In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
Machine Learning In Node.js With TensorFlow.js
☞ https://morioh.com/p/a517bc403340
Machine Learning A-Z™: Hands-On Python & R In Data Science
☞ http://learnstartup.net/p/SJw1YoTMg
A Complete Machine Learning Project Walk-Through in Python
☞ https://morioh.com/p/b56ae6b04ffc
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☞ https://morioh.com/p/9738ad13a3f5
The Future of Machine Learning and JavaScript
☞ https://morioh.com/p/23b7c7f5e9
In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
With a short demo, you see a complete ML model life-cycle example, you will walk away with: MLflow concepts and abstractions for models, experiments, and projects How to get started with MLFlow Using tracking Python APIs during model training Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
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