machine learning models
Machine learned models and data-driven systems are being increasingly used to help make decisions in application domains such as financial services, healthcare, education, and human resources. With the goal that a significant portion of these decision systems becoming fully-automated, there…
Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. In this session, we provide an overview for one of…
Using data in your data warehouse for machine learning use cases like churn prediction can be complicated because of the different tools and skills required. In this session, learn how with Amazon Redshift Machine Learning, you can use SQL to…
This video is a tutorial on the AWS Deep Racer. I go through all the bells and whistle of the service and you will learn how to create your own self-driving car model. Race in the virtual track and get…
AWS provides ML services for every use case so Startups of any size can launch immediately. AWS is among the top rated on Stanford’s 2020 deep learning benchmark, DAWNBench, for the fastest training time, lowest cost, lowest inference latency, and…
Ever wonder how data scientists at digital freight marketplace Convoy are able to find the most cost-efficient trucking routes or how healthcare Startup SyntheticGestalt is taking the drug discovery process from 4 years down to 9 hours to accelerate scientific…
Cloud computing gives you a number of advantages, such as the ability to scale your web application or website on demand. If you have a new web application and want to use cloud computing, you might be asking yourself, “Where…
This tutorial will show how to auto create machine learning models with full visibility using Amazon SageMaker Autopilot. Typical approaches to automated machine learning do not give you the insights or the logic that went into creating the model. In…