Amazon SageMaker Autopilot is an automated machine learning (AutoML) solution that performs all the tasks you need to complete an end-to-end machine learning (ML) workflow. It explores and prepares your data, applies different algorithms to generate a model, and transparently provides model insights and explainability reports to help you interpret the results. Autopilot can also create a real-time endpoint for online inference. You can access Autopilot's one-click features in Amazon SageMaker Studio or by using the AWS SDK for Python (Boto3) or the SageMaker Python SDK. In this post, we show how to make batch predictions on an unlabeled dataset using an Autopilot-trained model.

Amazon SageMaker Autopilot automatically builds, trains, and tunes the best machine learning (ML) models based on your data, while allowing you to maintain full control and visibility. We have recently announced support for time series data in Autopilot. You can use Autopilot to tackle regression and classification tasks on time series data, or sequence data in general. Time series data is a special type of sequence data where data points are collected at even time intervals. Manually preparing the data, selecting the right ML model, and optimizing its parameters is a complex task, even for an expert practitioner.

AWS has introduced AI for data analytics (AIDA) partner solutions, which embed predictive analytics into mainstream analytics workspaces. AWS AIDA partner solutions make it possible for business experts to use artificial intelligence (AI) and machine learning (ML) to derive better insights from data and take action. These AI/ML solutions from Amazon Web Services (AWS) Partners have interfaces and integrations that help bring predictive analytics into the normal workflow of business experts, those who use data to run their business, and those who have limited data science experience. AWS AIDA includes partner solutions from Amplitude, Anaplan, Causality Link, Domo, Exasol, InterWorks, Pegasystems, Provectus, Qlik, Snowflake, Tableau, TIBCO, and Workato. Organisations have varying levels of maturity in their analytics journey.

AutoML is a powerful capability, provided by Amazon SageMaker Autopilot, that allows non-experts to create machine learning (ML) models to invoke in their applications. The problem that we want to solve arises when, due to governance constraints, Amazon SageMaker resources can't be deployed in the same AWS account where they are used. This post walks through an implementation using the SageMaker Python SDK. It's divided into two sections: The solution described in this post is provided in the Jupyter notebook available in this GitHub repository. For a full explanation of Autopilot, you can refer to the examples available in GitHub, particularly Top Candidates Customer Churn Prediction with Amazon SageMaker Autopilot and Batch Transform (Python SDK).

A few minutes later, the kernel should be started and ready to go. The following screenshot shows our results. Depending on your preference, you can either create an Autopilot job through the Studio user interface without writing a single line of code, or use the SageMaker SDK in a SageMaker notebook. The following notebook uses the SageMaker SDK to create an Autopilot job. For simplicity, we explore the no code approach using the Studio console to demonstrate these new features.

Amazon SageMaker Autopilot automatically trains and tunes the best machine learning (ML) models for classification or regression problems while allowing you to maintain full control and visibility. This not only allows data analysts, developers, and data scientists to train, tune, and deploy models with little to no code, but you can also review a generated notebook that outlines all the steps that Autopilot took to generate the model. In some cases, you might also want to customize pipelines generated by Autopilot with your own custom components. This post shows you how to create and use models with Autopilot in a couple of clicks, then outlines how to adapt the SageMaker Autopilot generated code with your own feature selectors and custom transformers to add domain-specific features. We also use the dry run capability of Autopilot, in which Autopilot only generates code for data preprocessors, algorithms, and algorithm parameter settings.

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. In this tutorial, you create machine learning models automatically without writing a line of code! You use Amazon SageMaker Autopilot, an AutoML capability that automatically creates the best classification and regression machine learning models, while allowing full control and visibility. For this tutorial, you assume the role of a developer working at a bank. You have been asked to develop a machine learning model to predict whether a customer will enroll for a certificate of deposit (CD).

To enable developers and citizen data scientists to take advantage of machine learning, the industry is moving towards AutoML - a simplified approach to building and training ML models. Training and deploying a sophisticated machine learning model involve multiple phases with each phase demanding a unique skill set. An enterprise data science team consists of data engineers, data scientists, business analysts, researches, ML developers and DevOps professionals to manage the workflow involved in operationalizing AI for businesses. Each stage is handled by an individual or a team specializing in that task. Data engineers deal with the ingestion and acquisition of data from disparate sources.

Typical approaches to automatic ML don't provide insights into the data or logic used to create models, forcing you to compromise on accuracy. Join us as we introduce Amazon SageMaker Autopilot, an automated capability that generates ML models and provides complete control and visibility of them. Learn how Autopilot automatically inspects raw data, picks the best set of algorithms, trains multiple models, tunes them, and ranks them based on performance. The result is a recommendation for the best performing model and visibility into the logic and code for how the model was created and what's in it. Autopilot offers the best combination of automatic model creation with control and visibility.

Today, we're extremely happy to launch Amazon SageMaker Autopilot to automatically create the best classification and regression machine learning models, while allowing full control and visibility. In 1959, Arthur Samuel defined machine learning as the ability for computers to learn without being explicitly programmed. In practice, this means finding an algorithm than can extract patterns from an existing data set, and use these patterns to build a predictive model that will generalize well to new data. Since then, lots of machine learning algorithms have been invented, giving scientists and engineers plenty of options to choose from, and helping them build amazing applications. However, this abundance of algorithms also creates a difficulty: which one should you pick?