Confident AI
O que é Confident AI?
Confident AI é uma plataforma de avaliação de LLM all-in-one projetada para ajudar empresas a justificar a prontidão de produção de suas aplicações de Modelo de Linguagem. Oferece 14+ métricas, gerenciamento de conjunto de dados, monitoramento, integração de feedback humano e funciona com o framework aberto DeepEval.
Como usar Confident AI?
Usar o Confident AI é simples. Realize experimentos LLM, gerencie conjuntos de dados e monitore o desempenho. Integre feedback humano para melhorias automáticas nas aplicações LLM. Comece criando experimentos e avaliando os resultados.
Principais recursos da Confident AI
14+ métricas para experimentos LLM
Gerenciamento de conjunto de dados
Monitoramento de desempenho
Integração de feedback humano
Funciona com o framework DeepEval
Casos de uso da Confident AI
Empresas podem usar o Confident AI para avaliar a prontidão de suas aplicações LLM para implantação em produção.
Perguntas frequentes de Confident AI
O Confident AI pode ser usado por empresas de qualquer tamanho?
Como o Confident AI pode ajudar a melhorar automaticamente aplicativos LLM?
Confident AI Empresa
Confident AI Nome da empresa: Confident AI Inc. .
Preço de Confident AI
Link de preços de Confident AI: https://www.confident-ai.com/pricing
Github de Confident AI
Link de Github de Confident AI: https://github.com/confident-ai/deepeval
Avaliações de Confident AI (0)
Análise de Confident AI
Confident AI Análise de tráfego do site
Tráfego geográfico
Top 5 Regiões
Confident AI Iniciar incorporações
Use os emblemas do site para obter o apoio de sua comunidade para o lançamento do Toolify. Eles são fáceis de incorporar em sua página inicial ou rodapé.
Alternativa de Confident AI
25.41%
100.00%
Escuta de mídias sociais
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)