Let's learn about Amazon SageMaker (New Updates):
SageMaker Autopilot is the industry’s first automated machine learning capability that gives complete control and visibility into ML models.
Autopilot automatically inspects raw data, applies feature processors, picks the best set of algorithms, trains & tunes multiple models.
Users get full visibility into how the model was created and what’s in it & SageMaker Autopilot integrates with SageMaker Studio.
SageMaker Autopilot can be used by people without machine learning experience to easily produce a model.
SageMaker provides a full end-to-end workflow, but users can continue to use their existing tools with SageMaker.
SageMaker allows users to select the number and type of instance used for the hosted notebook, training & model hosting.
SageMaker stores code in ML storage volumes, secured by security groups and optionally encrypted at rest.
SageMaker Studio provides a single, web-based visual interface where users can perform all ML development steps.
SageMaker Studio gives users complete access, control & visibility into each step required to build, train & deploy models.
SageMaker Autopilot is a generic automatic ML solution for classification and regression problems, such as fraud detection, churn analysis & targeted marketing.
Users can train models using SageMaker Autopilot and get full access to the models as well as the pipelines that generated the models.
SageMaker Autopilot supports 2 built-in algorithms at launch: XGBoost and Linear Learner.
Amazon SageMaker Autopilot built-in algorithms support distributed training out of the box.
Sagemaker - Jupyter notebooks are supported.
SageMaker Notebooks provide one-click Jupyter notebooks that users can start working with in seconds.
With SageMaker Notebooks users can sign in with their corporate credentials using SSO and start working with notebooks within seconds.
SageMaker Notebooks give users access to all SageMaker features, such as distributed training, batch transform, hosting & experiment management.
SageMaker Ground Truth provides automated data labeling using machine learning.
SageMaker Ground Truth will first select a random sample of data and send it to Mechanical Turk to be labeled.
SageMaker Experiments helps users organize and track iterations to machine learning models.
SageMaker Experiments helps users manage iterations by automatically capturing the input parameters, configurations and results, and storing them as experiments.
SageMaker Debugger makes the training process more transparent by automatically capturing real-time metrics during training such as training and validation, confusion matrices & learning gradients to help improve model accuracy.
The metrics from SageMaker Debugger can be visualized in SageMaker Studio for easy understanding.
SageMaker Debugger can also generate warnings and remediation advice when common training problems are detected.
SageMaker RL includes RL toolkits such as Coach and Ray RLLib that offer implementations of RL agent algorithms such as DQN, PPO, A3C, and many more.
A Points to remember series by Piyush Jalan.