MLOps

Assembly Line

Industrial DataOps: The data backbone of digital twins

Date:

Author: Fredrik Holm

Topics: Digital Twin, MLOps

Organizations: Cognite

What is needed is not a single digital twin that perfectly encapsulates all aspects of the physical reality it mirrors, but rather an evolving set of “digital siblings.” Each sibling shares a lot of the same DNA (data, tools, and practices) but is built for a specific purpose, can evolve on its own, and provides value in isolation.

The data backbone to power digital twins needs to be governed in efficient ways to avoid the master data management challenges of the past—including tracking data lineage, managing access rights, and monitoring data quality, to mention a few examples. The governance structure has to focus on creating data products that may be used, reused, and collaborated on in efficient and cross-disciplinary ways. The data products have to be easily composable and be constructed like humans think about data ; As a graph where physical equipment are interconnected both physically and logically. And through this representation select parts of the graph may be used to populate the different digital twins in a consistent and coherent way.

Read more at Cognite Blog

Making The Most Of Data Lakes

Date:

Author: Anne Meixner

Topics: MLOps

Vertical: Semiconductor

Organizations: PDF Solutions, Synopsys

Data management and data analysis necessitates understanding the data storage and data compute options to design an optimal solution. This is made more difficult by the sheer volume of data generated by the design and manufacturing of semiconductor devices. There are more sensors being added into equipment, more complex heterogeneous chip architectures, and increased demands for reliability — which in turn increase the amount of simulation, inspection, metrology, and test data being generated.

Connecting different data sources is extremely valuable. It allows feed-forward decisions on manufacturing processes (package type, skipping burn-in), and feedback in order to trace causes of excursions (yield, quality, and customer returns).

“An understanding of the semiconductor manufacturing process and relationships throughout are essential for some applications,” said Jeff David, vice president of AI solutions at PDF Solutions. “For example, how can I use wafer equipment history and tool sensor data to predict the failure propensity of a chip at final test? How does time delay between process and test steps determine what data is useful in finding a root cause of a failure mode? What failure modes are predictable with which datasets? How do preceding process steps affect the data collected at a given process step?”

Read more at Semiconductor Engineering

MakinaRocks, unveiling the AI/ML modeling tool “Link”

Date:

Topics: MLOps

Organizations: MakinaRocks

Link is an extension for JupyterLab – an interactive development interface for notebooks, code, and data – that lets users easily create readable pipelines for AI and ML modeling. Link maintains the usability of JupyterLab that data scientists rely on while removing technological hurdles related to Kubernetes, a portable, open-source platform for managing workloads and services. By removing the technological hurdles associated with Kubernetes, Link allows users to create pipelines that can be used in MLOps environments with ease, even without a working knowledge of Kubernetes.

Read more at MakinaRocks News