Exploring Opik: A First Look at Automating LLM Scores and Online Evaluation скачать в хорошем качестве

Exploring Opik: A First Look at Automating LLM Scores and Online Evaluation

Tired of the slow, subjective, and inconsistent process of manually evaluating your LLM outputs? In this 15-minute video, we dive into the Opik dashboard to explore how you can automate your LLM scores and streamline your entire evaluation workflow. Automated evaluation is crucial for building reliable and high-performing AI applications. We'll take a hands-on look at Opik's "Online evaluation" feature, which allows you to set up predefined rules to score LLM outputs in real-time. This ensures consistent performance assessment and helps you iterate on your models and prompts faster than ever. ✨ *In this video, you'll learn:* Why manual LLM evaluation is a major bottleneck. An introduction to the Opik platform for LLM optimization. A step-by-step walkthrough of the "Online evaluation" dashboard. How to create automated rules to score responses based on set criteria. The benefits of automating your scores for efficiency and consistency. This guide is perfect for ML Engineers, AI Developers, Data Scientists, and anyone working on production-level LLM applications who wants to improve their testing and evaluation process. --- ⏰ *Timestamps:* 00:00 - The Problem with Manual LLM Evals 01:30 - Introducing Opik Demo Optimizer 03:00 - Dashboard Overview: Traces, Spans, and Metrics 04:15 - Deep Dive into the "Online Evaluation" Tab 06:30 - How to Create Your First Automated Rule 09:45 - Applying the Rule to LLM Outputs in Real-Time 12:00 - Analyzing the Automated Scores 14:00 - Final Thoughts & Benefits #LLMOps #AIEvaluation #MachineLearning #DeveloperTools #Opik #LLM LLM Evaluation, Automate LLM Evals, Opik, Opik AI, Opik Demo, Online Evaluation, MLOps, LLMOps, Machine Learning Operations, AI Developer Tools, Automate LLM Scores, LLM Performance, LLM Testing, Real-time LLM Scoring, Prompt Engineering, AI Model Evaluation, Natural Language Processing, LLM Observability, How to evaluate LLMs, LLM Output Validation, AI Application Development