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
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.
100% compliant with requirements for security-first environments
Reliable AI starts with traceable and metadata-rich data infrastructure
GitOps for ML
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
for AI Solutions
Eliminates major infrastructure challenges and speeds AI adoption
Helps ML engineers to detect and explain ML model issues and data drifts in production
Add MLOps automation
for your ML workflow
Scale ML use cases
across the enterprise
readiness & compliance
of ML models
Establish reproducible and scalable ML process across your organization
Machine Learning Infrastructure Solution
Rearchitect your organization’s infrastructure in 5-6 months to achieve robust performance.
- Feature Store
- Data Versioning & Lineage
Reproducible Machine Learning pipelines
Auditable FDA / SOC2 compliant machine learning infrastructure
- Model Versioning
- Model Monitoring
- Concept Drift Detection
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.
organizations to become more agile
and deliver ML innovation at scale.
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.
ML-powered Data & Analytics Platform
InMarket applies machine learning to glean insights from customers’ location data to drive precise marketing campaigns