Fine-Tuned Version of Microsoft’s Phi-3.5 is One of the First Small Language Models (SLM) for Manufacturing
SAN FRANCISCO, Nov. 13, 2024 /PRNewswire/ — Sight Machine, provider of the leading platform for data-driven manufacturing and industrial AI, today introduced Factory Namespace Manager, one of the first AI small language models (SLM) for manufacturing. The AI model tackles a core data governance challenge: mapping the multitude of factory data naming schemas into enterprise-wide unified namespaces or corporate data dictionaries.
Factory Namespace Manager is one of the first partner-enabled adapted AI models for manufacturing offered within the Azure AI model catalog, which Microsoft announced today. Factory Namespace Manager uses AI to fill a crucial gap in the technology needed to create a unified namespace: mapping between the original data field names and the corporate standard, enabling manufacturers to integrate factory data with enterprise data systems for end-to-end optimization.
SLMs Are More Cost-Effective to Train and Use
Factory Namespace Manager, which Sight Machine will demonstrate at next week’s Microsoft Ignite conference in Chicago, is a customized, fine-tuned version of Microsoft’s Phi-3.5 small language model. Unlike large language models (LLMs) – general purpose software trained on vast amounts of data – SLMs are used to focus on specific types of work and require less computing resources, offering strong performance at low cost and low latency.
“Our solution addresses a widespread challenge in the manufacturing industry, converting decentralized naming systems into a single corporate standard,” said Kurt DeMaagd, Sight Machine Chief AI Officer and Co-Founder. “This has become an acute problem as more clients push factory plant floor data to the cloud, removing data from its original context, and making the management of that data increasingly difficult.”
Tackling Complex Factory Data Environments
Individual plants often have thousands of data sources from multiple generations of machinery that are frequently 10 or 20 years old, and typically the data streams aren’t labeled in a standardized format that makes clear where the data comes from and what it represents. In order to perform analytics across lines, processes and plants (and even between otherwise-identical machines with different data labeling), companies need a standardized way to identify similar data. Today, creating this translation layer requires a heavy investment of time by subject matter experts with extensive knowledge on the nuances of both the legacy and the target naming schemas, and is thus typically done manually for a small subset of data.
“I’ve spoken to dozens of industrial companies about their current and potential use of AI in factory operations and the overwhelming feedback I hear and see in IDC survey data is that most companies are struggling to leverage AI effectively at scale due to the condition of their data,” said Jonathan Lang, Research Director of Worldwide IT/OT Convergence Strategies at IDC. “They have this dilemma that contextually similar data is formatted in multiple ways and is difficult to source and normalize amidst a complete lack of historical governance and data architecture. What I’ve heard loud and clear is that technology that helps to solve this challenge and reduce the labor requirement to decipher data will be readily adopted.”
The bottling company Swire Coca-Cola USA plans to use Factory Namespace Manager to efficiently map its extensive PLC and plant floor data into its corporate data namespace.
“We are working with Factory Namespace Manager to recognize patterns in the data we’ve manually translated, and then applying the patterns to the rest of our factory data,” said Bharathi Rajan, VP of Data & Insights at Swire Coca-Cola USA. “This will make it much easier to get relevant data to frontline workers, to inform decision making, and to integrate production insights into other parts of the company such as supply chain. This is one of the most useful applications of AI we’ve seen in manufacturing, and we’re excited to put it to work.”
“The collaboration between Microsoft and Sight Machine will give manufacturing organizations the ability to build AI solutions through Azure AI Studio and Microsoft Copilot Studio that deliver real value and advance business transformation,” said Satish Thomas, Corporate Vice President, Business & Industry Solutions, Microsoft. “Factory Namespace Manager applies SLM AI technology to a high-impact use case with strong potential ROI for companies pursuing data-driven manufacturing.”
How Sight Machine’s Manufacturing Data Platform Employs AI Techniques
AI is interwoven into Sight Machine’s Manufacturing Data Platform, which uses machine learning and other AI techniques to identify and optimize how machine settings, raw materials and production practices interact to determine throughput, quality, sustainability and other key manufacturing metrics.
Sight Machine’s AI offerings include Factory CoPilot, which uses generative AI technology to offer an intuitive, “ask the expert” experience for all manufacturing stakeholders. Built using Microsoft Azure OpenAI Service, Factory CoPilot can automatically summarize all relevant data and information about production in real-time (e.g., for daily meetings) and generate user-friendly reports, emails, charts and other content (in any language) about the performance of any machine, line or plant across the manufacturing enterprise, based on contextualized data in the Sight Machine platform.
Sight Machine also offers Blueprint, AI-driven tag-to-asset mapping software for clients that have large volumes of poorly identified data sources. It uses AI to map each data source (tag) to a specific asset (machine). Sight Machine built Blueprint in partnership with Microsoft and NVIDIA.
Learn More
To learn more about Factory Namespace Manager, please go to https://sightmachine.com/product/factory-namespace-manager/.
About Sight Machine
Sight Machine provides the leading platform for data-driven manufacturing and industrial AI, helping global manufacturers increase profitability, productivity and sustainability. Sight Machine’s Manufacturing Data Platform creates a common data foundation by capturing and structuring data from the entire factory to deliver a systemwide view of the manufacturing process. With insights powered by artificial intelligence, manufacturers can now optimize across their production processes and factory networks, and extend the impact to their broader supply and value chains. Sight Machine has offices in San Francisco and Ann Arbor, Mich. (www.sightmachine.com).
Sight Machine Press Contact: [email protected]
SOURCE Sight Machine Inc.
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