Amazon SageMaker Neo was launched at AWS re:Invent 2018. It made notable performance improvement on models with statically known input and output data shapes, typically image classification models. These models are usually composed of a stack of blocks that contain compute-intensive operators, such as convolution and matrix multiplication. Neo applies a series of optimizations to boost the model's performance and reduce memory usage. The static feature significantly simplifies the compilation, and you can decide on runtime inference tasks such as memory sizes ahead of time using a dedicated analysis pass.

Core ML is a machine learning (ML) model format created and supported by Apple that compiles, deploys, and runs on Apple devices. Developers who train their models in popular frameworks such as TensorFlow and PyTorch convert models to Core ML format to deploy them on Apple devices. Neo is an ML model compilation service on AWS that enables you to automatically convert models trained in TensorFlow, PyTorch, MXNet, and other popular frameworks, and optimize them for the target of your choice. With the new automated model conversion to Core ML, Neo now makes it easier to build apps on Apple's platform to convert models from popular libraries like TensorFlow and PyTorch to Core ML format. In this post, we show how to set up automatic model conversion, add a model to your app, and deploy and test your new model.

Various machine learning (ML) optimizations are possible at every stage of the flow during or after training. Model compiling is one optimization that creates a more efficient implementation of a trained model. In 2018, we launched Amazon SageMaker Neo to compile machine learning models for many frameworks and many platforms. We created the ML compiler service so that you don't need to set up compiler software, such as TVM, XLA, Glow, TensorRT, or OpenVINO, or be concerned with tuning the compiler for best model performance. Since then, we have updated Neo to support more operators and expand model coverage for TensorFlow, PyTorch, and Apache MXNet (incubating).

Artificial intelligence (AI) is slowly becoming more mainstream, as companies amass large amounts of data and look for the right technologies to analyze and leverage it. That's why Gartner predicted that 80% of emerging technologies will have AI foundations by 2021. With the trend towards predictive analytics, machine learning and other data sciences already underway, marketers need to start paying attention to how they can leverage these techniques to form a more data-driven marketing strategy. With this in mind, we've asked AI industry experts why marketing leaders need to start considering AI, and some of the best open-source AI frameworks to keep tabs on. Dean Abbott, chief data scientist and co-founder of SmarterHQ, believes AI should be top of mind for most business leaders.

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.