Amazon SageMaker
No 1Amazon SageMaker
End to End Machine Learning Platform. |
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Amazon SageMaker significantly reduces the amount of time needed to train, tune, and deploy machine learning models. Amazon SageMaker manages and automates all the sophisticated training and tuning techniques so you can get models into production quickly. |
Zero Setup. |
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Amazon SageMaker includes hosted Jupyter notebooks that make it easy to explore and visualize your training data stored in Amazon S3. You can connect directly to data in S3, or use AWS Glue to move data from Amazon RDS, Amazon DynamoDB, and Amazon Redshift into S3 for analysis in your notebook. |
Train with Any Deep Learning Framework. |
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With Amazon SageMaker, you can use the deep learning framework of your choice for model training. Amazon SageMaker is pre-configured to run TensorFlow and Apache MXNet; two popular deep learning frameworks. You can also bring your own Docker container with any framework you like - such as Caffe2, PyTorch, Microsoft Cognitive Toolkit (CNTK), or Torch. |
Per Second Billing. |
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With Amazon SageMaker, you pay only for what you use. Building, training and hosting is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and hosting instances. |
No 2 Demos
Post a picture and ask @WhereML to idenfity your location. Using Amazon SageMaker, this neural network was trained using the Berkely Multimedia Commons Database. The pictures are sent to a SageMaker endpoint for inference and the top locations are returned to the user. This demo was built by Randall Hunt, AWS Soluction Architect.
Demo 2: Finding Aircraft in Overhead Imagery
Discover how a simple Convolution Neural Network (CNN) written using Gluon and detect aircraft in overhead imagery. This demo contains a link to an HTML page of the SageMaker notebook. To view the dynamic notebook in SageMaker click here but please note that you must be logged into the demo account for this to work.
Demo 3: Movie Recommendation Service
Technology to help support curation of content experience is a very common ask from media & entertainment customers. This demo showcases how customers can build their own recommendation capabilities in this space using Amazon S3, Amazon API Gateway, AWS Lambda, Amazon Elasticsearch Service, AWS Elastic Beanstalk, and Amazon SageMaker
This notebook looks at a collection of tweets in a dataset from Kaggle.com. This demonstration shows how you can use a SageMaker notebook just like any other Jupyter notebook but spin training clusters for model training on-demand, when it makes sense in your EDA. This notebook was built on a t2.large, so it was very inexpensive to build all of the intial charts shown.
