugging techniques for LLMs

Ethical Considerations and Responsible AI Practices for Enterprise-Grade LLMs

  • Implementing advanced techniques for mitigating biases and ensuring fairness in LLM-based applications
  • Leveraging advanced bias detection and mitigation techniques, such as adversarial debiasing and fairness constraints
  • Designing and conducting fairness audits and assessments for LLM-based applications
  • Ensuring transparency, accountability, and explainability in LLM-based decision-making
  • Implementing advanced explainability techniques, such as counterfactual explanations and feature importance
  • Designing and implementing governance frameworks and processes for responsible LLM deployment and monitoring
  • Conducting an ethical assessment and implementing responsible AI practices for an LLM-based application

Schedule

FAQ

Does the course schedule include a Lunchbreak?

Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.

What languages are used to deliver training?

Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.

What does GTR stand for?

GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.

Does Ascendient Learning deliver group training?

Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.

What does vendor-authorized training mean?

As a vendor-authorized training partner, we offer a curriculum that our partners have vetted. We use the same course materials and facilitate the same labs as our vendor-delivered training. These courses are considered the gold standard and, as such, are priced accordingly.

Is the training too basic, or will you go deep into technology?

It depends on your requirements, your role in your company, and your depth of knowledge. The good news about many of our learning paths, you can start from the fundamentals to highly specialized training.

How up-to-date are your courses and support materials?

We continuously work with our vendors to evaluate and refresh course material to reflect the latest training courses and best practices.

Are your instructors seasoned trainers who have deep knowledge of the training topic?

Ascendient Learning instructors have an average of 27 years of practical IT experience and have also served as consultants for an average of 15 years. To stay current, instructors spend at least 25 percent of their time learning new, emerging technologies and courses.

Do you provide hands-on training and exercises in an actual lab environment?

Lab access is dependent on the vendor and the type of training you sign up for. However, many of our top vendors will provide lab access to students to test and practice. The course description will specify lab access.

Will you customize the training for our company’s specific needs and goals?

We will work with you to identify training needs and areas of growth.  We offer a variety of training methods, such as private group training, on-site of your choice, and virtually. We provide courses and certifications that are aligned with your business goals.

How do I get started with certification?

Getting started on a certification pathway depends on your goals and the vendor you choose to get certified in. Many vendors offer entry-level IT certification to advanced IT certification that can boost your career. To get access to certification vouchers and discounts, please contact info@ascendientlearning.com.

Will I get access to content after I complete a course?

You will get access to the PDF of course books and guides, but access to the recording and slides will depend on the vendor and type of training you receive.

How do I request a W9 for Ascendient Learning?

View our filing status and how to request a W9.

Reviews

Class was easy to sign up for and ExitCertified provided very good communication

The platform is very intuitive and easy to navigate. Great tool for learning

The labs and course material gave me valuable insights into cloud security architecture

Labs and the study materials provided for Architecting on AWS course are very easy to understand and explains all the topics required to pass the Associate certification.

The instructor really took his time and made sure I was able to understand the concepts.