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
Machine Learning A-Z™: Hands-On Python & R In Data Science
A Complete Machine Learning Project Walk-Through in Python
Machine Learning for Front-End Developers
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
#MachineLearning #MLflow #Morioh
Social Network for Developers: http://bit.ly/2TFv1y0
Developer’s Store: http://bit.ly/2L27JNR
Learn Startup: http://bit.ly/2UDotMN
Learn to code: http://bit.ly/2pN2aXx