MLCOST-10: Use managed build environments
Consider using managed notebooks instead of local ones, or notebooks hosted on a server. Managed notebooks come bundled with security, network, storage, compute capabilities that take a lot of time and resources to develop locally. Managed ML build environment also makes it easy to decide the type of machine you prefer so you don’t need to manage any complex AMIs or security groups-this makes it very easy to get started. It can also provide access to GPUs and big machines with large amounts of RAM that might not be possible on a local setup.
Implementation plan
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Use Amazon SageMaker Notebooks - An Amazon SageMaker notebook instance is a ML compute instance running the Jupyter Notebook App. SageMaker manages creating the instance and related resources. Use Jupyter notebooks in your notebook instance to prepare and process data, write code to train models, deploy models to SageMaker hosting, and test or validate your models. SageMaker provides hosted Jupyter notebooks that require no setup, so you can begin processing your training data sets immediately. With a few clicks in the SageMaker console, you can create a fully managed notebook instance, pre-loaded with useful libraries for machine learning. You only need to add your data.
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Use Amazon SageMaker Studio - Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps, improving data science team productivity. SageMaker Studio gives you complete access, control, and visibility into each step required to build, train, and deploy models. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, compare results, and deploy models to production all in one place, making you much more productive. All ML development activities including notebooks, experiment management, automatic model creation, debugging, and model and data drift detection can be performed with SageMaker Studio.
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Use SageMaker Canvas - Amazon SageMaker Canvas, a new visual, no code capability that allows business analysts to build ML models and generate accurate predictions without writing code or requiring ML expertise. Its intuitive user interface lets you browse and access disparate data sources in the cloud or on premises, combine datasets with the click of a button, train accurate models, and then generate new predictions once new data is available.
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