Altair® SmartWorks™ Analytics: Overview

Learn about the different ways to deploy machine learning models using Altair® SmartWorks™ Analytics.

Users can begin making predictions after their machine learning models are registered and deployed in the SmartWorks Analytics MLOps application. This video explains how to send requests for predictions to a model using a Python script. Empower everyone to make augmented, data-driven decisions. See how with SmartWorks Analytics.

Knowledge Studio makes it easy to export models to Altair’s SmartWorks Analytics for model management and model deployment. This video was produced using Knowledge Studio 2021.3 and SmartWorks Analytics 2021.3.

Leveraging material data, integrated simulation, data analytics, and AI, Altair's solutions are driving a shift to circular design, creating regenerative, closed-loop systems that use a product, its materials, and its components at their highest value for as long as possible, while minimizing the creation of waste.

Circular product lifecycle management benefits industry and the earth by considering what the product you're developing will become in the future.

Refining their processes to capitalize on resource efficiency, manufacturers today are relying on smart, connected technology to make their products more sustainable.

Altair makes sustainably-driven product design possible by weaving a digital thread through its development, testing, and operation.

IoT-enabled infrastructure in factories produce mountains of data that managers can use to optimize plant operations. IoT technology and the sensors that generate most of this data are key building blocks for executing on the industry 4.0 vision of a smart factory. The proper analysis and visualization of this data, coupled with machine learning deployed in the cloud or at the edge, can help companies achieve higher degrees of productivity, flexibility, and innovation and compete more effectively. The need to stitch together the tools required to support the entire workflow — from data prep to machine learning and MLOps (machine learning model operationalization management) — requires broad specialized knowledge and expertise, as well as significant upfront investment. Coupled with the need for data to pass between systems and tools, these factors introduce substantial amounts of risk into the implementation process. We built Altair® SmartWorks™ Analytics specifically to address these challenges and enable data-driven decision making and AI-augmented automation. In this video, we demonstrate how to build an analytics pipeline, including deployment of MLOps models, in a smart factory environment.

Learn how to manage and securely operationalize machine learning models at scale.

Explore how the AutoML nodes allow you to train classifiers by utilizing built-in feature transformations and optional automated hyperparameter tuning with our out-of-the box, and fully customizable gridsearch. AutoML models created using the Python-based engines of SmartWorks Analytics can be registered, versioned, and served through integrations with MLflow and Seldon Core. Empower everyone to make augmented, data-driven decisions. See how with SmartWorks Analytics.