Google Extends Vertex with More GenAI Features
Generative AI is taking the world by storm, as organizations discover the myriad ways it can be used to serve and entice customers. With today’s enhancements to Vertex AI, Google Cloud is giving its customers more GenAI capabilities to choose from.
The pace of adoption of GenAI and large language models (LLMs) has been nothing but astonishing since OpenAI rocked the world with the launch of ChatGPT nine months ago. It also put Google in the unconventional position of playing catchup with OpenAI and its partner Microsoft–which is ironic since Google developed the core transformer model technology underpinning LLMs.
Google Cloud has narrowed the gap considerably since it started adding GenAI and LLM capabilities to Vertex AI, its flagship product for enterprise AI. According to June Yang, Google Cloud’s vice president of Cloud AI and industry solutions, the number of GenAI customer accounts in Vertex AI has grown by more than 15x in just the past quarter.
“And the GenAI products we’re seeing on Google Cloud Platform has grown by over 150 times,” Yang added during a press conference last week. “Really, just a staggering amount of growth. We’re very happy to see this type of demand.”
Newcomers to Vertex AI will have a veritable smorgasbord of LLMs and image-generating models to choose from, as the company now boasts more than 100 large foundational models in its Model Garden. PaLM is the hometown favorite, as a Google product, but you can also find Llama2, made by crosstown rival Meta, wandering the Garden. Claude 2, a foundation model developed Anthropic, is another third-party model now available to Vertex users.
An upgrade to PaLM will expand the input length by more than 4x, to 32,000 tokens. That will make it easier for customers to input longer documents and pieces of conversation into Google’s biggest foundational model, Yang says. “One of the key requests we’ve heard from customers is they want a bigger context lens windows so they can input more data,” Yang said.
PaLM also boasts full compatibility with 38 languages, including Arabic, Chinese, Japanese, German, and Spanish, among others. There are more than 100 more languages in private preview for PaLM, Yang added. Codey, a text-to-code model developed by Google, can boast up to 25% better code generation, Yang said. And Imagen, Google’s model for image generation, also boasts better quality output.
In addition to increasing the breadth and quality of foundation models, Google Cloud also announced that Vertex AI Search and Conversation is now generally available.
Vertex AI Search and Conversation utilizes vector search capabilities under the covers to provide a better search experience than keyword-search alone can provide, but without requiring advanced AI skills to integrate the search engine into customer environments. It also brings features like multi-turn search, which provides a more streamlined conversation, and conversation and search summarization.
“Think about this as Google Search for your business data,” Yang said. “You may have seen Google Search’s generative experiences from a consumer side. With Vertex AI Search, you can now offer the same generative AI experiences to your employes, partners, and customers, with built-in low code, multi-model and multi-language capabilities.”
Google Cloud also announced the general availability of Vertex AI extensions, which is set of developer tools within Vertex AI Search and Conversations that connect models to APIs to take action on real-time data.
“With extensions, a developer can now build their own extension or leverage an extension built either by Google or our partners,” Yang said. “And developers can use these extensions to build powerful GenAI application, like digital assistant, search engines, automated workflow, and more.”
The company said it’s developing pre-built extensions that connect Vertex AI to Google Cloud databases services like BigQuery and AlloyDB, the company said. It’s also committed to connecting to third-party NoSQL databases from MongoDB, Redis, and DataStax .
Google Cloud made the Vertex AI announcements at Google Cloud Next, a multi-day conference that’s expected to attract 20,000 people to San Francisco this week.
September 21, 2023
- RelationalAI to Host ‘The Promise of AI and Relational Knowledge Graphs’ Webinar
- dbt Labs to Reveal Exciting New Features and Products at Coalesce 2023
- ibi Unleashes the Power of Legacy Systems with Open Data Hub for Mainframe
- Dataiku Partners with the Brilliant Club to Boost University Access for Disadvantaged Students
- Oracle Adds AI Capabilities to Oracle Analytics Cloud
- SingleStore Announces New Real-Time Generative AI Event, Features and Partnerships
- Timeplus Open Sources Its Powerful Streaming Analytics Engine for Developers Globally
- MongoDB Launches Atlas for Manufacturing and Automotive
- Cisco to Acquire Splunk, to Help Make Organizations More Secure and Resilient in an AI-Powered World
- Oracle Continues MySQL HeatWave Innovation with Vector Store and New Generative AI Capabilities
- Galileo Introduces LLM Studio: Enhanced Modules to Fine-tune, Prompt, and Monitor Generative AI Outputs
- Presto Foundation Announces Contributions by IBM and Uber that Reinforce Commitment to the Presto Open Source Community
- SAP Store Now Features BioData’s Labguru: A Comprehensive Life Science Data Management Solution
- TruEra Launches Comprehensive AI Tool with Enhanced Capabilities to Address LLM Risks and Enhance Development
- Airbyte Users Create More than 1,500 Data Integration Connectors with No-Code Builder
- Privacera’s Integration with Collibra Creates End-to-End Data Governance
September 20, 2023
- Telmai Unveils New AI-Driven Data Observability Features in Latest Release
- Starburst Expands Data Lake Analytics Platform to Support Every Stage of the Cloud Journey
- Prominent Authors File Class Action Against OpenAI Alleging Copyright Infringement Through LLMs
- New MITRE-Harris Poll Reveals Decline in Public Trust of AI Technologies from Last Year
Most Read Features
- Databricks Versus Snowflake: Comparing Data Giants
- Data Mesh Vs. Data Fabric: Understanding the Differences
- Big Data File Formats Demystified
- PayPal Feeds the DL Beast with Huge Vault of Fraud Data
- What Is MosaicML, and Why Is Databricks Buying It For $1.3B?
- How Generative AI Is Transforming the Call Center Market
- What Does ChatGPT for Your Enterprise Really Mean?
- Hallucinations, Plagiarism, and ChatGPT
- Google Claims Its TPU v4 Outperforms Nvidia A100
- Duet AI Goes Everywhere in Google’s Cloud
- More Features…
Most Read News In Brief
- Mathematica Helps Crack Zodiac Killer’s Code
- GenAI Debuts Atop Gartner’s 2023 Hype Cycle
- Python Finally Comes to Excel
- Is ChatGPT Getting Dumber?
- Top 10 In-Demand GenAI Skills
- Neo4j Finds the Vector for Graph-LLM Integration
- DSPy Puts ‘Programming Over Prompting’ in AI Model Development
- Starburst Brings Dataframes Into Trino Platform
- Data Fabric Firm Denodo Raises $336 Million
- Anaconda’s Commercial Fee Is Paying Off, CEO Says
- More News In Brief…
Most Read This Just In
- Salesforce and Snowflake Make Data Sharing-Based Integration Generally Available
- Salesforce and AWS Deepen Generative AI Partnership with Harmonized Customer Profile Offerings
- Dataiku Announces Breakthroughs in Generative AI Enterprise Applications, Safety, and Tooling
- NetApp and Google Cloud Introduce Managed Storage Service to Revolutionize Enterprise Workloads in the Cloud
- Google Cloud Unveils New Generative AI Innovations and Partnerships at Next ’23
- Groq Achieves Doubling of Llama-2 70B LLM Inference Performance in 3 Weeks
- Quantum Announces Next-Gen Cold Data Storage Solutions, Simplifying Cloud Integration
- Habu Unveils Generative AI Capabilities on Google Cloud to Create Value From Data Clean Rooms
- IBM to Participate in $235M Series D Funding Round of Hugging Face
- More This Just In…