sagemaker
Cloud security at AWS is the highest priority. At AWS, building a secure environment from our data centers to our network architecture is of paramount importance. The same principles apply to machine learning where we provide a secure environment using…
This session takes you through the journey of building Enterprise Scale ML workflows on Kubernetes and Amazon SageMaker with Kubeflow Pipelines. Kubeflow is a popular open-source machine learning (ML) toolkit for Kubernetes users who want to build custom ML pipelines.…
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…
Preparing training data is a critical step in machine learning. Preparing data involves creating labelled data, creating features, visualizing the features, and processing the data so it can be made available for training. In this session, learn how to use…
Amazon FSx for Lustre is a high-performance file system for processing Amazon S3 or on-premises data. Learn more at - https://amzn.to/2GglrJa. With Amazon FSx for Lustre, you can launch and run a Lustre file system that can process massive data…
In this session, learn more about Amazon SageMaker Edge Manager, a new capability of Amazon SageMaker that helps developers operate machine learning (ML) models on a fleet of edge devices and solve challenges with constraints and maintenance of ML models…
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…
Insufficient enterprise AI adoption often happens due to lack of time, data, and skills required to develop ML models for solving your business problems. In this session, learn how pretrained ML models which is available in AWS Marketplace and deployed…
AWS offers many choices for solving business problems through machine learning (ML), ranging from built-in algorithms to frameworks and more in using ML services. Amazon SageMaker supports the different built-in ML algorithms, such as classification, regression, and recommendation. Built-in algorithms…
Machine learning workflows are hard to build because you need to create hundreds of code packages for data preparation, model training and deployment, and stitch them together so they run as a sequence of steps. In this session, learn about…