Building machine learning (ML) models has traditionally required a binary choice. On one hand, you could manually prepare the features, select the algorithm, and optimize the model parameters in order to have full control over the model design. However, this approach requires deep ML expertise. On the other hand, if you don’t have that expertise, you could use an automated approach (AutoML) to model generation that takes care of all of the heavy lifting, but provides very little visibility into how the model was created. While a model created with AutoML can work well, you may have less trust in it because you can’t understand what went into it, you can’t recreate it, and you can’t learn best practices which may help you in the future. In this session I am going to show you how SageMaker Autopilot eliminates this choice, allowing you to automatically build machine learning models without compromises, explore different solutions to find the best model, and then directly deploy the model to production with just one click.
Other content in this Stream
For businesses working with AI and ML however, living this never normal is simply ‘business as usual’, where constant change offers abundant opportunities to innovate, and thrive.
In this session, we share specific examples from Amazon.com's consumer/retail and other businesses to explain how AI/ML helps Amazon deliver the best customer experience possible.
Learn how executives and managers who are looking to achieve success using ML to accelerate innovation and drive technological progress.
This session walks technology leaders through the largest Canadian influences to AI/ML including the latest Canadian investment by AWS in this space.
With no prior machine learning experience you can start creating A/B tests to see the impact of Amazon Personalize on increasing user engagement with your recommended products and content.
Discover how using Amazon Textract, Amazon Comprehend, and Amazon Augmented AI provide organizations with a machine learning solutio to overcome document processing and analysis at scale.
Here the latest security updates in the Well-Architected categories of detection, identity management, data protection, and incident response.
This session will provide a high level overview of cognitive search, why it's important, and what it can do for your customers and employees.
Take a deep dive into Amazon Fraud Detector, its use cases in your business, and how to quickly get started.
In this whiteboarding session, learn how to design an ML application guided by the AWS Well-Architected Framework five pillars.
Discover ways to use Apache Spark on AWS to analyze large datasets, perform data quality checks, transform raw data into machine learning features, and train predictive models.
In this session, you'll see how to create, automate, and manage end-to-end ML workflows using Amazon SageMaker Pipelines.
Using the popular Hugging Face Transformers open source library for BERT to train and deploy multiple natural language understanding (NLU) models.
In this session, we walk through how to use the real-time training metrics and set up alerts so you can reduce troubleshooting time, training costs and improve model quality.
Learn how to solve all these problems with Amazon SageMaker Feature Store, and how to use it with both the SageMaker Studio user interface and the SageMaker SDK.
In this session, explore how to choose the proper instance for ML training and inference based on model size, complexity, throughput, framework choice, inference latency and portability requirements.