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re:Invent 2017 – AWS SageMaker, DeepLens Bring Machine Learning To ‘Everyday Devs’ Machine learning is a key component of many Amazon Web Services (AWS), from its database products to security solutions. The company has made a concerted effort in making machine learning more accessible to all its customers and partners, regardless their level of expertise. AWS announced a partnership with Microsoft in October to create “Gluon”, a deep learning library that aims at helping developers build complex models. It also offers machine learning courses for partners at both intermediate and beginner levels. AWS recently launched the Amazon ML Solutions Lab, which connects data scientists with organizations who are starting their first machine-learning projects. AWS announced the next steps in its efforts to democratize machine-learning at its reInvent conference in Las Vegas this week. The new SageMaker machine-learning platform and DeepLens programmable cam are both the result of “a long history of machine learning at Amazon,” according Andy Jassy (AWS CEO), who presented the products during Wednesday’s reInvent keynote. Amazon SageMaker Now available, Amazon SageMaker allows “everyday developers” to easily create and deploy machine-learning models, Jassy stated in his keynote. SageMaker comes pre-configured algorithms or users can create their own. Jassy says that model-training is as simple as one click. Randall Hunt, AWS Senior Technical Advocate, shared more information about SageMaker in a Wednesday blog post. He said SageMaker consists of three parts:


Nov 11, 2022
  • Authoring: Zero-setup hosted Jupyter notebook interfaces for data exploration and cleaning. These can be run on general instances or GPU-powered instances.
  • Model Training: A distributed service for model building, training, validation. You can either use the built-in common unsupervised and supervised learning algorithms and frameworks, or create your own training using Docker containers. To support faster model building, the training can be scaled to hundreds of instances. S3 is used to read the training data and S3 is used to store the model artifacts. The model artifacts refer to the data dependent parameters of your model, not the code that allows for inferences. This separation of concerns allows you to easily deploy Amazon SageMaker-trained models to other platforms, such as IoT devices.
  • Model Hosting: A model hosting platform with HTTPs endpoints that allow you to invoke your models and get real-time inferences. These endpoints are scalable to handle traffic and allow you A/B test multiple models simultaneously. These endpoints can be built using the SDK, or you can create your own configurations using Docker images.

Hunt stated that each component can be used in its own right or in combination. SageMaker is currently only available in the AWS Northern Virginia, Oregon and Ohio regions. You can find more information, including pricing, here. AWS DeepLens Jassy also revealed a new edge device called AWS DeepLens. This allows developers to build machine learning models that incorporate real-world objects such as faces or scenes. DeepLens is a Wi-Fi-enabled portable video camera that features a 4MP lens, 1080P support, and a 2-D microphone array. It is powered by an Intel Atom processor that has 8GB RAM and 16GB expandable memory. It also has USB 2.0, HDMI, and Micro SD ports. It runs Ubuntu 16.04 and includes Greengrass Core. There are also pre-trained machine learning models that can detect and recognize images. [Click on the image to see a larger view.] The specifications of the DeepLens camera. Source: AWS. DeepLens camera specs.

By Delilah