Machine learning is becoming essential for a lot of companies and they want to use it to optimize their operations and make new services. One of the challenges is that sometimes you need to deploy a model in an environment where you have limited internet connection and no operators to manage the infrastructure for ML. In this case, you need to use Machine Learning on Edge and have a way to deploy and monitor your models and applications remotely. AWS Panorama is a machine learning device by AWS with a software development kit and corresponding AWS service which manages devices and applications. It is focused on working with computer vision models and video streams.

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.

LAS VEGAS -- Ambarella, Inc. (Nasdaq: AMBA), an artificial intelligence (AI) vision silicon company, today announced that Ambarella and Amazon Web Services, Inc. (AWS) customers can now use Amazon SageMaker Neo to train machine learning (ML) models once and run them on any device equipped with an Ambarella CVflow -powered AI vision system on chip (SoC). Until now, developers had to manually optimize ML models for devices based on Ambarella AI vision SoCs. This step could add considerable delays and errors to the application development process. Ambarella and AWS collaborated to simplify the process by integrating the Ambarella toolchain with the Amazon SageMaker Neo cloud service. Now, developers can simply bring their trained models to Amazon SageMaker Neo and automatically optimize the model for Ambarella CVflow-powered SoCs.

LAS VEGAS--(BUSINESS WIRE)--Ambarella, Inc. (Nasdaq: AMBA), an artificial intelligence (AI) vision silicon company, today announced that Ambarella and Amazon Web Services, Inc. (AWS) customers can now use Amazon SageMaker Neo to train machine learning (ML) models once and run them on any device equipped with an Ambarella CVflow -powered AI vision system on chip (SoC). Until now, developers had to manually optimize ML models for devices based on Ambarella AI vision SoCs. This step could add considerable delays and errors to the application development process. Ambarella and AWS collaborated to simplify the process by integrating the Ambarella toolchain with the Amazon SageMaker Neo cloud service. Now, developers can simply bring their trained models to Amazon SageMaker Neo and automatically optimize the model for Ambarella CVflow-powered SoCs.

At re:Invent 2018, AWS added many capabilities to Amazon SageMaker, a machine learning platform as a service. SageMaker Neo was announced as an extension of SageMaker that optimizes fully-trained ML models for various deployment targets. Neo-AI project turns SageMaker Neo into an open source project making it possible for hardware and software vendors to extend the platform. Machine learning models have two distinct phases – training and inference. Data scientists and developers select the right algorithm that's most appropriate for the business problem.

It already has quite a few smart code confections: Rekognition, Lex, Polly, Transcribe, Comprehend, Translate, Sagemaker, and Greengrass, among others. At its re:Invent gathering in Las Vegas today, AWS threw a handful of new flavors into the mix, among them: Elastic Inference, SageMaker GroundTruth, SageMaker RL, Amazon SageMaker Neo, Personalize, Forecast, Textract, and Comprehend Medical. It also teased a machine-learning inference chip called Inferentia, and a small radio-controlled car called DeepRacer for executing autonomous driving models in the real-world and terrifying pets. It's a 1/18th scale race car that's ostensibly intended to help people understand and implement reinforcement learning. It may also help with customer acquisition, retention, and spending.