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