Smart ML Experiment tracking and model registry with Neptune.ai Platform

Neptune.ai is a Machine Learning observability platform for ML experiment tracking and model registry for the enterprise teams. With this SaaS platform, any enterprise user can log, store, query, display, organize, and compare all your model metadata in a single place. Highlights: - Feel in control of your model building and experimentation - Be more productive at ML engineering and research - Focus on ML, leave metadata bookkeeping to platform - Use computational resources more efficiently - Build reproducible, compliant, and traceable models - Any one can get started in just 5 minutes Content Timeline: ----------------- - (00:00) Video Start - (00:08) Introduction - (02:00) Why and Why? - (04:05) 3 Key Features - (05:05) Platform Service Status - (06:00) MLOps Tutorials - (08:31) Get Platform Access - (09:15) Jupyter Notebook (colab & GitHub) - (10:30) Heart Disease Detection ML Experiment - (12:12) ML Experiment in Keras/TensorFlow - (20:46) Export colab notebook to GitHub - (22:16) Adding Neptune Tracking code - (23:08) Create Tracking Project - (26:17) Initialize Neptune runtime objects - (28:28) ML Experiment with Neptune Objects - (30:01) Neptune Callback with ML Experiment - (32:05) Neptune ML Tracking Dashboard - (38:30) Adding another Experiment - (42:05) Comparing Experiments - (44:32) Custom content (image) Tracking - (45:56) More Experiments tracking - (48:38) Adding Jupyter notebook as resource - (49:57) Pushing Colab notebook to GitHub - (50:19) Recap - (52:25) Credits Neptune.ai: https://neptune.ai/ Source Code used in this example: https://github.com/prodramp/publiccode/tree/master/machine_learning/neptune_ai Please visit: ------------------ Prodramp LLC | https://prodramp.com | @prodramp https://www.linkedin.com/company/prodramp Content Creator: Avkash Chauhan (@avkashchauhan) https://www.linkedin.com/in/avkashchauhan Tags: #ai #aicloud #h2oai #driverlessai #machinelearning #cloud #mlops #model #collaboration #deeplearning #modelserving #modeldeployment #keras #tensorflow #pytorch #datarobot #datahub #aiplatform #aicloud #modelperformance #modelfit #modeleffect #modelimpact #bias #modelbias #modeldeployment #modelregistery #modelpipeline #neptuneai