Learn how to use Azure Machine Learning data labeling to create, manage, and monitor data labeling projects.

With NVIDIA DeepStream, you can seamlessly develop optimized Intelligent Video Applications that can consume multiple video, image, and audio sources. You can also apply single or cascading inference operations to video frames in real-time, and transmit inference results to the cloud for archiving or additional processing.

In this module, you'll publish and deploy an ARM-based DeepStream container workload to NVIDIA embedded hardware by using Azure IoT Edge.

Azure Machine Learning studio is a GUI-based integrated development environment for constructing and operationalizing Machine Learning workflow on Azure. Learn how to develop custom object detection models using this service with NVIDIA GPU accelerated virtual machines.

In this path, you'll learn to design and implement a new AI paradigm called Machine Teaching for Autonomous AI. Machine Teaching uses the knowledge of subject matter experts to teach AI. Machine Teaching integrates known control methods, for stable control, Machine Learning, for advance perception, and Deep Reinforcement Learning, for learning strategies and human-like decision making. The deployment in industrial processes without disruption and providing real business value is considered from its design. Machine Teaching for Autonomous AI stores expert operators' skill set, and it helps companies achieve new levels of optimization for competitiveness, profitability, and sustainability. The hands-on development of Autonomous solutions will be taught using the Microsoft Project Bonsai Platform, a low-code platform where Machine Teaching is used to the implementation, training, and validation of Bonsai brains.

Learn how to use Azure AI Personalizer to empower your apps with smarter decision making through improved recommendations.

Characterize the types of connectivity options available to the Azure Sphere in an Industrial IoT scenario. Describe the functionality of each of the connectivity options and show how they can be combined to solve complex industrial IoT problems.

The interplay between AI, cloud, and edge is a rapidly evolving domain. Currently, many IoT solutions are based on basic telemetry. The telemetry function captures data from edge devices and stores it in a data store. Our approach extends beyond basic telemetry. We aim to model problems in the real world through machine learning and deep learning algorithms and implement the model through AI and Cloud on to edge devices. The model is trained in the cloud and deployed on the edge device. The deployment to the edge provides a feedback loop to improve the business process (digital transformation).

Use the Azure Machine Learning CLI (v2) to create and manage workspace resources.

Learn how to use Automated Machine Learning to train a labeled dataset and develop a production object detection model.

In this module, you'll learn how to set up and configure an x86-based Ubuntu 18.04 system to host an NVIDIA DeepStream development environment.

In this module, you'll learn how to set up and configure the NVIDIA DeepStream 6.0 Graph Composer on an X86-based Ubuntu 18.04 system to enable rapid development of Intelligent Video Analytics application pipelines for deployment to cloud and edge-capable devices.

Use components to easily share and reuse code and use component-based pipelines to create machine learning workflows.

NVIDIA Triton Inference Server is a multi-framework, open-source software that is optimized for inference. It supports popular machine learning frameworks like TensorFlow, Open Neural Network Exchange (ONNX) Runtime, PyTorch, NVIDIA TensorRT, and more. It can be used for your CPU or GPU workloads. In this module, you deploy your production model to NVIDIA Triton server to perform inference on a cloud-hosted virtual machine.

This learning path is a brief introduction to a new AI paradigm called Machine Teaching for Autonomous AI. Machine Teaching uses the knowledge of subject matter experts to teach AI. It integrates known control methods for stable control, Machine Learning for advance perception, and Deep Reinforcement Learning for learning strategies and human-like decision making. It's AI integrated in industrial processes without disruption and providing real business value. It's validated in simulation, explainable that your experts can validate. Machine Teaching for Autonomous AI stores expert operators' skill set, enhancing and homogenizing expert operators' best performance, and/or efficiently training novices on the job when deployed as a Decision Support tool. It helps your company achieve new levels of optimization for competitiveness, profitability, and sustainability. It provides a robust innovation path for all areas of industrial processes and business with real ROI. At the end of this learning path, you'll be able to:

Use the Azure Machine Learning CLI (v2) to train models as jobs. Train a model using a basic Python script, or perform hyperparameter tuning with a sweep job.

In this module, you learn how to create resources for getting started with Azure Machine Learning.

Azure AI Translator is a cloud-based service that uses AI to reliably translate text and documents between languages in near real time. You can add multilanguage user experiences to your apps in 90 languages and dialects, along with free text translation with any operating system. Translator also has customizable translation models that can better understand industry-specific terminology or pronouns.

Implement a cognitive service for performing language detection on an IoT Edge device. Describe the components and steps for implementing a cognitive service on an IoT Edge device.

Implement a neural network model for performing real-time image classification on a secured, internet-connected microcontroller-based device (Azure Sphere). Describe the components and steps for implementing a pre-trained image classification model on Azure Sphere.

Deploy an Azure Sphere application to monitor ambient conditions for a laboratory. The application will monitor the room environment, connect to Azure IoT Central, and send telemetry data from the device to the cloud. You'll control cloud-to-device communications and undertake actions as needed.

Create a computer vision solution on the IoT Edge using Azure AI services and Azure Speech Services. The application will capture and identify scanned item and convert the name of the item to the speech.

Deploy an Azure Sphere device application to monitor ambient conditions for laboratory conditions. The application will monitor the room environment conditions, connect to IoT Hub, and send telemetry data from the device to the cloud. You'll control cloud-to-device communications and undertake actions as needed.

Computer vision is an area of artificial intelligence that deals with visual perception. Azure AI Custom Vision enables you to train models on your own images for customized computer vision scenarios.