LLM Metrics and Trade-Offs E-Learning Training

The LLM Metrics and Trade-Offs: LearningKit helps you critically evaluate, compare and select Large Language Models for real-world AI applications. This LearningKit is designed for AI practitioners, data scientists, IT professionals and decision-makers who need to understand which LLM is the best fit for a specific use case.

Demo LLM Metrics and Trade-Offs Training

The training covers essential evaluation metrics such as BLEU, ROUGE, F1 and HELM. You will also learn how to navigate technical and business trade-offs related to accuracy, performance, latency, throughput, scalability, model size, resource usage and cost. Important ethical topics are included as well, such as bias, fairness, privacy, compliance and responsible AI use.

By the end of this LearningKit, you will be able to assess LLMs based on technical quality, operational constraints, ethical risks and business value. This helps you decide whether a public model, in-house model or specific model configuration is the right choice for your organization and application.

What will you learn?

• Understand and apply key LLM evaluation metrics
• Work with metrics such as HELM, BLEU, ROUGE and F1
• Distinguish between intrinsic and extrinsic evaluation methods
• Balance accuracy, model size and resource consumption
• Evaluate latency, throughput and scalability for real-world applications
• Assess cost and cloud resource usage for LLM deployment
• Compare public LLMs and in-house LLMs
• Identify and evaluate bias, fairness and ethical risks
• Align LLM selection with business requirements, compliance and security
• Create a structured LLM selection strategy

Who should attend?

This LearningKit is suitable for:

• AI practitioners
• Data scientists
• Machine learning engineers
• AI developers
• Business intelligence professionals
• IT managers and decision-makers
• Product owners of AI solutions
• Compliance and security professionals
• Organizations evaluating, comparing or implementing LLMs

This LearningKit with more than 9 hours of learning is divided into three tracks:

Course Outcome

Large Language Models and Key Metrics
In this course, you will learn how Large Language Models work and which metrics are used to evaluate their performance. You will explore HELM, BLEU, ROUGE and F1 scores and learn to distinguish between intrinsic and extrinsic evaluation methods. You will also review practical examples of public and in-house models.

LLM Accuracy, Performance, and Trade-Offs
This course focuses on balancing accuracy, performance, model size and computational resources. You will learn why larger models often perform better on complex tasks, while also requiring more resources. You will also learn how to evaluate trade-offs between model size, accuracy and resource consumption.

LLM Latency, Throughput, and Scalability
In this course, you will learn how latency, throughput and scalability affect LLM performance in real-world applications. You will explore how models handle heavy workloads, high-traffic scenarios, large-scale content generation and vast datasets.

LLM Cost Efficiency, Model Size, and Resource Optimization
This course covers cost efficiency and resource optimization when deploying LLMs. You will learn how to evaluate the computational costs of small, medium and large models in cloud environments such as AWS and Azure. You will also explore fine-tuning, resource demands and cost-performance trade-offs.

LLM Bias, Fairness, and Ethical Considerations
This course covers bias, fairness and ethical considerations in Large Language Models. You will learn how LLMs can unintentionally learn and reproduce data biases, and how to identify and evaluate output bias and fairness. Privacy, regulation and trustworthy AI are also addressed.

Selecting the Right LLM
In this course, you will learn how to select the right LLM for business tasks such as content creation, customer support and other AI applications. You will create frameworks for LLM selection based on metrics, organizational needs, compliance, security, ethical concerns and technical trade-offs.

LLM Metrics and Trade-Offs: Large Language Models and Key Metrics

Course: 1 Hour, 38 Minutes

• Course Overview
• Large Language Model Architecture
• Large Language Model Concepts
• Large Language Model Principles
• Large Language Model Types
• Large Language Model Selection Criteria
• Large Language Model Metrics
• Large Language Model Evaluation Methods
• Large Language Model Metrics Alignment
• Exploring Large Language Model Design
• Public Large Language Models
• In-House Large Language Models
• Evaluating Large Language Models
• Using Large Language Model Intrinsic Metrics
• Course Summary

LLM Metrics and Trade-Offs: LLM Accuracy, Performance, and Trade-Offs

Course: 1 Hour, 53 Minutes

• Course Overview
• Large Language Model Precision
• Large Language Model Recall
• Large Language Model F1 Scores
• Examining Large Language Model Precision
• Analyzing Large Language Model Recall
• Evaluating Large Language Model F1 Scores
• Large Language Model Accuracy
• Large Language Model Size
• Large Language Model Computational Cost
• Large Language Model Perplexity
• Large Language Model Selection Best Practices
• Large Language Model Fine-Tuning
• Fine-Tuning Large Language Models
• Comparing Metrics of Fine-Tuned Large Language Models
• Large Language Model Performance Metrics
• Large Language Model Overall Output Quality
• Course Summary

LLM Metrics and Trade-Offs: LLM Latency, Throughput, and Scalability

Course: 1 Hour, 43 Minutes

• Course Overview
• Large Language Model (LLM) Latency
• Large Language Model (LLM) Throughput
• Large Language Model (LLM) Scalability
• Measuring LLM Latency
• Measuring Large Model Latency
• Selecting Large Language Models
• Large Language Model and High Traffic
• Evaluating Large Language Models & Distributed Text
• Scaling Large Language Models
• Large Language Models & Time Sensitive Applications
• Large Language Models & Performance Balancing
• Large Language Models & Real-time Demands
• Ethical Considerations in LLM Deployment
• Optimizing LLM Hyperparameters to Reduce Costs
• Course Summary

LLM Metrics and Trade-Offs: LLM Cost Efficiency, Model Size, and Resource Optimization

Course: 1 Hour, 15 Minutes

• Course Overview
• Small LLM Computational Costs
• Large Language Model Cost Optimization
• Large Language Model Cost Analysis
• LLM In-House Cost Efficiency
• LLM Public Cost Efficiency
• Large Language Model Optimization
• Optimizing Large Language Models
• LLM Cost, Size, and Performance Trade-Offs
• LLM Scaling Strategies
• LLM Model Size Implications
• Course Summary

LLM Metrics and Trade-Offs: LLM Bias, Fairness, and Ethical Considerations

Course: 1 Hour, 24 Minutes

• Course Overview
• Bias in LLMs
• LLM Output Fairness
• Performing LLM Sentiment Analysis
• LLM Limitations
• Compliance Issues for LLMs
• Ethical Concerns for LLMs in Sensitive Applications
• LLM Bias Mitigation Techniques
• Fine-Tuning LLMs for Bias Mitigation
• LLM Selection
• AI Ethics in LLM Advancements
• Exploring AI Ethics in Long-Term LLM Development
• Course Summary

LLM Metrics and Trade-Offs: Selecting the Right LLM

Course: 1 Hour, 37 Minutes

• Course Overview
• LLM Use Cases
• Selecting LLMs
• Framework Design for LLM Selection
• Comparing LLMs
• LLM Performance, Security, and Compliance
• Comparing Public and In-House LLMs
• LLM Ethical Issues
• Evaluating LLM Ethics
• LLM Quality Assessments
• Comparing LLM Outputs
• LLM Selection Strategies
• Generating LLM Descriptions
• Course Summary

Assessment

• Final Exam: LLM Metrics and Trade-Offs