Systems | Development | Analytics | API | Testing

As LLMs become central to AI-driven products like copilots and customer support chatbots, data science teams need to ensure the LLM performs well for the use case. The process of LLM evaluation ensures reliability, safety and performance in production AI systems. In this guide, we explore how to approach evaluations across development and production lifecycles, what frameworks to use, and how the integration between open-source MLRun and Evidently AI enables more scalable, structured testing.

Playlists for every mood and occasion. Media recommendations grouped by the most niche theme from your watch history. Sophisticated ad algorithms that optimize pay-per-click ads for the customer experience. Whether you call them digital-native, disruptors, or just tech giants, the likes of Spotify, Netflix, and Amazon have long made uncannily personal experiences a key part of their differentiation or business models.

ClearML Enterprise v3.26 brings powerful upgrades across model deployment, NIMs container deployment, and dataset management – all part of our end-to-end platform for managing and scaling AI in the enterprise.

For data engineers and architects evaluating Spanner ETL solutions, the landscape has become more complex. Organizations must balance the need for sophisticated data transformations with accessibility for non-technical users, all while managing Spanner's unique architectural requirements. The right ETL tool can mean the difference between a successful implementation that delivers on Spanner's promise of global scale and consistency, or a costly project that fails to meet performance expectations.

Data warehouses have long been the gold standard for data analysis and reporting. Developed in the 1980s, the data warehouse concept was introduced to provide an architectural model for data flows from operational systems to decision support systems.

Let’s face it. Data lakehouses are the new normal, but that does not mean they are easy to use. Apache Iceberg gives you version control, schema evolution, and fine-grained partitioning. Trino lets you query it all with blazing speed. When it is time to plug that into your BI tools or analytics pipelines, things often grind to a halt. The problem is not your data or your engine. It is your connector. Architecting a data lakehouse is one thing. Getting it to actually perform is another.

When Microsoft released translytical task flows with write back, Power BI took a big leap. This is just the beginning of Microsoft enhancing write back capabilities within Power BI. Let’s take a look at where this currently stands, and where it is going. Microsoft’s translytical task flows bring native write back to Power BI. That’s a massive shift: Power BI becomes a two-way street—you can finally write data back, not just read it.

Apache Druid has emerged as the go-to solution for organizations requiring lightning-fast analytics on massive datasets. According to the Apache Druid ingestion documentation, this distributed, column-oriented database combines concepts from data warehouses, time-series databases, and search systems to deliver sub-second query performance on trillions of rows.