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SAS and Microsoft Certifications for Data Scientists

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There are numerous reasons why a data scientist would be interested in a SAS or Microsoft professional certification. First, it is a great way to pick up a new skill or even improve an existing skill. Certifications can help with professional and career development. And now, you can even take certification exams from the comfort of your own home. I've had the opportunity to earn several SAS and Microsoft certifications, so in today's article, I want to share my thoughts around each one to help you decide which is right for you!


Hyperparameter tuning a model - Azure Machine Learning

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Each time the training script logs the primary metric counts as one interval. This is an optional parameter that avoids premature termination of training runs by allowing all configurations to run for a minimum number of intervals. Each time the training script logs the primary metric counts as one interval. This is an optional parameter that avoids premature termination of training runs by allowing all configurations to run for a minimum number of intervals.


Microsoft previews AI for generating Power Apps formulas from natural language, examples

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Microsoft will use OpenAI's GPT-3 language model and "other Microsoft AI technology" to generate Power Platform formulas, known as Power Fx, using natural language input from users. "Now you'll be able to simply tell Power Apps what you'd like to see--for example, 'show me customers from the US whose subscription expired'--and a set of formulas will be presented along with an explanation of how they work," explained Power Apps director of program management Ryan Cunningham. The preview for the new toolset, called Power Apps Ideas, is due in June and will be built into Power Apps Studio. Microsoft introduced Power Fx in March 2021 as a low-code programming language designed to eventually be used across all Power Platform tools. Microsoft invested $1 billion in an AI platform with OpenAI in 2019.



MLOps for Python models using Azure Machine Learning - Azure Reference Architectures

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This reference architecture shows how to implement continuous integration (CI), continuous delivery (CD), and retraining pipeline for an AI application using Azure DevOps and Azure Machine Learning. The solution is built on the scikit-learn diabetes dataset but can be easily adapted for any AI scenario and other popular build systems such as Jenkins or Travis. A reference implementation for this architecture is available on GitHub. This build and test system is based on Azure DevOps and used for the build and release pipelines. Azure Pipelines breaks these pipelines into logical steps called tasks.


Microsoft details the latest developments in machine learning at GTC 21

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This article is part of the VB Lab Microsoft / NVIDIA GTC insight series. With the rapid pace of change taking place in AI and machine learning technology, it's no surprise Microsoft had its usual strong presence at this year's Nvidia GTC event. Representatives of the company shared their latest machine learning innovations in multiple sessions, covering inferencing at scale, a new capability to train machine learning models across hybrid environments, and the debut of the new PyTorch Profiler that will help data scientists be more efficient when they're analyzing and troubleshooting ML performance issues. In all three cases, Microsoft has paired its own technologies, like Azure, with open source tools and NVIDIA's GPU hardware and technologies to create these powerful new innovations. Much is made of the costs associated with collecting data and training machine learning models.



Azure-Samples/mlops-enterprise-template

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This template shows how to perform DevOps for Machine learning applications using Azure Machine Learning powered GitHub Actions. Using this template you will be able to setup your train and deployment infra, train the model and deploy them in an automated manner. If you don't have an Azure subscription, create a free account before you begin. Try the free or paid version of Azure Machine Learning today. To get started with ML Ops, simply create a new repo based off this template, by clicking on the green "Use this template" button: An Azure service principal needs to be generated.


Enabling Edge AI - Connected World

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This week we saw a big announcement that gets us further on our hybrid cloud journey--one where cloud strategies will also include edge and hybrid investments and companies can extend compute and AI (artificial intelligence) to the edge of the network. Microsoft introduces Azure Percept at its Ignite digital conference this week, which is a platform with added security for creating Azure AI technologies and solutions on the edge. The end-to-end edge AI platform includes hardware accelerators integrated with Azure AI and IoT (Internet of Things) services and pre-built AI models--for vision capabilities including object detection, shelf analytics, vehicle analytics, and audio capabilities like voice control and anomaly detection--and solution management to help go from prototype to production in minutes. This is big news especially as companies and partners look to digital transformation with great fervor. The goal of the Azure Percept platform is to simplify the process of developing, training, and deploying edge AI solutions, making it easier for more customers to take advantage of these kinds of offerings, according to Moe Tanabian, Microsoft vice president and GM of the Azure edge and devices group.


What is automated ML? AutoML - Azure Machine Learning

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Choose a local compute: If your scenario is about initial explorations or demos using small data and short trains (i.e. There is no setup time, the infrastructure resources (your PC or VM) are directly available. Choose a remote ML compute cluster: If you are training with larger datasets like in production training creating models which need longer trains, remote compute will provide much better end-to-end time performance because AutoML will parallelize trains across the cluster's nodes. On a remote compute, the start-up time for the internal infrastructure will add around 1.5 minutes per child run, plus additional minutes for the cluster infrastructure if the VMs are not yet up and running. Choose a local compute: If your scenario is about initial explorations or demos using small data and short trains (i.e.