Kahveh Saramout•March 31, 2023
In last week’s blog post, I showed how manufacturing organizations can save millions by leveraging foundation models. With the fifth industrial revolution underway, however, ensuring that these millions translate into competitive advantage will require them to adopt AI solutions as quickly as possible — a challenge for larger enterprises with baked-in data architecture that’s slow and difficult to change.
To embrace the fifth industrial revolution, manufacturing organizations will need to break down data silos and consolidate their unstructured data, including documents (SOPs, technical documents), images (from computer vision systems at the edge), production history, data historians, and more, into centralized data lakes. This enables the construction of robust production data pipelines that can effectively utilize this valuable information, further enhancing operational efficiency and driving innovation across the enterprise.
Aside from enabling an organization to leverage modern AI technologies, data lakes also promote data democratization empowering all employees to make informed decisions, enabling enterprise wide analytics, and creating standardized processes for data retention and integration. Building a data lake for fine-tuning large language models (LLMs) in a manufacturing environment requires a well-planned strategy and adherence to the following best practices.
The fifth industrial revolution, fueled by foundation models, holds tremendous potential to revolutionize the manufacturing industry by unlocking value from automation assets, enhancing productivity, and providing organizations with a competitive edge.
The foundation of the fifth industrial revolution lies in the seamless extraction, transformation, and loading (ETL) of data into a data lake. As a robust storage system, a data lake allows for the accommodation of structured and unstructured data at any scale. In a manufacturing setting, this includes data from manufacturing execution systems, databases, data historians, computer vision systems, and SOP or tech spec repositories.
Here's a simplified ETL strategy for this transition:
Labelbox has crafted a platform that is optimally suited to help your organization quickly structure your data and develop production-grade models. Tools like Labelbox Catalog facilitate this process by incorporating data from your data lake into an intuitive and user-friendly interface. You can effortlessly query data and find similar information using advanced similarity search features. This holistic approach allows your organizations to fully exploit your data assets and unlock the potential of foundation models.
Labelbox Annotate further enables your organization to label their data and transform unstructured data into structured formats, either by utilizing your own labelers or leveraging Labelbox's expert in-house labeling service. Model Foundry and Labelbox Model offer the quickest path from labeled data to trained models, providing easy access to the most popular and high-performing LLMs. By offering an end-to-end solution, Labelbox empowers your organization to develop and implement AI-driven strategies effectively and efficiently.
The financial benefits of implementing such a system are significant, with net-present values well into the millions. The numerous use cases for LLMs in manufacturing environments demonstrate their capacity to transform various aspects of operations, from decision-making and quality control to predictive maintenance and cross-plant collaboration.
As we stand on the cusp of this transformation, it is essential to remain proactive and agile. By doing so, you can ensure your continued growth, competitiveness, and success in the ever-changing world of industrial automation.