Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters.
They need to track the accuracy metrics for various experiments and use an API to query the metrics over time.
What should they use to track and report their experiments while minimizing manual effort?
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A. B. C. D.B.
When a data science team needs to experiment with various features, model architectures, and hyperparameters, it is important to have a system in place that can track the results of these experiments and allow for easy querying of the results over time. This is particularly important in cases where a large number of experiments are being run simultaneously or sequentially, as it can be difficult to keep track of all the results manually.
There are several tools and services available in the Google Cloud Platform (GCP) that can help with tracking and reporting experiment results. Let's go through each of the options provided in the question and explain their strengths and weaknesses:
A. Use Kubeflow Pipelines to execute the experiments. Export the metrics file, and query the results using the Kubeflow Pipelines API.
Kubeflow is an open-source platform for building and deploying machine learning workflows on Kubernetes. Kubeflow Pipelines is a component of Kubeflow that provides a way to create and manage machine learning workflows, including the ability to track experiment results. When using Kubeflow Pipelines, the data science team can create a pipeline for each experiment that includes all the necessary steps, such as data preprocessing, feature extraction, model training, and evaluation. They can then use the Kubeflow Pipelines UI to monitor the progress of the pipeline and view the results of each step.
One advantage of using Kubeflow Pipelines is that it provides a consistent and reproducible way to execute experiments. Each pipeline can be versioned and stored in a Git repository, making it easy to share and reproduce experiments. Another advantage is that Kubeflow Pipelines can export the experiment results to a metrics file in a standardized format, such as TensorFlow Summary or Apache Arrow. This metrics file can then be queried using the Kubeflow Pipelines API or imported into other tools for further analysis.
B. Use AI Platform Training to execute the experiments. Write the accuracy metrics to BigQuery, and query the results using the BigQuery API.
AI Platform Training is a managed service in GCP that allows data scientists to train machine learning models at scale. It provides a way to run training jobs using custom container images or pre-built frameworks such as TensorFlow, PyTorch, or Scikit-learn. When using AI Platform Training, the data science team can specify the hyperparameters and other configuration settings for each training job and monitor the progress of the job using the AI Platform Training UI or the command-line interface.
One advantage of using AI Platform Training is that it can automatically write the accuracy metrics to BigQuery, a fully-managed data warehouse in GCP. This makes it easy to query and analyze the results using SQL or other tools that support the BigQuery API. Another advantage is that AI Platform Training can be integrated with Kubeflow Pipelines, allowing for a more comprehensive experiment tracking and reporting system.
C. Use AI Platform Training to execute the experiments. Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
Cloud Monitoring is a monitoring and observability service in GCP that allows users to monitor the performance and health of their applications and infrastructure. When using AI Platform Training, the data science team can configure Cloud Monitoring to collect and store the accuracy metrics for each training job. They can then use the Cloud Monitoring UI or the Monitoring API to query the metrics and create dashboards and alerts.
One advantage of using Cloud Monitoring is that it provides real-time monitoring and alerting capabilities, allowing the data science team to quickly identify and respond to issues with their experiments. Another advantage is that Cloud Monitoring integrates with other GCP services, such as AI Platform Training and BigQuery, making it easy to build a comprehensive monitoring and reporting system.
D. Use AI Platform Notebooks to execute the experiments. Collect the results in