In BlueGranite’s recent webinar, you will see several examples of Python in action for data modeling and visualization in Power BI. You will also learn where and how Python fits into a Power BI development workflow.

You’ll also see how to balance Python with native Power BI functionality and determine what limitations must be considered when using Python in Power BI.

Are you looking to gain more control of the costs of operating your growing Azure HDInsight clusters?

Are you interested in driving higher utilization of your Azure HDInsight clusters?

If yes, then you should definitely watch this video to learn about how to leverage Azure HDInsight Autoscale feature to help you achieve higher cost efficiency.

Time Index:

  • [00:33] Introduction
  • [01:00] Customer challenge
  • [01:55] Static clusters
  • [03:40] Solution is HDInsightAutoscale
  • [06:00] Demo of deployment
  • [09:05] Demo of load-balanced scaling
  • [10:00] Demo of scheduled scaling
  • [11:20] Validating cluster size

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.

Here’s an interesting talk on Dask from AnacondaCon 2018.

Tom Augspurger. Scikit-Learn, NumPy, and pandas form a great toolkit for single-machine, in- memory analytics. Scaling them to larger datasets can be difficult, as you have to adjust your workflow to use chunking or incremental learners. Dask provides NumPy- and pandas-like data containers for manipulating larger than memory datasets, and dask-ml provides estimators and utilities for modeling larger than memory datasets.

These tools scale your usual workflow out to larger datasets. We’ll discuss some of the challenges data scientists run into when scaling out to larger datasets. We’ll then focus on demonstrations of how dask and dask-ml solve those challenges. We’ll see examples of how dask can expose a cluster of machines to scikit-learn’s built-in parallelization framework. We’ll see how dask-ml can train estimators on large datasets.AnacondaCon 2018. Tom Augspurger. Scikit-Learn, NumPy, and pandas form a great toolkit for single-machine, in- memory analytics.

Scaling them to larger datasets can be difficult, as you have to adjust your workflow to use chunking or incremental learners. Dask provides NumPy- and pandas-like data containers for manipulating larger than memory datasets, and dask-ml provides estimators and utilities for modeling larger than memory datasets. These tools scale your usual workflow out to larger datasets. We’ll discuss some of the challenges data scientists run into when scaling out to larger datasets. We’ll then focus on demonstrations of how dask and dask-ml solve those challenges. We’ll see examples of how dask can expose a cluster of machines to scikit-learn’s built-in parallelization framework. We’ll see how dask-ml can train estimators on large datasets.

Learn about what is Spark and using it in Big Data Clusters.

Time index

  • [00:00] Introduction
  • [00:30] One-sentence definition of Spark
  • [00:47] Storing Big Data
  • [01:44] What is Spark?
  • [02:35] Language choice
  • [03:27] Unified compute engine
  • [04:57] Spark with SQL Server
  • [05:47] Learning more
  • [06:10] Wrap-up

Learn how to use Azure Maps (aka.ms/MapsTechCommunity) to analyze a catchment area around a retail store. ShiSh

Shridhar, Principal PM in the Azure IoT Team , who spent 15 years on the Microsoft Industry Retail team, will demo how to build a catchment analysis for a café in downtown Seattle.

Learn how Azure Maps can pull in data on locations, competitors, traffic, and public transit through Microsoft partnerships with TomTom and Moovit.

Azure Maps also ingests data from any data provider including companies that offer human mobility data.

Watch the video below to see how a catchment analysis can help a retailer analyze business disruptors, decide where best to open a store, or identify opportunities for expanding business.

Related Resources

Python and Scala are two of the most popular languages used in data science and analytics.

Not too long ago, the data science language debate was centered around R vs. Python. Now the chatter has shifted towards Python vs. Scala.

Both languages provide great support in order to create cutting edge data analytics projects efficiently.

This article from Analytics India Magazine lists the differences between these two popular languages.

In this article, explore how Reddit has been using machine learning and what its plans are for the future.

Let’s take a walk through the history of machine learning at Reddit from its original days in 2006 to where we are today, including the pitfalls and mistakes made as well as their current ML projects and future efforts in the space. Based on a talk given by Anand […]

You can now derive rich insights from your IoT data in Azure Time Series Insights using advanced visualization options.

The team has introduced  several new capabilities into TSI Explorer since we launched last December. These include significant Performance improvements, new Explorations like Scatter Plots & Heatmaps, as well as an enhanced JS SDK and more. Rahul Kayal, PM in the TSI team walks us through the latest additions and enhancements in TSI.

Check this video out to learn more.     

Try Azure Time Series Insights today: https://aka.ms/tsipreview
Check out the JS SDK for TSI in action: https://aka.ms/tsiclientdemos
Try Azure IoT for free today: https://aka.ms/aft-iot