pipeline – Page 5 – Andrew Fairless, Ph.D.

What I Read: Building Data Platform

By Andrew Fairless on August 12, 2021July 22, 2021

https://www.montecarlodata.com/the-quick-and-dirty-guide-to-building-your-data-platform/ Updated July 9, 2021The Quick and Dirty Guide to Building Your Data PlatformBarr Moses, Lior Gavish“For most organizations, building a data platform is no longer a nice-to-have but aContinue readingWhat I Read: Building Data Platform

What I Read: What is a Data Mesh?

By Andrew Fairless on July 6, 2021June 22, 2021

https://cnr.sh/essays/what-the-heck-data-mesh What the Heck is a Data Mesh?!Chris Riccomini on June 8, 2021 “Yet, organizations don’t think of data as a product…. It would be madness to propose centralizing allContinue readingWhat I Read: What is a Data Mesh?

What I Read: Accelerating ML within CNN

By Andrew Fairless on June 3, 2021May 14, 2021

https://medium.com/cnn-digital/accelerating-ml-within-cnn-983f6b7bd2eb Accelerating ML within CNNKelly DavisMay 11 “Unfortunately, it was not designed for lightweight experimentation, and because of that, the research iteration process was frustratingly slow… we explored a numberContinue readingWhat I Read: Accelerating ML within CNN

What I Read: Feature stores

By Andrew Fairless on May 31, 2021May 14, 2021

https://www.kdnuggets.com/2021/05/feature-stores-how-avoid-feeling-every-day-is-groundhog-day.html Feature stores – how to avoid feeling that every day is Groundhog DayBy Monte Zweben, CEO, Splice Machine. “Feature stores stop the duplication of each task in the MLContinue readingWhat I Read: Feature stores

What I Read: Common Errors when Debugging Airflow DAGs

By Andrew Fairless on April 14, 2021April 2, 2021

https://www.astronomer.io/blog/7-common-errors-to-check-when-debugging-airflow-dag 7 Common Errors to Check when Debugging Airflow DAGsTasks not running? DAG stuck? Logs nowhere to be found? We’ve been there. Here’s a list of common snags and someContinue readingWhat I Read: Common Errors when Debugging Airflow DAGs

What I Read: Kedro Pipelines with Airflow

By Andrew Fairless on March 30, 2021March 15, 2021

https://medium.com/quantumblack/kedro-airflow-0-4-0-orchestrating-kedro-pipelines-with-airflow-23fb1283f24d Kedro-Airflow 0.4.0 — Orchestrating Kedro Pipelines with AirflowThe Kedro team and Astronomer have released Kedro-Airflow 0.4.0 to help you develop modular, maintainable & reproducible code with orchestration superpowers!QuantumBlack, aContinue readingWhat I Read: Kedro Pipelines with Airflow

What I Read: MLOps for effective AI strategy

By Andrew Fairless on March 14, 2021March 7, 2021

https://www.sigmoid.com/blogs/mlops-for-effective-ai-strategy/ SigmoidHow to use MLOps for an effective AI strategy “One of the foremost challenges in implementing MLOps is the difficulty in superimposing DevOps practices on ML pipelines. This isContinue readingWhat I Read: MLOps for effective AI strategy

What I Read: Machine learning is going real-time

By Andrew Fairless on March 3, 2021March 3, 2021

https://huyenchip.com/2020/12/27/real-time-machine-learning.html Machine learning is going real-timeDec 27, 2020Chip Huyen “Machine learning is going real-time, whether you’re ready or not. While the majority of companies are still debating whether there’s valueContinue readingWhat I Read: Machine learning is going real-time

What I Read: Long Live Data Discovery

By Andrew Fairless on March 1, 2021March 1, 2021

https://towardsdatascience.com/data-catalogs-are-dead-long-live-data-discovery-a0dc8d02bd34 Data Catalogs Are Dead; Long Live Data DiscoveryWhy we need to rethink our approach to metadata management and data governanceBarr MosesDec 14 “Traditional data catalogs and governance methodologies typicallyContinue readingWhat I Read: Long Live Data Discovery

What I Watch: Future of Data Engineering

By Andrew Fairless on March 1, 2021February 27, 2021

https://youtu.be/ZZr9oE4Oa5U Future of Data EngineeringDec 16, 2019InfoQ “Chris Riccomini talks about the current state-of-the-art in data pipelines and data warehousing, and shares some of the solutions to current problems dealingContinue readingWhat I Watch: Future of Data Engineering

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