This blog posts looks at using Apache Groovy with the Deep Java Library (DJL) and backed by the Apache MXNet engine to detect objects within an image. Deep learning falls under the branches of machine learning and artificial intelligence. It involves multiple layers (hence the "deep") of an artificial neural network. There are lots of ways to configure such networks and the details are beyond the scope of this blog post, but we can give some basic details. We will have four input nodes corresponding to the measurements of our four characteristics.

Goto: Amazon DynamoDB: Building NoSQL Database-Driven ApplicationsThis course introduces you to NoSQL databases and the challenges they solve. Expert instructors will dive deep into Amazon DynamoDB topics such as recovery, SDKs, partition keys, security and encryption, global tables, stateless applications, streams, and best practices. DynamoDB is a key-value and document database that delivers single-digit millisecond performance at any scale. It's a fully managed, multiregion, multimaster database with built-in security, backup and restore, and in-memory caching for internet-scale applications. DynamoDB can handle more than 10 trillion requests per day and support peaks of more than 20 million requests per second.

We have seen an explosion in developer tools and platforms related to machine learning and artificial intelligence during the last few years. From cloud-based cognitive APIs to libraries to frameworks to pre-trained models, developers make many choices to infuse AI into their applications. AI engineers and researchers choose a framework to train machine learning models. These frameworks abstract the underlying hardware and software stack to expose a simple API in languages such as Python and R. For example, an ML developer can leverage the parallelism offered by GPUs to accelerate a training job without changing much of the code written for the CPU. These frameworks expose simpler APIs that translate to complex mathematical computations and numerical analysis often needed for training the machine learning models. Apart from training, the machine learning frameworks simplify inference -- the process of utilizing a trained model for performing prediction or classification of live data.

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. Amazon SageMaker removes the heavy lifting from each step of the ML process to make it easier to develop high-quality models. As datasets continue to increase in size, additional compute is required to reduce the amount of time it takes to train. One method to scale horizontally and add these additional resources on Amazon SageMaker is through the use of Horovod and Apache MXNet. In this post, we show how you can reduce training time with MXNet and Horovod on Amazon SageMaker.

Amazon recently bought up a self-driving autonomous ride-hailing startup Zoox, which is being claimed as the most ambitious step that the tech giant has taken in the recent past. Reportedly a $1.2 billion deal, the acquisition of the Robo-taxi company is not just to build upon its capabilities to deliver packages but actively set foot in the autonomous driving industry. While Amazon has invested heavily in developing drones or autonomous delivery robots in the past, its investment in self-driving vehicles has recently gained traction. Some of the other ventures of the company have been in self-driving truck Embark when CNBC reported that it had been hauling Amazon cargo on some of its test runs. For instance, in drones, Amazon has designed a future delivery system to safely deliver packages to customers in a short period of time.

Amazon wants to make it easier to get AI-powered apps up and running on Amazon Web Services. Toward that end, it today launched AWS Deep Learning Containers, a library of Docker images preinstalled with popular deep learning frameworks. "We've done all the hard work of building, compiling, and generating, configuring, optimizing all of these frameworks, so you don't have to," Dr. Matt Wood, general manager of deep learning and AI at AWS, said onstage at the AWS Summit in Santa Clara this morning. "And that means that you do less of the undifferentiated heavy lifting of installing these very, very complicated frameworks and then maintaining them." The new AWS container images in question -- which are preconfigured and validated by Amazon -- support Google's TensorFlow machine learning framework and Apache MXNet, with Facebook's PyTorch and other deep learning frameworks to come.

At re:Invent 2018, AWS announced Amazon Elastic Inference (EI), a new service that lets you attach just the right amount of GPU-powered inference acceleration to any Amazon EC2 instance. This is also available for Amazon SageMaker notebook instances and endpoints, bringing acceleration to built-in algorithms and to deep learning environments. In this blog post, I show how to use the models in the ONNX Model Zoo on GitHub to perform inference by using MXNet with Elastic Inference Accelerator (EIA) as a backend. Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75 percent. Amazon Elastic Inference provides support for Apache MXNet, TensorFlow, and ONNX models.

When it comes to deep learning frameworks, TensorFlow is one of the most preferred toolkits. However, one framework that is fast becoming the favorite of developers and data scientists is PyTorch. PyTorch is an open source project from Facebook which is used extensively within the company. For a long time, Facebook developers used another homegrown framework called Caffe2, which was adopted by academia and researchers. Last year, Facebook announced that it is merging the efforts of developing Caffe2 and PyTorch to focus on creating a unified framework that is accessible to the community.

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.

Many state of the art deep learning models have hefty compute, storage and power consumption requirements which make them impractical or difficult to use on resource-constrained devices. In this tech talk, you'll learn why Apache MXNet, an open Source library for Deep Learning, is IoT-friendly in many ways. In addition, you'll learn how services like AWS Lambda and AWS Greengrass make it easy to deploy MXNet models on edge devices.