When does class start/end?
Class hours may vary, please reach out to contact@ascendientlearning.com if you have any questions.
Evaluating and Monitoring Generative AI Models and Applications
Build evaluation pipelines and monitoring systems that keep generative AI performing in production. Evaluation foundations and practical techniques cover the difference between offline evaluation and...
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$2,000USD
Duration
2 days
Course Code
GAI-2201
Available Formats
Classroom
Overview
Course Description
Build evaluation pipelines and monitoring systems that keep generative AI performing in production. Evaluation foundations and practical techniques cover the difference between offline evaluation and ongoing monitoring, setting up evaluation pipelines, evaluating different GenAI tasks and use cases, incorporating user feedback, and applying multimodal evaluation. Evaluating agentic AI covers goal setting and planning, tool use and memory, reasoning and decision-making, agentic efficiency, and multi-agent interactions. Generative-AI monitoring covers fundamentals including metrics, workflow, alerts, logs, and verification, tools and infrastructure including dashboards, data logging, and CI/CD integration, and advanced monitoring with remediation through anomaly and drift detection, root-cause analysis, automated and manual remediation, incident response, and continuous improvement leveraged from monitoring data. Hands-on labs produce A/B testing of prompting strategies, a user-feedback pipeline, agentic-evaluation harnesses, a monitoring system with alert thresholds, a monitoring dashboard, and an incident-response plan informed by real telemetry. The course is designed for data scientists, software developers, and DevOps engineers with Python and prior generative-AI experience.
Skills Gained
By the end of this course, participants will be able to:
- Configure evaluation pipelines that surface generative-AI quality issues
- Apply evaluation metrics across text, multimodal, and agentic outputs
- Build monitoring systems that detect drift and anomalies in production
- Apply root-cause analysis and automated remediation to monitoring findings
- Continuously improve generative AI models using monitoring telemetry
Who Can Benefit
This course is designed for:
- Data Scientists
- DevOps
- Software Developers
Prerequisites
Participants should enter this course with:
- Practical Python experience
- GAI-1201 or equivalent
Organizational Objectives
This course assists organizations to:
- Reduce production incidents through codified monitoring metrics, alert thresholds, and remediation playbooks
- Catch model regressions early through evaluation pipelines that run continuously
- Lower mean-time-to-recovery for AI incidents through automated remediation patterns
- Establish a shared evaluation discipline across data-science and engineering teams
Software
All attendees must have a modern web browser and an Internet connection.
Course Details
Course Details
Module 1 - Basics of Evaluating Generative AI Applications
By the end of this module, you will be able to apply automated metrics to GenAI output, distinguish GenAI evaluation from conventional software testing, interpret evaluation results, and define custom evaluation metrics for application-specific quality.
- Generative AI evaluation vs. software evaluation
- Metrics for evaluating generative AI
- Interpreting evaluation results
- Advanced metrics and techniques for generative AI
- Custom evaluation metrics
- Hands-on Lab: Use DeepEval and G-Eval to evaluate one LLM application against built-in and custom criteria.
Module 2 - Practical Generative AI Evaluation Techniques
By the end of this module, you will be able to set up an evaluation pipeline, evaluate different generative AI tasks and use cases, incorporate user feedback, and apply multimodal evaluation techniques.
- Setting up an Evaluation Pipeline
- Evaluating Different Generative AI Tasks
- Evaluating for Specific Use Cases
- Incorporating User Feedback
- Multimodal Evaluation Techniques
- Hands-on Lab: Run A/B testing of prompting strategies and build a user-feedback pipeline for one GenAI application.
Module 3 - Evaluating Agentic AI Systems
By the end of this module, you will be able to evaluate agent goal setting and planning, tool use and state management, reasoning and decision-making, agentic efficiency, and agent interactions with humans.
- Evaluating Goal Setting and Planning
- Evaluating Tool Use, Memory, and State Management
- Evaluating Reasoning and Decision-Making
- Evaluating Agentic Efficiency
- Evaluating Agentic Interactions
- Hands-on Lab: Evaluate agentic goal setting and agent interactions on a multi-step agentic workflow.
Module 4 - Basics of Generative AI Monitoring
By the end of this module, you will be able to distinguish ongoing monitoring from offline evaluation, choose key monitoring metrics for an LLM application in production, instrument an alert and log pipeline, and verify the monitoring system end-to-end.
- Differences Between Evaluation and Monitoring
- Identifying Key Monitoring Metrics
- Understanding the Monitoring Workflow
- Alerts, Logs, and Monitoring Verification
- Setting Up a Monitoring System
- Hands-on Lab: Stand up a monitoring system for one GenAI application with key metrics, alert thresholds, and verification.
Module 5 - Monitoring Tools and Infrastructure
By the end of this module, you will be able to choose between monitoring tools, build a monitoring dashboard, store and log telemetry data, and integrate monitoring with CI/CD pipelines.
- Overview of Monitoring tools
- Building a Monitoring Dashboard
- Data Logging and Storage
- Integration with CI/CD Pipelines
- Hands-on Lab: Build a monitoring dashboard and implement data logging for one GenAI application.
Module 6 - Advanced Monitoring and Remediation Strategies
By the end of this module, you will be able to apply anomaly, drift, and outlier detection to monitoring data, perform root-cause analysis, automate remediation, plan incident response, and use monitoring data for continuous improvement and security vulnerability detection.
- Anomaly, Drift, and Outlier Detection
- Root Cause Analysis
- Automated and Manual Remediation
- Incident Response Planning
- Continuous Improvement Leveraging Monitoring Data
- Monitoring for Security Vulnerabilities
- Hands-on Lab: Perform a root-cause analysis on real monitoring data and stand up an incident-response plan grounded in the findings.
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.
How do I find an Ascendient Learning training location?
We have training locations across the United States and Canada - View a complete list of classroom training locations.
Which delivery formats are available?
At Ascendient Learning, we offer training that is Instructor-Led, Online, Virtual, and Self-Paced.
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
The training was great . But i expected some of the Networking concepts would be covered in this certification .
ExitCertified gave a great course on AWS that covered all of the basics in depth with good lab materials.
Thorough explanations by the instructor along guide and practical training sim of software.
The class covered the concepts needed for the AWS Cloud Practitioner Certification.
Great instructor, clear and concise course. Labs were easy to follow and worked perfectly.
