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LLM Evaluation Using MLflow Intermediate

Master LLM Evaluation Using MLflow to deploy, trace, and validate Large Language Models effectively—ideal for MLOps engineers and AI developers facing the 85% model failure rate in production. Gain hands-on expertise in systematic evaluation, LLM-as-a-judge metrics, and automated validation using MLflow’s unified platform, addressing the critical skills gap in enterprises adopting generative AI.

This Koenig Original course prepares professionals for real-world LLM deployment challenges with 30-day lab access and 1-on-1 training. Build production-ready evaluation pipelines using MLflow’s native flavors and plugins, ensuring models meet performance benchmarks and accelerate career growth in high-demand AI engineering roles.

24 Hours (3 Days)
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0+ professionals trained

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Course Overview

The LLM Evaluation Using MLflow course by Koenig Original provides an essential MLflow LLM evaluation tutorial for professionals seeking to master AI model validation training. Designed for data scientists and machine learning engineers, this curriculum focuses on the systematic assessment of Large Language Models. Koenig Original differentiates this offering with its Guaranteed-to-Run scheduling and 1-on-1 training options, ensuring that every participant receives personalized, hands-on instruction to master these critical MLOps competencies. This training is ideal for those involved in deploying, monitoring, or validating generative AI applications within complex enterprise environments.

Participants gain hands-on experience with core components of the MLflow framework, including LLM-as-a-Judge scorers, evaluation datasets, and tracing. Using real-world scenarios, students configure automated evaluation pipelines in a cloud-based lab environment to test and score model outputs. A key project involves building a comprehensive evaluation suite for a Retrieval-Augmented Generation system, where learners assess retrieval relevance, groundedness, and response correctness. Through guided labs, attendees work with custom scorers to simulate production-level validation workflows, ensuring they can effectively manage model performance and safety.

This course enhances career readiness for roles in AI evaluation, a discipline seeing rapid industry adoption. According to LinkedIn job market data from early 2026, there is significant demand for specialized talent, with over 150 dedicated AI Evaluation Engineer roles posted. Graduates are well-positioned for these positions, with U.S. salaries averaging 125,000 dollars and reaching up to 173,000 dollars in high-demand markets. By mastering MLflow’s systematic evaluation capabilities, participants are equipped to lead evaluation-driven development practices and successfully drive AI applications from prototype to reliable production deployment.

What You'll Learn

✓Configure MLflow core components including the Tracking URI and Model Registry to scale large language model evaluation for complex production environments.
✓Execute seamless deployments of large language models using MLflow to minimize setup latency and ensure consistent model performance.
✓Audit large language model outputs using the MLflow Evaluate API and industry-standard metrics to identify optimal models and improve predictive accuracy.
✓Optimize prompt engineering strategies within hands-on cloud-based labs to refine large language model responses and enhance user satisfaction.
✓Implement automated validation workflows integrating Giskard and Trubrics to enforce model quality standards and reduce manual testing requirements.
✓Deploy and manage MLflow deployment servers for large language models to maintain high availability and security for enterprise AI applications as part of Koenig Original's expert-led training on LLM Evaluation Using MLflow.

Prerequisites

Recommended knowledge before taking this course
  • Strong knowledge of machine learning fundamentals, essential for effective LLM evaluation using MLflow in the Koenig Original course
  • Proficiency in Python programming to implement and customize LLM assessment workflows with MLflow
  • Familiarity with key model evaluation metrics like precision, recall, and F1-score, crucial for assessing Large Language Models in MLflow environments
  • Hands-on experience deploying and working with Large Language Models (LLMs), enabling practical application of MLflow's tracking and management features
  • Understanding of MLflow's core components—Tracking, Models, and Registry—vital for comprehensive LLM evaluation and model lifecycle management
  • Practical experience with AI/ML experimentation and validation tools to optimize LLM performance and reliability in real-world scenarios
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Certification Exam

Everything you need to know about the LLM Evaluation Using MLflow certification exam

Exam Details
Exam Name
LLM Evaluation Using MLflow
Format
Multiple choice, labs & case studies
Questions
Duration
Passing Score
Validity
Retake Policy
Not applicable
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Course Curriculum

Structured learning with hands-on labs and real-world scenarios

1
Day 1– Mastering LLM Evaluation Using MLflow Fundamentals
MLflow Framework Essentials Core MLflow Architecture Components Configuring MLflow Development Environments Defining LLM Evaluation Requirements Tracking LLM Performance Metrics Logging Parameters and Model Metrics Structuring Experiments for Reproducibility Lab: Executing Initial LLM Experiments
2
Day 2– Advanced LLM Evaluation Using MLflow Deployment
MLflow Models Framework Integration Native MLflow Flavors for LLMs Standardizing LLM Packaging Workflows Deploying LLMs via MLflow Servers Managing MLflow Model Registry Versioning LLMs for Production Reliability Serving Models via REST APIs Lab: Deploying Hugging Face LLMs
3
Day 3– Advanced LLM Evaluation Using MLflow Validation
Quantifying LLM Performance Metrics Static Dataset Evaluation Methodologies Designing Functional Evaluation Metrics Utilizing MLflow Evaluation Modules Automating Model Validation Pipelines Integrating Giskard Evaluation Plugins Implementing Trubrics for Validation Lab: Validating LLMs with MLflow

What's Included in Your Training

Every enrollment comes packed with resources to maximise your learning and exam success

Career Outcomes

82%

of LLM Evaluation Using MLflow certified professionals report career advancement within 6 months

Salary Impact

+28%

Average salary increase reported after obtaining the LLM Evaluation Using MLflow certification

Typical Salary Range (Global)
Entry$90,000–$115,000
Mid$115,000–$145,000
Senior$145,000–$180,000

*Source: Glassdoor / LinkedIn 2025

Job Roles

6
  • LLM Evaluation Specialist
  • MLOps Engineer
  • Machine Learning Engineer
  • AI Platform Engineer
  • GenAI Quality Engineer
  • LLM Operations Engineer

Companies Hiring

5,000+
Databricks Microsoft Google Accenture Deloitte JPMorgan Chase Meta Amazon IBM Capgemini

and 5,000+ organizations worldwide seeking LLM Evaluation Using MLflow certified professionals

Real Transformations

Course Student Reviews

Real results from IT professionals who trained with Koenig — rated 4.9/5 from 18,400+ verified reviews.

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    AZ-400 Team Training ✓ Verified
  • ★★★★★

    “Passed AZ-104 on first attempt. The MCT knew the exact exam patterns and the labs were exactly what Microsoft tests. Worth every penny.”

    Rahul M.

    Rahul M.

    Azure Administrator

    AZ-104 Certified ✓ Verified
  • ★★★★★

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  • ★★★★★

    “The 1-on-1 format was a game changer. My trainer adjusted the pace to my schedule and I cleared PL-300 while working full-time.”

    Ahmed R.

    Ahmed R.

    Business Intelligence Lead

    PL-300 Certified ✓ Verified
  • ★★★★★

    “Passed AZ-104 on first attempt. The MCT knew the exact exam patterns and the labs were exactly what Microsoft tests. Worth every penny.”

    Rahul M.

    Rahul M.

    Azure Administrator

    AZ-104 Certified ✓ Verified
  • ★★★★★

    “I trained 15 of my team members for SC-200. Koenig's on-site delivery was seamless and all 15 passed within 3 months.”

    Sarah K.

    Sarah K.

    CISO, Financial Services

    Enterprise Client ✓ Verified
  • ★★★★★

    “The 1-on-1 format was a game changer. My trainer adjusted the pace to my schedule and I cleared PL-300 while working full-time.”

    Ahmed R.

    Ahmed R.

    Business Intelligence Lead

    PL-300 Certified ✓ Verified
  • ★★★★★

    “From AZ-900 to AZ-305 in 6 months. Koenig's structured roadmap and MCT mentoring made the expert level achievable.”

    Priya S.

    Priya S.

    Cloud Solutions Architect

    AZ-305 Expert ✓ Verified
  • ★★★★★

    “As an L&D head I've used 5 training vendors. Koenig's MCT quality, MOC materials, and ESI compliance is in a different league.”

    James T.

    James T.

    Head of L&D, UK Enterprise

    100+ Learners Trained ✓ Verified
  • ★★★★★

    “SC-900 and SC-300 back to back — both cleared first try. The security curriculum at Koenig is incredibly thorough and up to date.”

    Aisha N.

    Aisha N.

    Security Analyst

    SC-300 Certified ✓ Verified
  • ★★★★★

    “From AZ-900 to AZ-305 in 6 months. Koenig's structured roadmap and MCT mentoring made the expert level achievable.”

    Priya S.

    Priya S.

    Cloud Solutions Architect

    AZ-305 Expert ✓ Verified
  • ★★★★★

    “As an L&D head I've used 5 training vendors. Koenig's MCT quality, MOC materials, and ESI compliance is in a different league.”

    James T.

    James T.

    Head of L&D, UK Enterprise

    100+ Learners Trained ✓ Verified
  • ★★★★★

    “SC-900 and SC-300 back to back — both cleared first try. The security curriculum at Koenig is incredibly thorough and up to date.”

    Aisha N.

    Aisha N.

    Security Analyst

    SC-300 Certified ✓ Verified
  • ★★★★★

    “AI-102 was daunting but the trainer broke it down perfectly. Real Azure OpenAI labs made the difference. Highly recommend.”

    David L.

    David L.

    AI Engineer

    AI-102 Certified ✓ Verified
  • ★★★★★

    “DP-600 Fabric certification done in 3 weeks of part-time study. The customised schedule around my timezone was a lifesaver.”

    Mei W.

    Mei W.

    Data Platform Engineer

    DP-600 Certified ✓ Verified
  • ★★★★★

    “Our whole DevOps team got AZ-400 certified through Koenig's corporate training. Smooth logistics and top-tier MCTs throughout.”

    Carlos R.

    Carlos R.

    Engineering Manager

    AZ-400 Team Training ✓ Verified
  • ★★★★★

    “AI-102 was daunting but the trainer broke it down perfectly. Real Azure OpenAI labs made the difference. Highly recommend.”

    David L.

    David L.

    AI Engineer

    AI-102 Certified ✓ Verified
  • ★★★★★

    “DP-600 Fabric certification done in 3 weeks of part-time study. The customised schedule around my timezone was a lifesaver.”

    Mei W.

    Mei W.

    Data Platform Engineer

    DP-600 Certified ✓ Verified
  • ★★★★★

    “Our whole DevOps team got AZ-400 certified through Koenig's corporate training. Smooth logistics and top-tier MCTs throughout.”

    Carlos R.

    Carlos R.

    Engineering Manager

    AZ-400 Team Training ✓ Verified

Frequently Asked Questions

Everything you need to know about the LLM Evaluation Using MLflow training course

Is the certification exam included in the course fee for LLM Evaluation Using MLflow?
The certification exam is not included in the LLM Evaluation Using MLflow course fee and requires separate purchase. The exam voucher is an optional add-on. The base course fee is INR 22,359, while the comprehensive package including hands-on labs is INR 28,359.
What training formats are available for the LLM Evaluation Using MLflow course, and is Guaranteed-to-Run scheduling offered?
Koenig Original provides the LLM Evaluation Using MLflow course via 1-on-1, live online, and classroom formats. All formats feature Guaranteed-to-Run (GTR) scheduling. This 24-hour expert-led training includes official courseware and practical labs for every enrolled participant.
How long is lab access provided, and what environment is used for the LLM Evaluation Using MLflow course?
Students receive 30 days of lab access for LLM Evaluation Using MLflow via cloud-based sandboxes requiring an OpenAI key. These labs facilitate real-time practice with MLflow LLM tracing, evaluation metrics, and model validation using specialized Giskard and Trubrics plugins.
What is Koenig's rescheduling and cancellation policy for the LLM Evaluation Using MLflow course?
Koenig permits free rescheduling of LLM Evaluation Using MLflow if requested over 10 days before the start. Changes or cancellations within 10 days incur a 50% fee. Per Terms of Service, a single training session may only be rescheduled once.
What is the format, duration, number of questions, and passing score for the LLM Evaluation Using MLflow certification exam?
The LLM Evaluation Using MLflow certification exam consists of multiple-choice questions, labs, and case studies. The 60-minute test features 40–45 questions with a 70% passing threshold. This format follows standard Generative AI exam patterns and industry benchmarks.
How long is the LLM Evaluation Using MLflow certification valid, and what is the renewal process?
The LLM Evaluation Using MLflow certification remains valid for two years. Renewal requires completing the latest course version or passing the current exam. Koenig aligns renewal costs with initial pricing and offers alumni discounts for returning students.
What post-training support does Koenig provide after completing the LLM Evaluation Using MLflow course?
Koenig offers 30 days of post-training support for LLM Evaluation Using MLflow, including session recordings and expert mentor guidance. Learners receive a certificate of completion, practice materials, and eligibility for retakes under the Koenig Happiness Guarantee policy.
What are the prerequisites for enrolling in the LLM Evaluation Using MLflow course?
Prerequisites for LLM Evaluation Using MLflow include basic machine learning knowledge, Python proficiency, and familiarity with metrics like F1-score, precision, and recall. These foundations ensure you effectively master MLflow evaluation tools and complex lab exercises.
How does the LLM Evaluation Using MLflow course impact career progression and earning potential?
Mastering LLM Evaluation Using MLflow accelerates AI engineering careers. Professionals in India often see salary growth from ₹8–12 LPA to ₹15–20 LPA, while global roles average $110,000–$160,000 annually. Demand is surging for experts in generative AI model validation.
How does instructor-led training for LLM Evaluation Using MLflow compare to self-study options?
Instructor-led LLM Evaluation Using MLflow training provides structured mentorship, labs, and feedback, outperforming self-study. Koenig’s approach combines curated content, GTR scheduling, and dedicated support to ensure significantly higher success rates for busy IT professionals.

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