ML Workflows without Code using Microsoft Flow #ArtificicalIntelligence #MachineLearning #MicrosoftFlow #CognitiveServices @MicrosoftFlow @sbisson

Microsoft Flow templates for social media from


Microsoft Flow allows users to connect applications to improve workflows and increase productivity. These automated workflows are used for simple tasks like collecting twitter messages into an excel file OR sending a text message when you receive an email from your boss.

Turn repetitive tasks into multistep workflows. For example, with a few clicks capture tweets and add them as leads in Dynamics 365, subscribers in Mailchimp, and more…

Recently, Microsoft added several machine learning connectors to Flow for computer vision, text analytics and more! In one example, the computer vision connector was used to create a tool for content moderators. This Flow flags racy images uploaded to SharePoint and sends an email to the content moderator for review.

@sbisson recently covered AI and Microsoft Flow on Infoworld and highlights that these flows can be created without writing any code making it easy for anyone to put together a simple app. With new Flow connectors added regularly and the addition of cognitive services, this lowers the barrier to entry for getting a machine learning workflow up and running.

Tools such as Flow (and the related Logic Apps) are a useful way to implement simple apps that link disparate microservices. There’s no need to think about infrastructure, only the services you’re using as triggers and actions. As an abstraction of common distributed application design patterns, it’s a useful way to work, especially if you’re building an application that won’t get a lot of demand or where it’s hard to justify more development resources.

Adding machine learning to Flow gives you more tools for processing data as it passes through a workflow, either deriving insights or transforming content. Using Azure Cognitive Services this way gives you access to both pretrained and, in the case of Custom Vision, custom models. It’s a combination that should get you working with machine learning apps a lot more quickly than traditional development techniques, developing skills and concepts that can be used across your entire business.

If you would like to see some more examples of Flows in action check out these videos. If you’d like to learn more about cognitive services on Azure take a look here.

As 2022 starts, let’s take some time to share our goals for CircuitPython in 2022. Just like past years (full summary 2019, 2020, and 2021), we’d like everyone in the CircuitPython community to contribute by posting their thoughts to some public place on the Internet. Here are a few ways to post: a video on YouTub, a post on the CircuitPython forum, a blog post on your site, a series of Tweets, a Gist on GitHub. We want to hear from you. When you post, please add #CircuitPython2022 and email to let us know about your post so we can blog it up here.

Stop breadboarding and soldering – start making immediately! Adafruit’s Circuit Playground is jam-packed with LEDs, sensors, buttons, alligator clip pads and more. Build projects with Circuit Playground in a few minutes with the drag-and-drop MakeCode programming site, learn computer science using the CS Discoveries class on, jump into CircuitPython to learn Python and hardware together, TinyGO, or even use the Arduino IDE. Circuit Playground Express is the newest and best Circuit Playground board, with support for CircuitPython, MakeCode, and Arduino. It has a powerful processor, 10 NeoPixels, mini speaker, InfraRed receive and transmit, two buttons, a switch, 14 alligator clip pads, and lots of sensors: capacitive touch, IR proximity, temperature, light, motion and sound. A whole wide world of electronics and coding is waiting for you, and it fits in the palm of your hand.

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