Increase efficiency your business processes with ML-models. Implement high-precision ML-models faster using all available data, easy add new features, and invoke them in real time using the MLOps Platform
Challenges
Serving large number of business critical ML-models
1
Creation high-precision ML-models trained on scalable infrastructure and using modern ML libraries
2
Reduce cost and complexity feature engineering
3
Increase the efficiency of the team of data scientists and data engineers
4
Standardize the processes of the ML lifecycle
5
Increase the efficiency of using hardware resources for building, training and serving models
6
Challenges
1
Serving large number of business critical ML-models
2
Creation high-precision ML-models trained on scalable infrastructure and using modern ML libraries
3
Reduce cost and complexity feature engineering
4
Increase the efficiency of the team of data scientists and data engineers
5
Standardize the processes of the ML lifecycle
6
Increase the efficiency of using hardware resources for building, training and serving models
Solution
Implement centralized MLOps platform based on best of breed open source MLOps components and customized CI/CD processes.
Main features
Cloud (AWS, Azure) and On-Premises (kubernetes) deployment
Support wide set of ML libraries and frameworks: sklearn, tensorflow, xgboost, catboost, etc.
Integration with multiple datasources: Postgres, Hive, Snowflake, Trino, Synapse, etc.
Advanced tools for feature engineering
Manage model features in feature store
Dynamic creating notebooks by template for your team