Category: Amazon SageMaker Autopilot

September 2023: This post was reviewed and updated for accuracy. Data fuels machine learning (ML); the quality of data has a direct impact on the quality of ML models. Therefore, improving data quality and employing the right feature engineering techniques are critical to creating accurate ML models. ML practitioners often tediously iterate on feature engineering, […]

Amazon SageMaker Autopilot helps you complete an end-to-end machine learning (ML) workflow by automating the steps of feature engineering, training, tuning, and deploying an ML model for inference. You provide SageMaker Autopilot with a tabular data set and a target attribute to predict. Then, SageMaker Autopilot automatically explores your data, trains, tunes, ranks and finds […]

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 […]

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 […]

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. Amazon SageMaker Autopilot makes it possible for organizations to quickly build and deploy an end-to-end machine learning (ML) model and inference pipeline with just a few lines of code or even without […]

Starting today, you can use Amazon SageMaker Autopilot to tackle regression and classification tasks on large datasets up to 100 GB. Additionally, you can now provide your datasets in either CSV or Apache Parquet content types. Businesses are generating more data than ever. A corresponding demand is growing for generating insights from these large datasets […]

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. Examples of such […]

Whether you are developing a machine learning (ML) model for reducing operating cost, improving efficiency, or improving customer satisfaction, there are no perfect solutions when it comes to producing an effective model. From an ML development perspective, data scientists typically go through stages of data exploration, feature engineering, model development, and model training and tuning […]

Data generates new value to businesses through insights and building predictive models. However, although data is plentiful, available data scientists are far and few. Despite our attempts in recent years to produce data scientists from academia and elsewhere, we still see a huge shortage that will continue into the near future. To accelerate model building, […]

Machine learning (ML) is used throughout the financial services industry to perform a wide variety of tasks, such as fraud detection, market surveillance, portfolio optimization, loan solvency prediction, direct marketing, and many others. This breadth of use cases has created a need for lines of business to quickly generate high-quality and performant models that can […]