Accelerate and automate every step of building and managing production-ready AI/ML apps
Our Insight Designer simplifies the journey of everyone involved in the AI lifecycle by accelerating model building and training, validation, deployment, monitoring, and governance—shortening time to market, improving ML operations, AI accuracy, and trustability.
Enable data scientists to innovate with AI/ML via support for a variety of IDEs, ML frameworks, toolkits, and programming languages.
Dynamically allocate compute resources based on requirements, ensure resource accountability across the lifecycle, and lower costs.
Standardize and automate ML engineering, MLOps, and governance to support repeatability and auditability.
Provide persona-specific access to relevant capabilities and workflows while leveraging a single, integrated AI platform.
Leverage the best AI frameworks and templates to prepare, build, train, and deploy high-quality ML models.
Develop statistical, AI/ML, and generative AI data models, and productionize them by performing feature engineering, training, retraining, and inferencing through a repeatable development process
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Access model performance, auditability, cost- effectiveness, and other dimensions and understand model performance in production along with drift to identify and resolve issues before deploying them to production
Learn moreGather insights and identify features using natural language prompts from the Insight Designer. Automatically generate code for building and registering ML models using any of your datasets.
Collaborate seamlessly with cross-functional teams via a customizable, self-serve workbench that makes available the finest AI frameworks, simplifying and accelerating the task of innovating together.
Accelerate model deployment and optimize AI models for superior performance, lower latency, and more efficient memory usage, reducing the complexities of infrastructure scaling and multi-environment deployments.
Seamlessly monitor model performance, eliminate model drift and bias, and perform maintenance and debugging on all their AI models across a single pane.
Break down governance silos and implement a comprehensive model and data governance framework across the enterprise to have a uniform single version of the truth across various models.
See how your peers operationalized ML at scale to create the value they want consistently
Operationalizing ML at scale helped a global banking giant improve the efficiency of their ML-powered risk analytics.
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Fosfor accurately forecasts footfall for a leading travel experience player, helping the company deliver a strong customer experience, improving customer satisfaction multi-fold.
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An integrated risk management framework is fundamental to any bank to be able to run more efficiently and generate profits consistently and sustainably.
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