You work on a team that builds state-of-the-art deep learning models by using the TensorFlow framework. Your team runs multiple ML experiments each week, which makes it difficult to track the experiment runs. You want a simple approach to effectively track, visualize, and debug ML experiment runs on Google Cloud while minimizing any overhead code. How should you proceed?

You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?

You built and manage a production system that is responsible for predicting sales numbers.
Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn't changed; however the accuracy of the model has steadily deteriorated. What issue is most likely causing the steady decline in model accuracy?

You manage an ML workflow for a fraud detection model. The source code for data processing and model training is stored in a version control repository. You need to implement a continuous training (CT) pipeline that automatically runs unit tests and launches a new training run on Agent Platform Pipelines when code is updated in the repository. You want to use a fully managed service to orchestrate this process. What should you do?

You are developing an image recognition model using PyTorch based on ResNet50 architecture.
Your code is working fine on your local laptop on a small subsample. Your full dataset has 200k labeled images. You want to quickly scale your training workload while minimizing cost. You plan to use 4 V100 GPUs. What should you do?