Data privacy and security are top of mind for almost every enterprise in the world. But creating machine learning in a manner that is secure and privacy-aware presents specific challenges. Learn how to build and deploy secure, protected and scalable machine learning using Azure Machine Learning. Whether you are targeting the cloud or the edge, this session will help you understand how to apply multi-factor authentication, role-based authorization, data encryption, VNETs, and other security and privacy best practices to the machine learning lifecycle.
From smart sensors and actuating devices to integrated robotic systems, industrial organizations are employing increasingly sophisticated automation technologies to drive efficiency and productivity. Microsoft is accelerating the journey toward a world where machines operate in more dynamic and intuitive ways. From smart buildings, to industrial machinery, to robotics, Microsoft is democratizing the development of increasingly autonomous systems by providing domain experts, developers, and data scientists with the tools they need to seamlessly develop and manage autonomous systems.
Automated ML is an emerging field in Machine Learning that helps developers and new data scientists with little data science knowledge build Machine Learning models and solutions without understanding the complexity of Learning Algorithm selection, and Hyper parameter tuning. With Azure Machine Learning's automated machine learning capability, given a dataset and a few configuration parameters, you will get a trained high quality Machine Learning model for the dataset that you can use for Predictions. You will learn how CBRE & Walgreen-Boots are using it for productivity gains, empowering domain experts to build ML based solutions and scale to build several models with Azure Machine Learning's automated ML.
The holy grail of the Internet of Things is the ability to easily distribute the intelligence of your application across the Cloud and the Edge. Being able to run analytics, AI or store data at the Edge addresses many common and key enterprise IoT scenarios. Come learn how to easily create deployments for IoT devices that include AI, Machine Learning, Stream Analytics, as well as your own custom code on devices smaller than a Raspberry PI.
Everyone wants to do machine learning, but what does it actually take to make it a reality? Azure Machine Learning service accelerates the end-to-end machine learning lifecycle, enabling data scientists and developers to quickly experiment, iterate, and innovate together. We'll discuss what it takes in practice to do machine learning at scale from data to deployment. We'll walk through an example of how Azure Machine Learning service can speed up each step in your machine learning process from data prep, to model creation, to deployment, to management and finally to monitoring. In doing so, we'll showcase key new features that democratize AI, allow mixed-skill teams to collaborate, and enable ML Ops. We'll share these notebooks so you can start experimenting yourself. With Azure Machine Learning service, you'll be able to *actually* do machine learning.
Machine learning development has new complexities beyond software development. There are a myriad of tools and frameworks which make it hard to track experiments, reproduce results and deploy machine learning models. Learn how you can accelerate and manage your end-to-end machine learning lifecycle on Azure Databricks using MLflow and Azure Machine Learning to reliably build, share and deploy machine learning applications using Azure Databricks.
Ever wondered what breed that dog or cat is? Let’s build a pet detector to recognize them in pictures! We will walk through the training, optimizing, and deploying of a deep learning model using Azure Notebooks and the Azure Machine Learning service. We will use transfer learning to retrain a MobileNet model using TensorFlow to recognize dog and cat breeds using the Oxford IIIT Pet Dataset. Next, we’ll optimize the model using Azure Machine Learning service to improve the model accuracy. Putting on our developer hat, we'll then refactor the notebooks into Python modules using Visual Studio Code. Finally, we will deploy the model as a web service in Azure. See how Azure and Visual Studio Code has made AI and machine learning easy.
Azure Machine Learning is one of the most useful Azure services when dealing with AI workflow. In order to integrate the application with AML pipeline, and to use AML more efficiently, additional steps might be taken. In this talk, the AI Customer engineering team will present a set of ‘Tips and Tricks’ based on customer learnings and early product features.
MLOps is DevOps for machine learning and brings continuous integration, delivery and learning to the machine learning lifecycle. This session will show you how to get to market faster with better models, and operationalize more reliably with greater governance. See a real-world fraud detection scenario where online retailers used MLOps to track and identify model production, efficiently validate models and determine best practices. Learn how to store and version models, collect continuous feedback on model behavior. Maintain quality models to keep your business and customers ahead.
Artificial Intelligence is right here, right now—and making AI real for businesses is our goal. The visual workflow capability in Azure Machine Learning service is a powerful simple drag and drop authoring environment with no-code needed. You can go from ideation to deployment in a matter of clicks and visually design your machine learning models with ease. Whether you are new to machine learning, data scientist or a developer, you can leverage this platform to accelerate your data science journey. See in action how our customers empowered their data science teams with real-time AI to jump start production.
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