In this blog, we are going to see how to create object detection model which can be used in PowerApps or MS Flow / PowerAutomate Step 1: Log in to portal.office.com. Select the PowerApps, If PowerApps is not visible then click on All Apps then you will able to see the PowerApps. Step 2: Expand the AI Builder Section and click on Build Section in PowerApps. Note: Please ensure that you are select the correct instance. Step 3: Click on the Object Detection Model We are going to create the Object Detection Model which can be used to created PowerApps or In MS Flow / Automate. Step 4: Name the AI Model and click on Create. Step 5: We will select the Domain of Model so we will go with Common Object. And click on Next. Step 6: Before moving forward, we will download the data which will be used to train and test the Model. Kaggle is the best source for the data to train the machine learning model. We will require the fruit images as we are designing the Fruit Detection Model, search fruit images and download the Dataset given in the screenshot. Select the Fruit Image for Object Detection Click on the Download Step 7: After downloading the Zip file extract it. You will see the following two folders — train_zip and test_zip respectively. Now, we will open the train_zip folder and you can see that there will be four categories of images Apple Banana Orange Mixed [Apple, Banana, Orange] Step 8: Let move back to the PowerApps Platform, 2nd step in the creation of Object Model is to define the object that we are going to detect. Here, we have three objects — Apple, Banana & Orange and click on next to move further steps. Step 9: We will require a minimum 15 images of each category to train our Object Detection Model. Now, we will click on Add Images and select Upload from local storage. Step 10: Select the images. Once all images are upload click on Close. Click on Next Step 11: Now, we are in the most important phase of the training where we provide the Tag or Label to the Images which we have uploaded. Click on the uploaded image and select the area where the object is present. Once you will select the area you will get the option to select the object is that present in the selection area. If an image is not suitable for the model to remove that particular image click on the “Don’t use Image ”. Click on remove to remove the image from the model data set. Step 12: Once you are done will the tagging or labeling the image click on the Done tagging. Note: Please ensure that you have a more tagged image for each model so that your model will work accurately. The more the label data more the accurate your machine learning model. Step 13: Click on the Train to start the training your model based on tagged images. Click on Go to Model It will take around 5–10 min to train based on your complexity of images, model and objects. Step 14: Publish the model. Once you model finished with done and you have published the Model. You can use that model to in Power App or MS Flow. We are going to see that in the next part of this blog.

Introduction: Microsoft released a PDF cheat sheet of which machine learning algorithms can be used on Azure Machine Learning Studio. This Microsoft Azure Machine Learning Algorithm Cheat Sheet helps you choose the right machine learning algorithm for your predictive analytics solutions from the Microsoft Azure Machine Learning library of algorithms. The algorithms have been grouped in 5 different groups. These groups are: Regression: For predicting values. For Example when predicting a stocks price. Anomaly detection: For finding unusual data points. For example, any highly unusual credit card spending patterns which deviates from the normal credit card spending patterns. Clustering: The data points have no labels associated with them. Instead, the goal of an unsupervised learning algorithm is to organize the data in some way or to describe its structure. For example, discovering companies with similar marketing strategies. Two-class classification: When there are only two choices, it’s called two-class or binomial classification. For example distinguishing between a Cat or Dog. Multi-class classification: For predicting three or more categories. For Example predicting the winner of a Race. To read the cheat sheet, read the path and algorithm labels on the chart as “For <path label>, use <algorithm>.” For example, “For speed, use two class logistic regression.” Sometimes more than one branch applies. In this case it is better to create scored models with both the algorithm and compare both of their accuracy to decide which algorithm is the better fit. Even a beginner can easily use the cheat sheet provided to select which algorithm is apt for creating their predictive solution. There are some generalizations and oversimplifications, but it points you in a safe direction. It also means that there are lots of algorithms not listed here but these many algorithms are more than enough to give you a good head start in the ML world.

Introduction: Azure Machine Learning Studio is an amazing tool that lets us create efficient ML experiments with simple drag and drop features. We can predict anything from Flight Predictions to Churn Analysis. But what if we want to represent this predicted data a more visually appealing format? Well it is possible to do this by representing your predictions on Power BI! Pre-Requisites: Basic Understanding of Azure Machine Learning Studio. Basic Understanding of Power BI. A Blob Container created on Azure Storage.   Steps: Create your Azure Machine Learning Experiment on Azure Machine Learning Studio. Convert your Training Experiment to a Predictive Experiment and Deploy it as a Web Service. We will create a Console application in Visual Studio and copy paste the code inside Batch Execution. For automation we can create automated data pipelines but for now we will just use a simple Console application. Remove the existing code from the Console Application and copy paste the Batch Execution code. Install the necessary Nuget Packages and also update the following parameters. – BaseURL will be the same. – Storage Account Name, Storage Account Key and Storage Container Name will be parameters that can be found in your Azure Blob Storage which was created. – Api Key can be found in the Web Experiment Page in Azure Machine Learning Studio. – The input path is the path where you have saved your input csvfile for Batch Execution. Your Input csv file should have all the features which you have used to train your experiment After you run your Console application a new output1results.csv file should get generated in your Blob Container. The output results should include the labels which your experiment generates in it’s output. It should include the Scored Labels and Scored Probabilities labels as well. Now you can get your data using Azure Blob Storage as your source in Power BI and use the columns in the output1result.csv file to generate your ML Predicted Reports. The Report can look something like this. I hope this blog helps you to combine Azure Machine Learning Studio and Power BI to create a powerful predictive solution.