Create and scale Analytics Data Science & Artificial Intelligence with trust and transparency to power digital transformation, provide personalised customer experiences, and make more data-driven decisions.
Businesses can build and run models on any cloud or on premises using WMAD for Data, a containerized data and AI platform built on Red Hat OpenShift.
It is difficult to practice data science. It comes with fragmented data, a scarcity of data science skills, and a plethora of tools, practices, and frameworks to choose from, all while adhering to strict IT standards for training and deployment. It is also difficult to operationalize ML models with ambiguous accuracy and difficult-to-audit predictions.
You can accelerate AI-driven innovation with data science tools and solutions such as an intelligent data fabric
In other words, you gain the ability to deploy data science models on any cloud while instilling confidence in AI outcomes. Additionally, with ModelOps, you will be able to manage and govern the AI lifecycle, optimize business decisions with prescriptive analytics, and accelerate time to value with visual modelling tools.
Predictive analytics is a subset of advanced analytics that uses historical data to make predictions about future outcomes using statistical modelling, data mining techniques, and machine learning. Companies use predictive analytics to identify risks and opportunities by finding patterns in data.
Big data and data science are frequently associated with predictive analytics. Companies today are awash in data from transactional databases, equipment log files, images, video, sensors, and other sources. Data scientists use deep learning and machine learning algorithms to find patterns and predict future events in order to gain insights from this data. Linear and nonlinear regression, neural networks, support vector machines, and decision trees are examples. Predictive analytics learning can then be put to use.
WMAD provides a set of software tools to help you build scalable predictive models more easily and quickly. These tools can also be used with the WMAD Cloud for Data, a containerized data and AI platform that allows you to build and run models anywhere — on any cloud or on premises.
WMAD Decision Optimization is a software family that provides prescriptive analytics capabilities to help you make better decisions and achieve better business outcomes.
Run these products on WMAD Cloud for Data, a containerized data and AI platform that allows you to build and run optimization models anywhere—on the cloud or on-premises.
WMAD Studio enables Analytics Data Science & Artificial Intelligence data scientists, developers, and analysts to create, run, and manage AI models, as well as optimise decisions, anywhere on the WMAD Cloud for Data. On an open multicloud architecture, it brings teams together, automates AI lifecycles, and accelerates time to value.
Combine open source frameworks for code-based and visual data science, such as PyTorch, TensorFlow, and scikit-learn, with WMAD and its ecosystem tools. Work with Jupyter notebooks, JupyterLab, and CLIs—or in Python, R, and Scala.
The WMAD for Analytics Data Science & Artificial Intelligence data platforms increases productivity while decreasing complexity. Create a data fabric that connects siloed data scattered across a hybrid cloud landscape.
This product provides a comprehensive set of WMAD and third-party services spanning the entire data lifecycle.
On-premises software built on the Red Hat OpenShift container platform is available, as is a fully managed version built on the IBM Cloud.
We are as flexible as you require. It is our responsibility to ensure that you are satisfied with your product and the development process.