Building and Deploying Machine Learning Solutions with Vertex AI Challenge Lab GSP354

Challenge scenario You were recently hired as a Machine Learning Engineer at a startup movie review website. Your manager has tasked you with building a machine learning model to classify the sentiment of user movie reviews as positive or negative. These predictions will be used as an input in downstream movie rating systems and to surface top supportive and critical reviews on the movie website application. The challenge: your business requirements are that you have just 6 weeks to productionize a model that achieves great than 75% accuracy to improve upon an existing bootstrapped solution. Furthermore, after doing some exploratory analysis in your startup's data warehouse, you found that you only have a small dataset of 50k text reviews to build a higher performing solution. Your challenge To build and deploy a high performance machine learning model with limited data quickly, you will walk through training and deploying a custom TensorFlow BERT sentiment classifier for online predictions on Google Cloud's Vertex AI platform. Vertex AI is Google Cloud's next generation machine learning development platform where you can leverage the latest ML pre-built components and AutoML to significantly enhance your development productivity, the ability to scale your workflow and decision making with your data, and accelerate time to value. First, you will progress through a typical experimentation workflow where you will build your model from pre-trained BERT components from TF-Hub and tf.keras classification layers to train and evaluate your model in a Vertex Notebook. You will then package your model code into a Docker container to train on Google Cloud's Vertex AI. Lastly, you will define and run a Kubeflow Pipeline on Vertex Pipelines that trains and deploys your model to a Vertex Endpoint that you will query for online predictions. #gcp #googlecloud #qwiklabs

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