Powerpoint Designer utilizes machine learning to provide users with redesigned slides to maximize their engagement and visual appeal.

Up to 4.1 million Designer slides are created daily and the Designer team is adding new types of content continuously.

Time Index:

  • [02:39] Demo – PowerPoint suggests design ideas to help users build memorable slides effortlessly
  • [03:28] A behind-the-scenes look at how PowerPoint was built to make intelligent design recommendations
  • [04:47] AI focused on intelligently cropping images in photos and centering the objects, positioning the images, and even using multi-label classifiers to determine the best treatment.
  • [06:00] How PowerPoint is solving for Natural Language Processing (NLP).
  • [07:32] Providing recommendations when image choices don’t meet the users’ needs.
  • [09:30] How Azure Machine Learning helps the dev team scale and increase throughput for data scientists.
  • [11:10] How distributed GPUs helps the team work more quickly and run multiple models at once.

Azure Synapse is a limitless analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs. All of this leverages our limitless Azure Data Lake Storage service for any type of data.

Microsoft Mechanics explains.

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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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.

Additional information:

Azure Machine Learning compute instances (formerly Notebook VMs) is a hosted PaaS offering that supports the full lifecycle of inner-loop ML development–from model authoring, to model training and model deployment.

AzureML Compute Instances are deeply integrated with AzureML workspaces and provide a first-class experience for model authoring through integrated notebooks using AzureML Python and R SDK.

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The AI Show’s Favorite links:

This episode of the AI show provides a quick overview of new batch inference capability that allows Azure Machine Learning users to get inferences on large scale datasets in a secure, scalable, performant and cost-effective way by fully leveraging the power of cloud.

Learn More:

Batch Inference Documentation

https://aka.ms/batch-inference-documentation

Batch Inference Notebooks

https://aka.ms/batch-inference-notebooks

Azure Machine Learning now offers two editions that are tailored for your machine learning needs, Enterprise and Basic, making it easy for developers and data scientists to accelerate the end to end machine learning lifecycle. The Basic edition is a one stop destination for open source developers and data scientists who are comfortable with a code first experience. The Enterprise edition boosts productivity with no-code machine learning tools for all ML skill levels, and is tailored for enterprises of various sizes, developers, data engineers or data workers.

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The new Azure Machine Learning studio is an immersive web experience for managing the end-to-end lifecycle.

The new web experience brings all of the data science capabilities for data scientists and engineers, across diverse skill levels from no code authoring, to code-first experiences, and their ML assets together in a single web pane to streamline machine learning.

Learn More:

The AI Show’s Favorite links: