Confident AI
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Confident AI 제품 정보
Confident AI이란 무엇인가요?
Confident AI는 모든 LLM 평가 플랫폼으로, 회사들이 자연어 모델 애플리케이션의 제품 준비 상태를 정당화하는 데 도움을 주도록 설계되었습니다. 14개 이상의 지표, 데이터셋 관리, 모니터링, 인간 피드백 통합을 제공하며 DeepEval 오픈 프레임워크와 작동합니다.
Confident AI을 어떻게 사용하나요?
Confident AI를 사용하는 것은 간단합니다. LLM 실험을 수행하고, 데이터셋을 관리하고, 성능을 모니터링합니다. 인간 피드백을 통합하여 LLM 애플리케이션에서 자동 개선을 도와줍니다. 실험 설정을 시작하고 결과를 평가하세요.
Confident AI의 핵심 기능
LLM 실험을 위한 14개 이상의 지표
데이터셋 관리
성능 모니터링
인간 피드백 통합
DeepEval 프레임워크와 작동
Confident AI의 사용 사례
#1
회사들은 제품 배포를 위한 LLM 애플리케이션의 준비 상태를 평가하기 위해 Confident AI를 사용할 수 있습니다.
Confident AI의 FAQ
Confident AI는 모든 크기의 회사에서 사용할 수 있습니까?
Confident AI가 어떻게 LLM 앱을 자동으로 개선하는 데 도움을 줄까요?
Confident AI 회사
Confident AI 회사 이름: Confident AI Inc. .
Confident AI 가격
Confident AI 가격 링크: https://www.confident-ai.com/pricing
Confident AI Github
Confident AI Github 링크: https://github.com/confident-ai/deepeval
Confident AI 리뷰(0)
5점 중 5점
Confident AI 분석
Confident AI 웹사이트 트래픽 분석
지리적 트래픽
상위 5지역
26.50%
12.49%
10.96%
7.55%
5.59%
May 2024 - Feb 2025 데스크톱 장치만 해당
Confident AI 삽입 실행
웹사이트 배지를 사용하여 커뮤니티에서 Toolify Launch에 대한 지원을 유도하세요. 홈페이지나 바닥글에 쉽게 삽입할 수 있습니다.
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소셜 리스닝
How to Setup DeepEval for Fast, Easy, and Powerful LLM Evaluations
Quickly get started running evals for your LLMs with Open-Source framework DeepEval. This is a quick how-to tutorial on how-to get started using the DeepEval framework. Evaluating LLMs is critical for improving performance and guaranteeing reliability for production LLM applications. At Eigen, we run evals on all our applications to ensure we’re meeting required thresholds and find areas where we can improve. If you’re looking to build production AI applications contact us at eigen.net. Follow along the Quick Introduction in the DeepEval documentation. https://docs.confident-ai.com/docs/getting-started Make sure to create a free account to view your eval results at http://confident-ai.com and run the command “deepeval login” in your terminal to automatically view your results in the web app. Here are the commands to run if you're following along: **Setup Python Virtual Environment** python3 -m venv venv source venv/bin/activate **Install DeepEval** pip install -U deepeval **Set OpenAI API Key as Env. Variable** export OPENAI_API_KEY=yourAPIkey **Create file to run test** touch test_example.py **Paste code below to test_example.py** ____________________ from deepeval import assert_test from deepeval.test_case import LLMTestCase from deepeval.metrics import AnswerRelevancyMetric def test_answer_relevancy(): answer_relevancy_metric = AnswerRelevancyMetric(threshold=0.5) test_case = LLMTestCase( input="What if these shoes don't fit?", actual_output="We offer a 30-day full refund at no extra cost." ) assert_test(test_case, [answer_relevancy_metric]) **Command For Testing First Eval** deepeval test run test_example.py
How to fine-tune an LLM? Getting started
In the first episode of the AI, LLM, and GraphRAG series, Code with Buda takes you on a hands-on journey about how to fine tune an LLM. The topics covered are: 1. How to Generate a Training Dataset 2. How to Fine-tune an LLM to Learn Cypher 3. How to Brainstorm Model Validation Techniques You can check out Marko on Github here: https://github.com/gitbuda If you are interested in the topic, learn more about GraphRAG and how companies are using it with Memgraph: 1. Memgraph Academy - Stay one node ahead: https://memgraph.com/academy/enhancing-ai-with-graph-databases-and-llms 2. Microchip Optimises LLM Chatbot with RAG and a Knowledge Graph: https://memgraph.com/webinars/microchip-optimizes-llm-chatbot-with-rag-and-a-knowledge-graph 3. Optimising Insulin Management: The Role of GraphRAG in Patient Care: https://memgraph.com/webinars/optimizing-insulin-management-the-role-of-graphrag-in-patient-care --- About Memgraph: Memgraph offers a light and powerful graph platform comprising the Memgraph Graph Database, MAGE Library, and Memgraph Lab Visualization. Memgraph is a dynamic, lightweight graph database optimized for analyzing data, relationships, and dependencies quickly and efficiently. It comes with a rich suite of pre-built deep path traversal algorithms and a library of traditional, dynamic, and ML algorithms tailored for advanced graph analysis, making Memgraph an excellent choice in critical decision-making scenarios such as risk assessment (fraud detection, cybersecurity threat analysis, & criminal risk assessment), 360-degree data and network exploration [Identity and Access Management (IAM), Master Data Management (MDM), & Bill of Materials (BOM)], and logistics and network optimization. Website: https://www.memgraph.com Twitter: https://www.twitter.com/memgraphdb LinkedIn: https://www.linkedin.com/company/memgraph Facebook: https://www.facebook.com/memgraph --- 00:00:00 Intro 00:02:51 Star explaining setup and the work so far 00:04:27 Quick unsloth.ai explanation 00:05:09 Continue with the setup and the context 00:26:14 Centralize templates 01:18:49 Play with the base model 02:24:20 Refactor and play with the models 02:46:26 Deepevel setup (some unrelated issues) 03:11:57 Playing with deepevel (docs.confident-ai.com) 03:18:52 Setup the OpenAI 03:28:43 Pause (skip this completely) 03:37:23 Getting deepeval work with local GPU running ollama model (llama3)