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Machine Learning: From Notebook to Production with Amazon Sagemaker I AWS Dev Day 2018
1. Machine Learning: From
Notebook to Production
with Amazon Sagemaker
Julien Simon
Principal Evangelist,Artificial Intelligence & Machine Learning
@julsimon
Pre-built Notebook Instances
For training data exploration and preprocessing, Amazon SageMaker provides fully managed notebook instances running Jupyter notebooks that include example code for common model training and hosting exercises. These notebook instances are pre-loaded with Anaconda packages, and popular deep learning libraries like TensorFlow, and Apache MXNet.
Highly-optimized Machine Learning Algorithms
Amazon SageMaker installs high-performance, scalable machine learning algorithms optimized for speed, scale, and accuracy, to run on extremely large training datasets. Based on the type of learning that you are undertaking, you can choose from supervised algorithms, such as linear/logistic regression or classification; as well as unsupervised learning, such as with k-means clustering.
TRAIN
One-click Training
When you’re ready to train in Amazon SageMaker, simply indicate the type and quantity of instances you need and initiate training with a single click. SageMaker sets up the distributed compute cluster, performs the training, and tears down the cluster when complete. SageMaker seamlessly scales to tens of nodes with hundreds of GPUs, so you no longer need to worry about all the complexity and lost time involved in making distributed training architectures work.
Built-in Automatic Hyperparameter Optimization (in Preview)
Using built-in hyperparameter optimization (HPO), SageMaker can automatically tune your algorithm by adjusting hundreds of different combinations of parameters, to quickly arrive at the best solution for your machine learning problem. HPO lets you easily optimize an ML model on SageMaker by exploring lots of variations of the same algorithm with varying hyperparameters to pick the one with the best performance on your data.
DEPLOY
Deployment without Engineering Effort
After training, SageMaker provides the model artifacts and scoring images to you for deployment to Amazon EC2 or anywhere else. When you’re ready to deploy your model, you can launch into a secure and elastically scalable environment, with one-click deployment from the SageMaker console.
Fully Managed
Amazon SageMaker handles all of the compute infrastructure on your behalf, with built-in Amazon CloudWatch monitoring and logging, to perform health checks, apply security patches, and other routine maintenance, as well as ensure updates to the supported deep learning frameworks as they become available.