Learn how to integrate Striim with BigQuery ML for real-time data processing in machine learning.

Now you can create and manage your control plane on-prem or on-cloud, regardless of where your data and compute are. We recently announced extensive new orchestration,scheduling, and compute management capabilities for optimizing control of enterprise AI & ML. Machine learning and DevOps practitioners can now fully utilize GPUs for maximal usage with minimal costs.

Many developers and enterprises looking to use machine learning (ML) to generate insights from data get bogged down by operational complexity. We have been making it easier and faster to build and manage ML models with Snowpark ML, the Python library and underlying infrastructure for end-to-end ML workflows in Snowflake.

Across the industry, organizations are attempting to find ways to implement generative AI in their business and operations. But doing so requires significant engineering, quality data and overcoming risks. In this blog post, we show all the elements and practices you need to to take to productize LLMs and generative AI. You can watch the full talk this blog post is based on, which took place at ODSC West 2023, here.

Sense is a talent engagement platform that improves recruitment processes with automation, AI and personalization. Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization, including a large number of data and data science professionals. This includes a data team, an analytics team, DevOps, AI/ML, and a data science team. The AI/Ml team is made up of ML engineers, data scientists and backend product engineers.

Sense is a talent engagement company whose platform improves the recruitment processes with automation, AI and personalization. Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization including a large number of data and AI professionals. This includes a data team, an analytics team, DevOps, AI/ML, and a data science team. The AI/Ml team is made up of ML engineers, data scientists and backend product engineers.

BigQuery ML’s approach to feature engineering, modularity, allows for easy reuse of feature pipelines within BigQuery, and portability to Vertex AI.

Adopting and deploying Generative AI within your organization is pivotal to driving innovation and outsmarting the competition while at the same time, creating efficiency, productivity, and sustainable growth. Acknowledging that AI adoption is not a one-size-fits-all process, each organization will have its unique set of use cases, challenges, objectives, and resources.

Historically, only a few AI experts within an organization could develop insights using machine learning (ML) and predictive analytics. Yet in this new wave of AI, democratizing ML to more data teams is crucial—and for Snowflake SQL users, it’s now a reality.

2023 was the year of generative AI, with applications like ChatGPT, Bard and others becoming so mainstream we almost forgot what it was like to live in a world without them. Yet despite its seemingly revolutionary capabilities, it's important to remember that Generative AI is an extension of “traditional AI”, which in itself is a step in the digital transformation revolution.