MLOps Platform

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
  • Tracking ML experiments with ML repository
  • Deploy complex inference graphs: models ensembles, A/B testing, multi armed bandit
  • Track and maintain production models in real time. Configure dashboards using set of available monitors and triggers
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