Machine Learning Infrastructure

01. AI Adoption Acceleration

Implement various AI & ML use cases in a live production environment quickly and efficiently

Implement AI-ready infrastructure as a foundation for a variety of AI & ML use cases

Gain visibility into specific AI use cases through a well-architected, auditable, and transparent infrastructure

Cut down on AI adoption time by shortcutting to a ready-to-use infrastructure solution

02. AI & ML Scalability

Scale use cases of AI & ML across your enterprise in a monitored and auditable, AI-ready environment

Scale effortlessly while changing demands to ML model inference in an automated, repeatable, and predictable fashion

Use finely tuned machine learning process and technology to implement specific use cases of AI & ML across your enterprise

Take advantage of a reference architecture for immutable and reusable machine learning pipelines

03. ML Model Readiness

Operationalize and handle machine learning models in production at the enterprise scale

Achieve strong levels of reliability of your AI solutions

Take advantage of built-in testing and monitoring to ensure the production readiness of your AI/ML initiatives

Achieve a considerable reduction in technical debt of your AI/ML system on an enterprise scale

04. ML Model Compliance

Financial Services, Government, Healthcare, and other security-first organizations require AI solutions to be compliant with industry requirements

Run large scale experiments on production data sets without providing engineers with direct access to production data

Enforce a strict model control environment, with ongoing monitoring and governance processes on board

Achieve next-level transparency — fully explain, document, and validate how your ML model(s) was built and is being used

Detect and track subtle changes in model operating conditions to explore how the changes have impacted the fairness and performance of your ML models

05. ML Experimentation & Research

Increase the productivity of your data science teams through reproducibility of ML processes in your organization

Start using a versioned, scalable, and metadata-aware Feature Store to streamline reproducible ML experiments and production deployments

Run and track hundreds of experiments searching different data splits and preprocessing pipelines, and searching for the best model architecture

Grow your data science team without compromising the productivity of engineers

Take advantage of an ML experimentation environment designed for reproducibility

Augment your ML training through advanced instrumentation and built-in re-training infrastructure

06. ML Process Visibility

Gain visibility into machine learning processes to ensure their transparency and auditability

Improve your organization’s visibility into ML processes through advanced instrumentation and monitoring

Track actions with different versions of ML models and monitor their performance using highly customizable machine learning infrastructure

Bring trust into your organization’s machine learning processes with a ready-to-go solution built for auditability and operational transparency