The Machine Learning Workflow with Vertex AI Flashcards
Give examples of how Vertex AI supports the ML workflow.
- Feature Store (provides a centralized repository for organizing, storing, and serving features to feed to training models)
- Vizier (helps you tune hyperparameters in complex machine learning models)
- Explainable AI (helps interpret training performance and model behaviors)
- Pipelines, (help you automate and monitor the ML production line)
What are the benefits of Vertex AI Feature Store?
- features are shareable for training or serving tasks (Features are managed and served from a central repository, which helps maintain consistency across your organization)
- features are reusable (This helps save time and reduces duplicative efforts, especially for high-value features)
- features are scalable (Features automatically scale to provide low-latency serving, so you can focus on developing the logic to create the features without worrying about deployment)
- features are easy to use (Feature Store is built on an easy-to-navigate user interface)
What is AI?
Artificial intelligence, or AI, is an umbrella term that includes anything related to computers mimicking human intelligence. For example, in an online word processor, robots performing human actions all the way down to spell check.
What is ML?
Machine learning is a subset of AI that mainly refers to supervised and unsupervised learning. You might also hear the term deep learning, or deep neural networks. It’s a subset of machine learning that adds layers in between input data and output results to make a machine learn at more dept
How are hyperparameters tuned in AutoML?
Hyperparameter tuning happens automatically on the back end. This is largely done by a neural architect search, which finds the best-fit model by comparing the performance against thousands of other models.
How a model is evaluated in Vertex AI?
Vertex AI provides extensive evaluation metrics to help determine a model’s performance.
What is Recall?
Recall refers to all the positive cases, and looks at how many were predicted correctly.
This means that recall is equal to the true positives, divided by the sum of the true positive and false negatives.
What is Presision?
Precision refers to all the cases predicted as positive, and how many are actually positive.
This means that precision is equal to the true positives, divided by the sum of the true positive and false positives.
What are type 1 and type 2 errors
False positive and false negative respectively
What is Explainable AI?
Explainable AI is a set of tools and frameworks to help understand and interpret predictions made by machine learning models.
What does model serving consist of?
- model deployment
- model monitoring
What is MLOps?
MLOps combines machine learning development with operations and applies similar principles from DevOps to machine learning models, which is short for development and operations.
In this case, this refers to building an integrated ML system and operating it in production
What doe it mean practicing MLOps?
It means advocating for automation and monitoring at each step of the ML system construction.
This means adopting a process to enable
- continuous integration,
- continuous training,
- continuous delivery.
What options are available to deploy an ML model?
- deploy to an endpoint (is best when immediate results with low latency are needed) A model must be deployed to an endpoint before that model can be used to serve real-time predictions.
- deploy using batch prediction (is best when no immediate response is required, and accumulated data should be processed with a single request) For example, sending out new ads every other week based on the user’s recent purchasing behavior and what’s currently popular on the market.
- deploy using offline prediction (is best when the model should be deployed in a specific environment off the cloud)
