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

What I Read: Where Programming, Ops, AI, and the Cloud are Headed

By Andrew Fairless on March 18, 2021March 7, 2021

https://www.oreilly.com/radar/where-programming-ops-ai-and-the-cloud-are-headed-in-2021/ Where Programming, Ops, AI, and the Cloud are Headed in 2021Following O’Reilly online learning trends to see what’s coming next.By Mike LoukidesJanuary 25, 2021 “Operations staff too frequently assumeContinue readingWhat I Read: Where Programming, Ops, AI, and the Cloud are Headed

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: MLOps Changing How Machine Learning Models Developed

By Andrew Fairless on February 26, 2021February 18, 2021

https://www.kdnuggets.com/2020/12/mlops-changing-machine-learning-developed.html MLOps Is Changing How Machine Learning Models Are DevelopedBy Henrik Skogstrom, Head of Growth at Valohai “Let’s look at three ways that MLOps changes how machine learning is developed:Continue readingWhat I Read: MLOps Changing How Machine Learning Models Developed

What I Read: data quality, ML Ops

By Andrew Fairless on January 27, 2021January 1, 2021

https://greatexpectations.io/blog/ml-ops-data-quality/ Why data quality is key to successful ML OpsIn this first post in our 2-part ML Ops series, we are going to look at ML Ops and highlight howContinue readingWhat I Read: data quality, ML Ops

What I Read: Data Scientists Should Be More End-to-End

By Andrew Fairless on January 17, 2021December 26, 2020

https://eugeneyan.com/writing/end-to-end-data-science/ Unpopular Opinion – Data Scientists Should Be More End-to-End “Eric Colson of Stitch Fix was initially “lured to a function-based division of labour by the attraction of process efficiencies”…Continue readingWhat I Read: Data Scientists Should Be More End-to-End

What I Read: End-to-End Machine Learning Platforms

By Andrew Fairless on January 15, 2021December 24, 2020

https://databaseline.tech/a-tour-of-end-to-end-ml-platforms/ A Tour of End-to-End Machine Learning PlatformsIan Hellström21 February 2020 “What turns a collection of machine learning solutions into an end-to-end machine learning platform is an architecture that embracesContinue readingWhat I Read: End-to-End Machine Learning Platforms

What I Read: start deploying

By Andrew Fairless on January 6, 2021December 22, 2020

https://towardsdatascience.com/stop-training-more-models-start-deploying-them-using-webassembly-49a3f178569e Stop training more models, start deploying them (using WebAssembly)We are hardly living up to the promises of AI in healthcare. It’s not because of our training, it’s because ofContinue readingWhat I Read: start deploying

What I Read: DevOps for ML Data

By Andrew Fairless on December 23, 2020December 10, 2020

https://tecton.ai/blog/devops-ml-data/ Why We Need DevOps for ML DataPosted by Kevin Stumpf on April 28, 2020 “Unfortunately, most tools under the MLOps banner tend to focus only on workflows around theContinue readingWhat I Read: DevOps for ML Data

What I Read: Monitoring Machine Learning Models in Production

By Andrew Fairless on December 20, 2020December 23, 2020

https://christophergs.com/machine%20learning/2020/03/14/how-to-monitor-machine-learning-models/ Monitoring Machine Learning Models in ProductionA Comprehensive Guide14 March 2020 “Once you have deployed your machine learning model to production it rapidly becomes apparent that the work is notContinue readingWhat I Read: Monitoring Machine Learning Models in Production

Tag: MLOps