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I'm looking into the best way to set up an end-to-end machine learning pipeline, and evaluating the data exploration component options.
I'm trying to figure out the difference between google cloud datalab, and google cloud ai platform notebooks. They both seem to offer similar functionality, so not sure why they both exist, or whether one is a new iteration of the other.
If they are different, what is the benefit of one over the other?
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Google AI Platform Notebooks are enterprise-grade notebooks best suited for those with compliance requirements, those with a need to ingest data from GCP sources like BigQuery, and those who are already in the GCP ecosystem and can take advantage of existing compute instances.
Google Cloud DataLab is a tool for exploratory data analysis, data visualization, and building machine learning models. It is a powerful BI Engine tool for structured and unstructured data to analyse the patterns in every dataset.
The AI Platform training service allows you to train models using a wide range of different customization options. You can select many different machine types to power your training jobs, enable distributed training, use hyperparameter tuning, and accelerate with GPUs and TPUs.
Google Cloud Datalab is a web based, interactive tool that makes it easy for you to gain insight into raw data, and present, collaborate, and publish data insights in a fast, simple, and cost effective way.
Google Cloud AI Platform Notebooks is effectively the upgraded version of Google Cloud Datalab and gives you benefits like being able to use the notebook directly in your browser without having to setup an ssh tunnel first (which datalab forces you to do).
If you're creating a new notebook, you 100% want to go with AI Platform Notebooks
(Also, Datalab is now deprecated)
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Quoting the End-to-End Machine Learning with TensorFlow on GCP course on Coursera (link) -
AI Platform Notebooks is the next generation of hosted notebook on GCP, and has replaced Cloud Datalab.
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