Amazon SageMaker is a "fully managed machine learning service". This means it provisions an environment for data scientists and developers without them needing to worry about managing servers.Please note: at the time of this post Amazon SageMaker is only available in the Ireland region for Europe.
Leveraging the ease-of-use of Jupyter Notebooks, SageMaker enables you to easily explore and analyze data, sadly the service does not (yet) support JupyterLab.
Training and hosting instances are billed by seconds of usage, with notebook instances being billed hourly.
When we first visit the Amazon SageMaker dashboard we are asked to create a notebook instance. Here we can choose a name, an instance type, an IAM role, a VPC, configure the instance's life cycle and choose an encryption key for the notebook data.
In fact, SageMaker actually gives a very transparent vibe and allows you to use Amazon's or your own algorithms and frameworks. Furthermore the service hosts jobs, models and endpoints.
An overview of the Amazon SageMaker workflow
The above image shows a simplified workflow on SageMaker.
After some data wrangling we train a model using a training image stored on Amazon ECR (green). We then have model artifacts (blue) which we can use to run, test and deploy (red). An endpoint is created to give applications access to the trained model and run inferences on new data (purple).
A few noteworthy realizations
Amazon SageMaker streamlines the creation of ML pipelines and minimizes the need for maintenance while simultaneously cutting costs as you only pay for what you use. It comes with various (open-source) features and enables you to run "bring your own"-code.
Be sure to keep an eye on our blog in the coming weeks as we take a deeper dive into Amazon SageMaker!