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3.5 ML Prediction

Followings are the steps to predict the outcomes from a trained model:

Click “ML Prediction” on the left menu panel and click “Create New Prediction” from the top menu bar.

Fill out the form. Make sure to provide the same list of features that were used in the model training.

Select “No” for recommendation and leave all other fields to default for most cases. Select “yes” if this prediction is for recommendation engine.

Save the form.

Expand “Transformer” from the left menu and click on the test data set related transformer.

Expand “ML Model” and click on the ANN model just trained.

Connect the two rectangles as shown in Figure 3.5 below.

Figure 3.5: Model prediction screen

Submit to save the prediction configuration.

Select the prediction config and click “Run” located at the top menu bar to execute the prediction job.

After the prediction is completed, use the data exploration to assess the accuracy of the model. For example, the following interactive query in Figure 3.6 shows an ad hoc analysis done on the test prediction. This SQL statement yields the confusion matrix.

Figure 3.6: A sample interactive query to analyze the prediction test result

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Shamshad (Sam) Ansari is an author, inventor, and thought leader in the fields of computer vision, machine learning, artificial intelligence, and cognitive science. He has extensive experience in high scale, distributed, and parallel computing. Sam currently serves as an Adjunct Professor at George Mason University, teaching graduate- level programs within the Data Analytics Engineering department of the Volgenau School of Engineering. His areas of instruction encompass machine learning, natural language processing, and computer vision, where he imparts his knowledge and expertise to aspiring professionals.

Having authored multiple publications on topics such as machine learning, RFID, and high-scale enterprise computing, Sam’s contributions extend beyond academia. Sam’s book, titled “Building Computer Vision Applications Using Artificial Neural Networks,” has garnered acclaim with two published editions. It received recognition as one of the top 10 books ever written on this subject by bookauthority.org, highlighting the significant impact and quality of Sam’s contributions to the field. He holds four US patents related to healthcare AI, showcasing his innovative mindset and practical application of technology.

Throughout his extensive 20+ years of experience in enterprise software development, Sam has been involved with several tech startups and early-stage companies. He has played pivotal roles in building and expanding tech teams from the ground up, contributing to their eventual acquisition by larger organizations. At the beginning of his career, he worked with esteemed institutions such as the US Department of Defense (DOD) and IBM, honing his skills and knowledge in the industry.

Currently, Sam serves as the President and CEO of Accure, Inc., an AI company that he founded. He is the creator, architect, and a significant contributor to Momentum AI, a no-code platform that encompasses data engineering, machine learning, AI, MLOps, data warehousing, and business intelligence. Throughout his career, Sam has made notable contributions in various domains including healthcare, retail, supply chain, banking and finance, and manufacturing. Demonstrating his leadership skills, he has successfully managed teams of software engineers, data scientists, and DevSecOps professionals, leading them to deliver exceptional results. Sam earned his bachelor’s degree in engineering from Birsa Institute of Technology (BIT) Sindri and subsequently a Master’s degree from the prestigious Indian Institute of Information Technology and Management Kerala (IIITM-K).