Snowflake's Cortex AISQL is a production SQL engine that integrates native semantic operations directly into SQL. This integration allows users to write declarative queries that combine relational operations with semantic reasoning, enabling them to query both structured and unstructured data effortlessly. However, making semantic operations efficient at production scale poses fundamental challenges. Semantic operations are more expensive than traditional SQL operations, possess distinct latency and throughput characteristics, and their cost and selectivity are unknown during query compilation. Furthermore, existing query engines are not designed to optimize semantic operations. The AISQL query execution engine addresses these challenges through three novel techniques informed by production deployment data from Snowflake customers. First, AI-aware query optimization treats AI inference cost as a first-class optimization objective, reasoning about large language model (LLM) cost directly during query planning to achieve 2-8$\times$ speedups. Second, adaptive model cascades reduce inference costs by routing most rows through a fast proxy model while escalating uncertain cases to a powerful oracle model, achieving 2-6$\times$ speedups while maintaining 90-95% of oracle model quality. Third, semantic join query rewriting lowers the quadratic time complexity of join operations to linear through reformulation as multi-label classification tasks, achieving 15-70$\times$ speedups with often improved prediction quality. AISQL is deployed in production at Snowflake, where it powers diverse customer workloads across analytics, search, and content understanding.

We are looking for a Director-Level Product Management Leader to join our rapidly growing team! Our product managers oversee the product functionality and design for our flexible, scalable, and easy-to-use data cloud. We need someone who has the breadth of skills and experience that will make our product loved by customers - someone who can develop a deep understanding of the Snowflake product, a deep empathy for customers and a clear vision of how the product should evolve. Specifically, the person hired for this role will serve as the business owner for our data science and machine learning workload. The role entails shaping the product roadmap for this workload with a strong focus on execution, all while working in a cross-functional way with teams across the company to make Snowflake and our customers successful for this workload, and it includes working with our ecosystem of technology and solution partners to align on joint technology and business integration roadmaps in this space.

We're at the forefront of the data revolution, committed to building the world's greatest data and applications platform. Our'get it done' culture allows everyone at Snowflake to have an equal opportunity to innovate on new ideas, create work with a lasting impact, and excel in a culture of collaboration. In this role, you will work closely with Sales/GTM, Finance, Security, and IT stakeholders to build best in class datasets supporting analytics across the company. This is a high-impact role that will also help shape the future of Snowflake products and services.

In this article, we will discuss the 10 best machine learning stocks to invest in. If you want to explore similar stocks, you can also take a look at 5 Best Machine Learning Stocks To Invest In. According to an industry analysis report by Fortune Business Insights, the global machine learning industry was valued at $15.4 billion in 2021 and is expected to reach a value of over $21 billion in 2022. The machine learning industry is expected to grow at a compound annual growth rate of 38.8% from 2022 through 2029 and reach a value of $210 billion by the end of 2029. One of the major drivers of this growth is the increasing adoption of machine learning in a variety of industries including technology, healthcare, manufacturing, automotive, retail, advertising, automation, defense, and financial services among others.