Systems | Development | Analytics | API | Testing

Choosing The Right LLM: Why Flexibility Is Key

In this Cortex AI roundtable, Snowflake's Arun Agarwal, Yusuf Ozuysal, and James Cha-Earley highlight why AI model flexibility is crucial for real-world applications. Recognizing that no single large language model excels at everything — coding, reasoning, extraction, or conversation — Snowflake Cortex AI brings the strengths of different LLMs together by integrating leading LLMs like OpenAI, Mistral, Anthropic, and Meta into one secure platform.

Hands-on with Apache Iceberg

As organizations move beyond traditional data lakes and warehouses, the need for scalable, performant, and reliable table formats has never been greater. Apache Iceberg is quickly becoming the go-to open-source table format for the modern data stack—bringing the features of SQL tables to the data lake with schema evolution, time travel, partitioning, and ACID transactions. In this exclusive Hevo Learning Lab session, join Alex Merced—Senior Developer Advocate at Dremio and co-author of Apache Iceberg.

Securing, Observing, and Governing MCP Servers with Kong AI Gateway

The explosion of AI-native applications is upon us. With each new week, massive innovations are being made in how AI-centric applications are being built. There are a variety of tools developers need to consider, be it supplying live contextual data via the Model Context Protocol (MCP) or leveraging the new Agent2Agent Protocol (A2A) to standardize how their agentic applications will communicate. The modern AI application can include communication between many different entities, including.

Maximizing GPU Efficiency with ClearML's Unified Memory Technology

AI builders deploying models into production focus on ensuring well-performing models are available for users. Once the model is live, the focus shifts to optimizing GPU usage for efficient deployment. While GPU machines offer the best performance, they are costly to run and frequently remain underutilized.

Test Parameterization Techniques

Test parameterization allows testers to run the same test case with multiple sets of input data, eliminating the need for duplicate test cases. Instead of hardcoding values, testers define variables that can be dynamically replaced during execution. This approach is essential for testing different scenarios efficiently, such as validating multiple user credentials or input combinations without creating separate test cases for each variation.