Generative AI (GenAI) and large language models (LLMs) are disrupting the way we work at a global scale. Snowflake is excited to announce an innovative product lineup that brings our platform’s ease of use, security and governance to the GenAI world.
Snowflake is announcing new product capabilities that are changing how developers build, deliver, distribute and operate their applications.
Snowflake has invested heavily in extending the Data Cloud to AI/ML workloads, starting in 2021 with the introduction of Snowpark, the set of libraries and runtimes in Snowflake that securely deploy and process Python and other popular programming languages.
Generative AI is unlocking new ways to drive innovation, improve productivity and derive more value from data. For organizations to fully capitalize on this potential, it’s critical that everyone — not just those with AI expertise — is able to access and use generative AI.
At Cloudera, we continuously strive to empower organizations to unlock the full potential of their data, catalyzing innovation and driving actionable insights. And so we are thrilled to introduce our latest applied ML prototype (AMP)—a large language model (LLM) chatbot customized with website data using Meta’s Llama2 LLM and Pinecone’s vector database.
The Managed Detection & Response (MDR) industry finds itself in a new era with unprecedented challenges from platform giants and the migration of the attack surface to the cloud, with innovation becoming a requirement for survival. Companies built to provide clients with 24×7 “eyes on glass” now find themselves at the intersection of rapid technological advancements and evolving threat landscapes.
At Next ‘23, Exabeam, Dun & Bradstreet, Optimizely and LiveRamp shared how they use BigQuery and Google Data and AI Cloud for data monetization.
ZoomInfo and Google Cloud recently expanded their partnership to offer ZoomInfo’s Data Cubes for Google Cloud customers via Google Analytics Hub.
Enterprises see embracing AI as a strategic imperative that will enable them to stay relevant in increasingly competitive markets. However, it remains difficult to quickly build these capabilities given the challenges with finding readily available talent and resources to get started rapidly on the AI journey.
