In this episode of CodeStories, Seth Juarez joins local Cloud Advocate, Christopher Maneu, on a tour of the Microsoft office in Paris, his remote office,  and a scuba diving club.

Learn how Christopher has automated a logbook with IoT Retrofitting

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Azure machine learning datasets is a great solution to manage your data for machine learning.

With datasets, you can directly access data from multiple sources without incurring extra storage cost; load data for training and inference through unified interface and built in support for open source libraries; track your data in ML experiments for reproducibility.

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Recently the Global AI Community held their annual Global AI Bootcamp where Eric Boyd (Corporate Vice President of Azure AI) was the keynote speaker.

Due to the scheduling of the event, I was unable to make the DC one.

In this video, Eric Boyd and Seth Juarez discuss Azure AI’s strategy and focus for the future, how users of all skill level can build ML models, and heavy investments based on customer demand.

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With ever changing data and customer signals continuous training and retraining can incur higher costs, especially on GPUs for deep learning models.

In AzureML, Microsoft has heard this concern loud and clear, and we want to share some tips to manage your costs and spend your budget wisely.

Come learn about the latest updates to AMLcompute including some tips on saving costs.

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How can organizations do more with their data using the and AI capabilities of Azure Cognitive Search.

Watch this video to see how they can take data from databases and files and easily mine it for additional knowledge, allowing them to better explore and understand their information.


Additional information:

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Azure Machine Learning has a large library of algorithms from the classification, recommender systems, clustering, anomaly detection, regression and text analytics families.

Each is designed to address a different type of machine learning problem.

In this demo, you will learn how to use Azure Machine Learning designer in a few simple steps and create an end-to-end machine learning pipeline for your data science scenario.

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This epsisode of the AI Show talks about the new ML assisted data labeling capability in Azure Machine Learning Studio.

You can create a data labeling project and either label the data yourself, or take help of other domain experts to create labels for you. Multiple labelers can use browser based labeling tools and work in parallel.

As human labelers create labels, an ML model is trained in the background and its output is used to accelerate the data labeling workflow in various ways such as active learning, task clustering, and pre-labeling. Finally, you can export the labels in different formats.

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