As Large Language Models (LLMs) revolutionize software development, the challenge of ensuring their reliable performance becomes increasingly crucial. This comprehensive guide explores the landscape of LLM evaluation, from specialized platforms like Langfuse and LangSmith to cloud provider solutions from AWS, Google Cloud, and Azure. Learn how to implement effective evaluation strategies, automate testing pipelines, and choose the right tools for your specific needs. Whether you're just starting with manual evaluations or ready to build sophisticated automated pipelines, discover how to gain confidence in your LLM applications through robust evaluation practices.
We've open-sourced our new dashboard to unify the experience for OSS and cloud users, although some features are initially CLI-only. This launch enhances onboarding and simplifies maintenance. Cloud users will see no change, while OSS users can enjoy a new interface and DAG visualizer. We encourage community contributions to help us expand and refine this dashboard further, looking forward to integrating more features soon.
Taking large language models (LLMs) into production is no small task. It's a complex process, often misunderstood, and something we’d like to delve into today.
A critical security vulnerability has been identified in ZenML versions prior to 0.46.7. This vulnerability potentially allows unauthorized users to take ownership of ZenML accounts through the user activation feature.
Seamlessly automating the journey from training to production, ZenML's new NLP project template offers a comprehensive MLOps solution for teams deploying Huggingface models to AWS Sagemaker endpoints. With its focus on reproducibility, scalability, and best practices, the template simplifies the integration of NLP models into workflows, complete with lineage tracking and various deployment options.
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