Build a custom ML model with Vertex AI

The video begins with Priyanka Vergadia introducing the concept of custom machine learning models in Vertex AI. She explains that the previous episodes focused on creating datasets and training machine learning models using auto ML. The current episode aims to assist Fuel Symbol in developing a custom machine learning model to predict vehicle fuel efficiency. The process involves understanding how to run custom training in Vertex AI and the requirements for creating a custom job, such as developing a Python training application or custom container, utilizing pre-built containers for Python applications with TensorFlow, scikit-learn, XG Boost, or PyTorch, and the need for a Cloud Storage bucket to store model output artifacts. Additionally, the video touches on the use of Vertex AI's hyperparameter tuning service and the selection of compute resources for training. The summary highlights the importance of preparing the necessary components, such as training code and dependencies, before submitting custom jobs to Vertex AI.

This paragraph details the process of setting up a custom training environment using Docker and Google Cloud services. It begins with creating a Docker file that uses a deep learning container image from Google Cloud, which comes preloaded with common machine learning frameworks like TensorFlow Enterprise, Pandas, and scikit-learn. The script then guides the user to set up a Cloud Storage bucket for exporting the trained TensorFlow model and to create a 'train.py' file within a 'trainer' directory, where the training code is written. The summary emphasizes the adaptation of the code from TensorFlow docks to predict fuel efficiency and the necessity of updating the bucket variable with the storage bucket's name. It also explains the local testing of the container and the process of pushing it to Google Container Registry to make it accessible for Vertex AI. The paragraph concludes with the initiation of a custom model training job in Vertex AI, explaining the selection of 'No managed datasets' and the use of custom containers for training. It also discusses the Compute and Pricing options, the Prediction Container setup, and the training process's monitoring.

The final paragraph of the script covers the deployment of the trained model and the process of making predictions. After the model is trained, it needs to be deployed to an endpoint for prediction services. The video demonstrates how to name the model and set up the traffic split and compute nodes for the endpoint. It also explains that deploying the endpoint may take a few minutes and how to monitor its progress in the Endpoint tab. Once deployed, the video proceeds to show how predictions can be made using the Vertex AI Python SDK from a Python notebook. The summary highlights the installation of the SDK, the creation of a Python 3 notebook, and the steps to make a prediction with test data. The video concludes with a brief mention of the next episode's content, which will focus on building a vision model using auto ML, and encourages viewers to engage in discussions and follow for updates.