Machine Learning Infrastructure
A cloud-native solution that improves the velocity of data science teams, adding visibility and auditability to the ML process, to rapidly launch AI projects.
Contact usAccelerating AI Adoption
Start implementing AI/ML use cases in an auditable and trusted, AI-ready environment enabled by Provectus ML Infrastructure. Forget about black box solutions that can cause headaches down the road. Instead, rely on a well-monitored process to annotate data, run experiments, test assumptions, and build, train, enhance and maintain ML models.
100% available, including consulting services for the assessment of business use cases, architecture customization, migration, and enterprise support.
Key Capabilities
100% compliant with requirements for security-first environments
Reliable AI starts with traceable and metadata-rich data infrastructure
Hook your ML training and retraining pipelines into CI/CD flow
Allows ML Engineers to spend 90% of time on modeling, not on feature engineering
Certified architecture ready to be used with different cloud vendors
Fully available source code that can be easily customized and manipulated
Eliminates major infrastructure challenges and speeds AI adoption
Helps ML engineers to detect and explain ML model issues and data drifts in production
Use Cases
- 01AI 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
- 02AI & 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
- 03ML 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
- 04ML 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
- 05ML 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
- 06ML 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
ML Infrastructure & MLOps
MLOps are best practices for collaboration between Data Scientists, Data & ML Engineers and IT Ops to help organizations manage the ML production lifecycle and successfully enable AI/ML projects on top of an ML Infrastructure.
MLOps unifies deployment, monitoring, management, and governance of AI/ML solutions in production, helping ML teams build and deploy models faster and at scale, and Business Units get the insights they need more quickly.
Learn how MLOps enables organizations to become more agile and deliver ML innovation at scale.
Learn more
Cracking the Box: Interpreting Black-Box Machine Learning Models
Explore major methods of ML model interpretability and learn how to build a resilient ML infrastructure for AI projects.
Download whitepaper
Case Studies
VTS utililizes its new ML infrastructure to accelerate time-to-market for AI/ML apps, helping real estate pros in their work
FireworkTV overhauls its video recommendation system, rebuilding its infrastructure to accommodate AI & ML