Orchestrate data and analytics operations
Did you know that ML code development is only 5-10% of the model development lifecycle? There’s a good chance that your data scientists are spending the rest of their valuable time performing repetitive tasks. Plus, nearly 90% of all the developed models never make it to production due to inadequate data quality. The lack of reliable processes to monitor and govern models also cause major hindrance in the post-production stage. Tiger Analytics addresses existing and emerging MLOps challenges holistically by taking a cutting-edge data foundry approach with XOps. Our Data Engineering expertise gives you access to ML frameworks and code templates, based on global best practices, for creating top-notch data science products. We offer competitive differentiators such as reduced time-to-deployment, reduced data engineering risks, and improved operational performance.
Faster time to model development and operationalization
Scale your enterprise’s ability to develop and operationalize models with reduced technical debt.
Create a quality data foundation
Address data quality and acquisition issues before they prevent business growth.
Extend reusability and discoverability for ML at scale
Create multiple reusable and discoverable features to maximize XOps.
Effectively govern and manage model risk
Effectively manage data and model drift for operationalized models.
Our Unique Approach
Tiger Analytics is backed by years of having understood the importance of DataOps and data management in the MLOps lifecycle. Our best-in-class data engineering team helps you follow a holistic data foundry approach to drive collaboration and deliver true value. We help you get a head-start by organizing your technology, data and analytics, and business teams.
Consulting and Advisory
- MLOPs and governance strategy
- Data foundry strategy for MLOPs
- ML technology strategy and roadmap
- ML technology services and MLaaS platform selection
- Functional data foundry
- Model monitoring and governance implementing
- Model operationalization with real-time and batch model inferencing
- Feature stores and model repository implementing
- Best practice-driven ML framework and code templates
- Best practices-driven image and video processing data pipelines and analytics
- Time series data processing and forecasting
- Multi-cloud reference architecture implementing for ML platform services
- Model monitoring and governance support services