Documentation

Deploying ML Models via MLOps UI

Login to MLOps web interface and follow the steps to deploy a model to MLOps:

From the left menu panel, click “Deploy” option

Fill out the form (as shown in Figure 4.1 below).

Model Name: Give a meaningful name of the model

Select Model Category: Select from the dropdown the type of model you are deploying.

Purpose (optional): Give a short description and reason why you are deploying this model

Business Description (optional): Give a business description about the usage of the model

Author: by default, the logged in user’s id is pre-filled. However, if the author of the model is someone else in the organization, provide that user’s id.

Select Cluster Id: Select the cluster where the model needs to be deployed. Depending on your organization’s deployment, the option may vary. Selecting “default_cluster” means, the model will be deployed within the same machine where the MLOps is deployed.

Choose File: Browse to the location in your machine where the model file is located and select the model to upload. The supported file formats are:

.xml for PMML compliant models

.onnx for ONNX compliant models

.zip for TensorFlow models (make sure all the model artifacts are included and packages as a zip file).

Click “Deploy” button.

If everything goes well, you should see the metadata of the model with the “success” confirmation.

Figure 4.1: Screen showing the model deployment page

Table of Contents

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).