SwaggerHub - Amazon API Gateway
This is a video showing SwaggerHub working with the Amazon API Gateway. In this call we walks through the advantages of using the platforms together with a demonstration of the integration.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
The Rise of AI in FP&A: How insightsoftware Empowers Your Team
Despite the transformative potential of AI, many financial planning and analysis (FP&A) teams are hesitating, waiting for this emerging technology to mature before investing. According to a recent Gartner report, a staggering 61% of finance organizations haven’t yet adopted AI. Finance has always been considered risk averse, so it is perhaps unsurprising to see that AI adoption in finance significantly lags other departments.
LLM Validation and Evaluation
LLM evaluation is the process of assessing the performance and capabilities of LLMs. This helps determine how well the model understands and generates language, ensuring that it meets the specific needs of applications. There are multiple ways to perform LLM evaluation, each with different advantages. In this blog post, we explain the role of LLM evaluation in AI lifecycles and the different types of LLM evaluation methods. In the end, we show a demo of a chatbot that was developed with crowdsourcing.
Event-Driven Microservices in Banking and Fraud Detection | Designing Event-Driven Microservices
How do we know whether Event-Driven Microservices are the right solution? This is the question that Tributary Bank faced when they looked at modernizing their old fraud-detection system. They were faced with many challenges, including scalability, reliability, and security. Some members of their team felt that switching to an event-driven microservice architecture would be the magic bullet that would solve all of their problems. But is there any such thing as a magic bullet? Let's take a look at the types of decisions Tributary Bank had to make as they started down this path.
Unlocking enhanced LLM capabilities with RAG in BigQuery
A look at how to augment LLM results with retrieval augemented generation (RAG) and vector search, directly from BigQuery.
SmartBear Introduces HaloAI, Transforming Software Development and Test Productivity with AI Technology
SmartBear HaloAI is already delivering results in beta: shatters test times by 98% in the first 2 weeks with Zephyr Scale; automates half of QA tests, saving 20 hours per regression cycle.
Best LLM Inference Engines and Servers to Deploy LLMs in Production
AI applications that produce human-like text, such as chatbots, virtual assistants, language translation, text generation, and more, are built on top of Large Language Models (LLMs). If you are deploying LLMs in production-grade applications, you might have faced some of the performance challenges with running these models. You might have also considered optimizing your deployment with an LLM inference engine or server.
Snowflake Arctic Cookbook Series: Instruction-Tuning Arctic
On April 24, we released Snowflake Arctic with a key goal in mind: to be truly open. In line with that goal, the Snowflake AI Research team is writing a series of cookbooks to describe how to pretrain, fine-tune, evaluate, and serve large-scale mixture-of-experts (MoEs) such as Arctic.
Practical Enterprise AI: Common Roadblocks and How to Avoid Them
Come learn what to keep in mind when implementing AI-powered applications and effective strategies for scalable, practical generative AI.
Contributing to Apache Kafka: How to Write a KIP
I’m brand new to writing KIPs (Kafka Improvement Proposals). I’ve written two so far, and my hands sweat every time I hit send on an email with ‘ KIP’ in the title. But I’ve also learned a lot from the process: about Apache Kafka internals, the process of writing KIPs, the Kafka community, and the most important motivation for developing software: our end users. What did I actually write? Let’s review KIP-941 and KIP-1020.