Machine Learning Infrastructure
Companies looking to adopt AI have to aggressively hire data scientists and ML engineers capable of using a wide variety of ML platforms, tools, and cloud products. They have to invest a massive amount of resources to come up with tangible solutions to achieve their business goals.
The Provectus Infrastructure for Machine Learning solution allows organizations to avoid these issues, offering a readily available ML infrastructure platform that is built using the best practices and processes of AI adoption.
The solution ensures ML experimentation productivity & reproducibility, transparency & auditability, and better collaboration, thereby significantly reducing the number of AI/ML’s infrastructure challenges.
ML Experimentation Productivity
Transparency & Auditability
High Customization vs. Ready to Go? Get Both
- Machine Learning experimentation environment designed for reproducibility
- Versioned, scalable and metadata-aware Feature Store
- Reference architecture for immutable and reusable machine learning pipelines
- Machine Learning training augmentation with advanced instrumentation and monitoring
- Integration with cloud services, such as AWS SageMaker
- Integration with data enrichment platforms, such as Figure Eight and AWS Ground Truth
- Production deployment and re-training infrastructure
customizable for a
- Machine Learning Pipelines architecture, implementation, and optimization
- Migration of legacy cumbersome machine learning processes to a newly selected platform
- Training and coaching for client’s engineers and managers
Foundation for AI Solutions
Eliminate major infrastructure challenges and adopt AI faster
Open, Certified Architecture
The source code is fully available and can be easily customized and manipulated
Cloud Native & Vendor Agnostic
The architecture is certified to be used with different cloud vendors
Consulting & Customization
Integration and delivery, as well as training for the team, are included
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