Amazon SageMaker provides fully managed assistance to data scientists and developers that allows them to quickly and easily create, train, and expand machine-learning patterns at any scale. Amazon SageMaker has modules that can be used together or separately to create, train, and deploy machine learning prototypes. It provides secure access to data sources for analysis and research by using a multilingual Jupyter authoring notebook. It also presents machine learning algorithms that can be used to efficiently deal with large amounts of data in a distributed environment.
Amazon SageMaker components:
1. Learn how to build
Amazon SageMaker makes it easy to create ML models, and make them available for training. It provides everything one needs to instantly connect to the training data and choose the best framework and algorithm for the application. Amazon SageMaker also includes hosted Jupyter notebooks, which make it easy to explore and reflect the training data from Amazon S3.
Amazon SageMaker contains the most popular machine-learning algorithms. These algorithms have been pre-installed, optimized to free up as much production as possible, and are a great incentive to choose your algorithm.
2. Train to be a trainer
The Amazon SageMaker console allows one to start training the model in a matter of seconds. Amazon SageMaker manages all the underlying frameworks, and can scale to train models up to a petabyte.
AmazonSageMaker automatically adapts the model to achieve maximum performance to make the training process more interactive and sincere.
3. Learn how to deploy
After creating the model, Amazon SageMaker makes production easy so that one can start generating prognostications based on new data (a process called inference).
Amazon SageMaker extends this model on an auto-scaling group Amazon EC2 instances dispersed across different availability zones. This allows for high production and high availability.
Amazon SageMaker includes built-in A/B testing capabilities to allow one to test their model, and to experiment with different versions to get the best results.
Amazon SageMaker’s top features include:
1. Prepare Data in Minutes
Amazon SageMaker Data Wrangler makes it easy to quickly prepare data and create model features. You can also connect to data sources to create model features and use built-in data transforms.
Amazon SageMaker clarify data is used to improve model quality by detecting biases during data preparation and training. SageMaker Clarify also provides model explanation reports, which allow stakeholders to see how models make predictions.
3. Privacy and Security
Amazon SageMaker provides you with a secure machine learning environment right from the beginning. With a full set of security features, you can support a wide variety of industry laws.
4. Data Labeling
Amazon SageMaker Ground Truth makes it easy to create highly accurate machine learning training datasets. You can also use the SageMaker ground truth dashboard to label your data in minutes using customs or builtin data labeling workflows like 3D point clouds and video.
5. Feature Store
Amazon SageMaker Feature Store provides machine learning (ML), real-time and batch features. Securely save, find, and trade features so you can obtain the same features during training and inference. This can help you save months of development time.
6. Data Processing at Scale
Amazon SageMaker Processing extends SageMaker’s flexibility, scalability and dependability to data processing tasks within the cloud. SageMaker Processing connects with existing storage, spins up resources required to complete your task, saves the output for permanent storage, logs and analyses.
7. Jupyter Notebooks – One-click
Amazon SageMaker Studio notebooks are one-click Jupyter notebooks that have completely elastic computing resources. This allows you to easily scale up or decrease the resources. You can share notebooks with just a click so that colleagues have the same notebook in the same place.
8. Built-in Algorithms
Pre-built images of Amazon SageMaker contain over 15 algorithms that can be used to train and infer.
9. Pre-Built Solutions & Open-Source Models
Amazon SageMaker JumpStart makes it easy to quickly get started with machine-learning with pre-built solutions that are deployable in minutes. SageMaker JumpStart allows you to deploy and fine-tune more than 150 open-source models.
Amazon SageMaker Autopilot creates and trains the best machine learning models based upon your data. It also gives you full control and visibility. The model can then be deployed to production in a single click or you can iterate to improve its quality.
11. Optimized to Major Frameworks
Many of the most popular deep learning frameworks, such as TensorFlow and Apache MXNet, are optimized for Amazon SageMaker. Frameworks are updated regularly to the latest version and tuned for AWS performance. These frameworks don’t require manual setup and can be used in the built-in containers.
12. Reinforcement Learning
Amazon SageMaker offers reinforcement learning in addition to the usual supervised and unsupervised learning. SageMaker comes with fully-managed reinforcement-learning algorithms that can be integrated into the system. These include some of the most innovative and high-performing algorithms in academic literature.
13. Managed Spot Training
Amazon SageMaker offers Managed Spot Training. This may be of help to you