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.
Learn how to use ZenML pipelines and BentoML to easily deploy machine learning models, be it on local or cloud environments. We will show you how to train a model using ZenML, package it with BentoML, and deploy it to a local machine or cloud provider. By the end of this post, you will have a better understanding of how to streamline the deployment of your machine learning models using ZenML and BentoML.
Test automation is tedious enough with traditional software engineering, but machine learning complexities can make it even less appealing. Using Deepchecks with ZenML pipelines can get you started as quickly as it takes you to read this article.
This week I spoke with Emeli Dral, co-founder and CTO of Evidently, an open-source tool tackling the problem of monitoring of models and data for machine learning. We discussed the challenges around building a tool that is both straightforward to use while also customizable and powerful.
ZenML combines forces with Great Expectations to add data validation to the list of continuous processes automated with MLOps. Discover why data validation is an important part of MLOps and try the new integration with a hands-on tutorial.
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