callicon +1 210 504 8191
whatsAppIcon +1 210 415 9856
emailIcon info@koenig-solutions.com
koenig-logo
logo-30-years
Login
koenig-logo
whatsAppIcon +1 210 415 9856
phoneIcon +1 210 504 8191

LLM Evaluation using MLflow Course Overview

LLM Evaluation using MLflow Course Overview

### LLM Evaluation using MLflow Course Overview

In our comprehensive LLM Evaluation using MLflow course, participants will delve into the core components and practical applications of MLflow over a span of three days (24 hours). The course is designed to teach you to deploy, trace, and evaluate Large Language Models (LLMs) effectively.

Learning objectives:
- Understand MLflow's core components and their scalability.
- Deploy and evaluate LLMs using MLflow.
- Gain expertise in model validation with tools like Giskard's and Trubrics' plugins.

The course includes hands-on labs using your OpenAI key, allowing you to apply these concepts in real-time. Basic knowledge of machine learning, Python, and model evaluation metrics is required to get the most out of this training.

4.6 Rating on Trustpilot

Happiness Guaranteed

happinessGuaranteed_icon

Purchase This Course

Fee On Request

♱

  • Live Training (Duration : 24 Hours)
  • Per Participant
  • Guaranteed-to-Run (GTR)
  • date-img
  • date-img

♱ Excluding VAT/GST

Classroom Training price is on request

You can request classroom training in any city on any date by Requesting More Information

happinessGuaranteed_icon

Happiness Guaranteed

  • Live Training (Duration : 24 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

You can request classroom training in any city on any date by Requesting More Information

happinessGuaranteed_icon

Happiness Guaranteed

Request More Information

Email:  WhatsApp:

Koenig's Unique Offerings

images-1-1

1-on-1 Training

Schedule personalized sessions based upon your availability.
images-1-1

Customized Training

Tailor your learning experience. Dive deeper in topics of greater interest to you.
happinessGuaranteed_icon

Happiness Guaranteed

Experience exceptional training with the confidence of our Happiness Guarantee, ensuring your satisfaction or a full refund.

images-1-1

Destination Training

Learning without limits. Create custom courses that fit your exact needs, from blended topics to brand-new content.

images-1-1

Fly-Me-A-Trainer (FMAT)

Flexible on-site learning for larger groups. Fly an expert to your location anywhere in the world.

Course Prerequisites

Prerequisites for LLM Evaluation using MLflow Course

To ensure a successful learning experience in the "LLM Evaluation using MLflow" course, participants should meet the following prerequisites:


  • Basic understanding of machine learning concepts
  • Proficiency in Python programming
  • Familiarity with model evaluation metrics

Meeting these prerequisites will prepare you to fully engage with the course material and lab exercises.


Target Audience for LLM Evaluation using MLflow

The "LLM Evaluation using MLflow" course by Koenig Solutions offers a comprehensive guide to deploying, tracing, and validating Large Language Models (LLMs) using MLflow, ideal for professionals with a basic understanding of machine learning.


  • Data Scientists
  • Machine Learning Engineers
  • AI Researchers
  • ML Developers
  • DevOps Engineers specializing in ML
  • Data Science Managers
  • AI Product Managers
  • Python Programmers working in AI/ML
  • Data Analysts with Machine Learning focus
  • ML Workflow Enthusiasts
  • Technical Leads in AI/ML projects
  • Software Engineers interested in ML Ops
  • Research Scientists in AI/ML
  • IT Professionals transitioning to Machine Learning
  • AI/ML Consultants


Learning Objectives - What you will Learn in this LLM Evaluation using MLflow?

1. Introduction to Course Learning Outcomes:

The "LLM Evaluation using MLflow" course offers a comprehensive understanding of MLflow, focusing on deploying and evaluating Large Language Models (LLMs). Participants will develop skills in managing machine learning workflows, ensuring effective model validation and evaluation.

2. Learning Objectives and Outcomes:

  • Understand the core components and functionality of MLflow.
  • Learn how to deploy and trace LLMs using MLflow.
  • Gain proficiency in evaluating LLMs with various metrics and automated tools.
  • Master prompt engineering and the use of Native MLflow Flavors for LLMs.
  • Perform comprehensive model validation leveraging plugins like Giskard and Trubrics.
  • Develop familiarity with MLflow deployment servers tailored for LLMs.
  • Explore scalability and practical use cases of MLflow in real-world scenarios.
  • Hands-on experience with MLflow tracing and tracking capabilities for effective workflow management.
  • Implement automated model validation techniques to ensure robustness and reliability.
  • Conduct performance evaluation using static datasets and functional metrics to optimize machine learning workflows.

Target Audience for LLM Evaluation using MLflow

The "LLM Evaluation using MLflow" course by Koenig Solutions offers a comprehensive guide to deploying, tracing, and validating Large Language Models (LLMs) using MLflow, ideal for professionals with a basic understanding of machine learning.


  • Data Scientists
  • Machine Learning Engineers
  • AI Researchers
  • ML Developers
  • DevOps Engineers specializing in ML
  • Data Science Managers
  • AI Product Managers
  • Python Programmers working in AI/ML
  • Data Analysts with Machine Learning focus
  • ML Workflow Enthusiasts
  • Technical Leads in AI/ML projects
  • Software Engineers interested in ML Ops
  • Research Scientists in AI/ML
  • IT Professionals transitioning to Machine Learning
  • AI/ML Consultants


Learning Objectives - What you will Learn in this LLM Evaluation using MLflow?

1. Introduction to Course Learning Outcomes:

The "LLM Evaluation using MLflow" course offers a comprehensive understanding of MLflow, focusing on deploying and evaluating Large Language Models (LLMs). Participants will develop skills in managing machine learning workflows, ensuring effective model validation and evaluation.

2. Learning Objectives and Outcomes:

  • Understand the core components and functionality of MLflow.
  • Learn how to deploy and trace LLMs using MLflow.
  • Gain proficiency in evaluating LLMs with various metrics and automated tools.
  • Master prompt engineering and the use of Native MLflow Flavors for LLMs.
  • Perform comprehensive model validation leveraging plugins like Giskard and Trubrics.
  • Develop familiarity with MLflow deployment servers tailored for LLMs.
  • Explore scalability and practical use cases of MLflow in real-world scenarios.
  • Hands-on experience with MLflow tracing and tracking capabilities for effective workflow management.
  • Implement automated model validation techniques to ensure robustness and reliability.
  • Conduct performance evaluation using static datasets and functional metrics to optimize machine learning workflows.