Systems | Development | Analytics | API | Testing

Today, we are excited to announce the public preview of Snowflake Cortex Analyst. Cortex Analyst, built using Meta’s Llama and Mistral models, is a fully managed service that provides a conversational interface to interact with structured data in Snowflake. It streamlines the development of intuitive, self-serve analytics applications for business users, while providing industry-leading accuracy.

In today's fast-paced world of media publishing, keeping up with technological advancements and changing consumer preferences is no easy task. Tight budgets, fierce competition and evolving audience behaviors add to the pressure, creating what's often termed the "content crash" — a saturation of content that makes it hard for publishers to stand out. But amidst these challenges, there's a beacon of hope: generative AI.

You’ve built an incredible AI API and are ready to release this functionality to your users. The issue is that you’re not sure exactly how to monetize it. Generally, monetizing APIs is challenging at scale, but monetizing AI APIs can be even more difficult. Some AI APIs may be charged on a “per API call” basis, but many AI APIs require charging users for input and output tokens used within an API call. Others may charge per unique user or API key.

Artificial Intelligence (AI) technology has been transforming industries and our day-to-day lives alike. Its undeniable impact has led to significant effort and investment into making AI more accessible to everyone, everywhere. Open-source AI technology and AI APIs are two examples of our commitment to AI democratization. AI APIs democratize AI by providing access to pre-trained AI models, even for developers without extensive machine learning expertise.

We are excited to announce the launch of our AI Infrastructure Control Plane, designed as a universal operating system for AI infrastructure. With this launch, we make it easier for IT teams and DevOps to gain ultimate control over their AI Infrastructure, manage complex environments, maximize compute utilization, and deliver an optimized self-serve experience for their AI Builders.

We know it feels like all anyone talks about these days is artificial intelligence. Since the launch of ChatGPT in 2022, the professional world has been abuzz with reactions to this game-changing technology. It’s everywhere – and for good reason. Artificial intelligence (AI) and machine learning (ML) tools have been around for a while, but ChatGPT brought AI into the mainstream in ways that hadn’t been seen before.

If 2023 was the year of gen experimentation, 2024 is the year of gen AI implementation. As companies embark on their implementation journey, they need to deal with a host of challenges, like performance, GPU efficiency and LLM risks. These challenges are exacerbated in highly-regulated industries, such as financial services and telecommunication, adding further implementation complexities. Below, we discuss these challenges and present some best practices and solutions to take into consideration.

Generative AI doesn’t just work with unstructured data.

Testing tools in AI and data automation have evolved to offer sophisticated features that ensure the quality of the end product and reduce its time to market. With reports suggesting 50% of manual testing being replaced by automated, these tools can help with various aspects of software testing, including unit testing, performance testing, and security testing. They can be integrated practically anywhere across the CI/CD pipeline for continuous testing and shift left testing.

‍McKinsey’s latest research projects that generative AI could contribute between $2.6 trillion and $4.4 trillion annually across various sectors. Experts have also observed that integrating AI-driven automation, threat detection, and low-code platforms redefines next-gen software development. Whether it is code generation, bug fixing, or even designing a new digital component, generative AI is seeping into all product engineering processes.