Dasha Maliugina

We highlight seven RAG benchmarks that help measure and compare how well different LLMs handle core RAG challenges like large context windows, grounded reasoning, and using retrieved evidence effectively.

We put together 7 examples of how top companies like Asana and GitHub run LLM evaluations. They share how they approach the task, what methods and metrics they use, what they test for, and their learnings along the way.

LLM safety benchmarks help to ensure models are robust and reliable. In this blog, we highlight 10 key safety and bias LLM benchmarks that help assess and improve LLM reliability.

RAG helps make LLM systems more accurate and reliable. We compiled 10 real-world examples of how companies use RAG to improve customer experience, automate routine tasks, and improve productivity.

In this guide, we’ll discuss key AI regulations, such as the EU AI Act and the Blueprint for AI Bill of Rights, and explain what they mean for teams building AI-powered products.

Real-world examples of LLM hallucinations and other failures that can occur in LLM-powered products in the wild, such as prompt injection and out-of-scope usage scenarios.

From being biased to making things up, there are numerous instances where we’ve seen AI going wrong. In this post, we’ll explore ten notable AI failures when the technology didn’t perform as expected.

How do companies use LLMs in production? We compiled 45 real-world LLM applications from companies that share their learnings from building LLM systems.

Our CTO Emeli Dral was an instructor for the ML Monitoring module of MLOps Zoomcamp 2024, a free MLOps course. We summarized the ML monitoring course notes and linked to all the practical videos.

We put together the most interesting conferences on AI, Machine Learning, and Data Science in 2025. And the best part? Some of them are free to attend or publish the content after the event.

Looking for MLOps courses to attend in 2023? We put together five great online MLOps courses for data scientists and ML engineers. They are free to join or publish their content for everyone to access without a fee.

In this blog, we recap the Ask-Me-Anything session with Lina Weichbrodt. We chatted about ML monitoring and debugging, adopting LLMs, and the challenges of being a freelance ML engineer.

In this blog, we recap the Ask-Me-Anything session with Stefan Krawczyk. We chatted about how to build an ML platform and what data science teams do wrong about ML dataflows.

How to approach building an internal ML platform if you’re not Google? We put together stories from 10 companies that shared their platforms’ design and learnings along the way.

In this blog, we recap the Ask-Me-Anything session with Neal Lathia. We chatted about career paths of an ML Engineer, building and expanding ML teams, Monzo’s ML stack, and 2023 ML trends.

Want to know how companies with top engineering teams do machine learning? We put together a list of the best machine learning blogs from companies that share specific ML use cases, lessons learned from building ML platforms, and insights into the tech they use.

In this blog, we recap the Ask-Me-Anything session with Ben Wilson. We chatted about AutoML use cases, deploying ML models to production, and how one can learn about ML engineering.

In this blog, we recap the Ask-Me-Anything session with Rick Lamers, where we chatted about the evolution of orchestration tools, their place within the MLOps landscape, the future of data pipelines, and building an open-source project amidst the economic crisis.

Now that Hacktoberfest 2022 is over, it’s time to celebrate our contributors, look back at what we’ve achieved together, and share what we’ve learned during this month of giving back to the community through contributing to open source.

In this blog, we recap the Ask-Me-Anything session with Jacopo Tagliabue, where we chatted about ML at a reasonable scale, testing RecSys, MLOps anti-patterns, what’s hot in DataOps, fundamentals in MLOps, and more.

In this blog, we recap the Ask-Me-Anything session with Bozhao Yu and Sean Sheng, where we chatted about why deploying a model is hard, beginner mistakes and how to avoid them, the challenges of building an open-source product, and BentoML’s roadmap.

In this blog, we recap Ask-Me-Anything session with Doris Xin, that covered the roles of Data Scientists and Data Engineers in an ML cycle, automation, MLOps tooling, bridging the gap between development and production, and more.

We recap Ask-Me-Anything session with Fabiana Clemente, which covered synthetic data, its quality, beginner mistakes in data generation, the data-centric approach, and how well companies are doing in getting there.

We ran an experiment to help build an intuition on how popular drift detection methods behave. In this blog, we share the key takeaways and the code to run the tests on your data.

In this blog we recap Ask-Me-Anything session with Matt Squire, that covered MLOps maturity and future, how MLOps fits in data-centric AI, and why open-source wins.

In this blog we recap Ask-Me-Anything session with Hamza Tahir, that covered MLOps trends and tools, the future of real-time ML, and building an open-source startup.

In this blog we recap the second Evidently Community Call that covers the recent feature updates in our open-source ML monitoring tool.

In this blog we recap Ask-Me-Anything session with Alexey Grigorev, that covered all things production machine learning, from tools to workflow, and even a bit on community building.