As customers continue to come up with new use-cases for machine learning, data gravity is as important as ever. Where latency and network connectivity is not an issue, generating data in one location (such as a manufacturing facility) and sending it to the cloud for inference is acceptable for some use-cases. With other critical use-cases, such as fraud detection for financial transactions, product quality in manufacturing, or analyzing video surveillance in real-time, customers are faced with the challenges that come with having to move that data to the cloud first. One of the challenges customers are facing with performing inference in the cloud is the lack of real-time inference and/or security requirements preventing user data to be sent or stored in the cloud. Tens of thousands of customers use Amazon SageMaker to accelerate their Machine Learning (ML) journey by helping data scientists and developers to prepare, build, train, and deploy machine learning models quickly.

Last AWS re:Invent, we announced the general availability of Amazon EMR on Amazon Elastic Kubernetes Service (Amazon EKS), a new deployment option for Amazon EMR that allows customers to automate the provisioning and management of Apache Spark on Amazon EKS. With Amazon EMR on EKS, customers can deploy EMR applications on the same Amazon EKS cluster as other types of applications, which allows them to share resources and standardize on a single solution for operating and managing all their applications. Customers running Apache Spark on Kubernetes can migrate to EMR on EKS and take advantage of the performance-optimized runtime, integration with Amazon EMR Studio for interactive jobs, integration with Apache Airflow and AWS Step Functions for running pipelines, and Spark UI for debugging. When customers submit jobs, EMR automatically packages the application into a container with the big data framework and provides prebuilt connectors for integrating with other AWS services. EMR then deploys the application on the EKS cluster and manages running the jobs, logging, and monitoring.

I made my first trip to China in late 2008. I was able to speak to developers and entrepreneurs and to get a sense of the then-nascent market for cloud computing. With over 900 million Internet users as of 2020 (according to a recent report from China Internet Network Information Center), China now has the largest user base in the world. A limited preview of the China (Beijing) Region was launched in 2013 and brought to general availability in 2016. A year later the AWS China (Ningxia) Region launched.

Amazon SageMaker is now available in the Africa (Cape Town) and Europe (Milan) AWS regions. 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. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models.

In this article, I am going to show you how to store a Tensorflow model in a file, upload it to Amazon S3, and configure the Docker image of Tensorflow Serving to serve that model via REST API. Before we start, we have to save a Tensorflow model in a file using the simple_save function. I'm going to assume that you have already trained your model. We need to specify the output directory and make sure that such a location exists. When the target directory is ready, we can call the simple_save function.

In this article, I am going to show you how to store a Tensorflow model in a file, upload it to Amazon S3, and configure the Docker image of Tensorflow Serving to serve that model via REST API. Before we start, we have to save a Tensorflow model in a file using the simple_save function. I'm going to assume that you have already trained your model. We need to specify the output directory and make sure that such a location exists. When the target directory is ready, we can call the simple_save function.

Amazon Web Services (AWS) has connected the Middle East to its global network with the launch of its Bahrain AWS region. The cloud supplier already has infrastructure in the region, but the launch of the Bahrain AWS region, with three datacentres, will connect to its global network. This will bring the Middle East region up to par with its other global AWS regions as the Middle East accelerates its digital transformation. Andy Jassy, CEO at AWS, said the cloud could unlock digital transformation in the Middle East. "Today, we are launching advanced and secure technology infrastructure that matches the scale of our other AWS regions around the world and are already seeing strong demand in the Middle East for AWS technologies like artificial intelligence (AI) and machine learning, data analytics, IoT [internet of things] and much more," he said.

Last year we launched new AWS Regions in France and China (Ningxia), and announced that we are working on regions in Bahrain, Hong Kong SAR, Sweden, and a second GovCloud Region in the United States. South Africa in Early 2020 Today, I am happy to announce that we will be opening an AWS Region in South Africa in the first half of 2020. The new Region will be based in Cape Town, will be comprised of three Availability Zones, and will give AWS customers and partners the ability to run their workloads and store their data in South Africa. The addition of the AWS Africa (Cape Town) Region will also enable organizations to provide lower latency to end users across Sub-Saharan Africa and will enable more African organizations to leverage advanced technologies such as Artificial Intelligence, Machine Learning, Internet of Things (IoT), mobile services, and more to drive innovation. AWS customers are already making use of 55 Availability Zones across 19 infrastructure regions worldwide.