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Foundation Models Empowered By New AI and Data Platform

By May 23, 2023No Comments
Foundation Models
A new platform unveiled for foundation models and generative AI offers a studio, data store, and governance toolkit.


IBM has unveiled a new AI and data platform called IBM watsonx to power next-generation foundation models for business. The platform is designed to empower enterprises to scale and accelerate the impact of advanced AI using trusted data.  Foundation models are trained on a broad set of unlabeled data that can be used for different tasks, with minimal fine-tuning.

In today’s AI-driven landscape, businesses require a comprehensive technology stack that facilitates training, fine-tuning, and deployment of AI models, including machine learning capabilities and foundation models, across their organization, with a focus on speed, governance, and data integrity. The watsonx platform addresses these needs by providing a unified solution that can run seamlessly across any cloud environment.

With watsonx, the company offers an AI development studio that provides access to curated and trained foundational models, as well as open-source models. The platform includes a data store for gathering and cleansing training and tuning data, along with a governance toolkit that empowers businesses to seamlessly implement AI workflows. This end-to-end AI workflow simplifies the adaptation and scaling of AI capabilities for enterprises.

“With the development of foundation models, AI for business is more powerful than ever,” said Arvind Krishna, Chairman and CEO of IBM. “Foundation models make deploying AI significantly more scalable, affordable, and efficient. We built IBM watsonx for the needs of enterprises, so that clients can be more than just users, they can become AI advantaged. With IBM watsonx, clients can quickly train and deploy custom AI capabilities across their entire business, all while retaining full control of their data.”

The watsonx platform consists of three primary product sets:

1) Anticipated for general availability in July 2023, this next-generation enterprise studio serves as a comprehensive platform for AI builders. It enables them to train, test, fine-tune, and deploy both traditional machine learning and innovative generative AI capabilities utilizing foundational models through an intuitive, open user interface.

The AI studio provides a diverse range of foundation models, along with training and tuning tools, and cost-effective infrastructure to support the complete data and AI lifecycle. This encompasses data preparation, model development, deployment, and monitoring.

The studio also includes a library of foundation models curated and trained by IBM, leveraging a large dataset of enterprise data subjected to rigorous filtering, cleansing, and auditable data lineage. These models extend beyond language and cover various modalities such as code, time-series data, tabular data, geospatial data, and IT events data. A select set of foundation models will be available in a beta tech preview to select clients.

Model categories include fm.code, which automates code generation through a natural-language interface; fm.NLP featuring large language models customized for specific or industry domains using curated data to mitigate bias effectively; and fm.geospatial, a model built on climate and remote sensing data to aid organizations in understanding and planning for environmental changes impacting their operations.

As part of a partnership between IBM and Hugging Face, the studio will leverage Hugging Face’s open-source libraries, offering access to thousands of open models and datasets.

“To benefit from the latest AI capabilities, enterprises want to build on open-source machine learning: open-source models trained on accessible datasets that can run within a secure environment with compliance and proper data governance,” said Clem Delangue, Co-founder and CEO of Hugging Face.

2) This data store, built on open lakehouse architecture and optimized for governed data and AI workloads, is also expected to be generally available in July 2023. It allows users to access robust data through a unified entry point and utilize multiple query engines to uncover valuable insights. It also provides built-in governance tools and seamless integrations with existing databases and tools.

3) watsonx.governance: This AI governance toolkit, expected to be generally available later this year, streamlines trusted AI workflows. It automates governance processes, protects customer privacy, detects model bias and drift, and helps organizations meet their ethical standards.

The watsonx platform enables clients to meet their organization’s needs in key areas such as customer and employee interactions, business workflow automation, IT process automation, threat protection, and sustainability goals.

IBM plans to incorporate foundational models into its major software products. For instance, Watson Code Assistant will utilize generative AI to enable developers to generate code using simple English commands. AIOps Insights, IBM’s AI Operations capabilities, will be enhanced with foundational models to provide greater visibility into IT performance and expedite incident resolution. Watson Assistant and Watson Orchestrate will leverage an NLP foundational model to enhance employee productivity and customer service experiences. Additionally, the Environmental Intelligence Suite will utilize the geospatial foundational model to help organizations address and mitigate environmental risks based on their specific goals.

At its annual Think conference, the company also announced several upcoming advancements. Among them is a GPU-as-a-service infrastructure offering, designed to support AI-intensive workloads. Additionally, it introduced an AI-powered dashboard to measure, track, manage, and report on cloud carbon emissions, supporting organizations in their sustainability efforts. Moreover, IBM Consulting launched a new practice specifically focused on watsonx and generative AI, aimed at assisting clients in effectively deploying AI technologies.


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