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Data Integration

Business processes for DataOps

The Comfiz Platform gives organizations the capacity to consolidate their business data and partners data into a coherent data environment dedicated to data enrichment for data science models design.

The Comfiz platform data integration features give data scientists, data architects and data analysts the capacity to:

  • Deploy a logical data model designed for data science and independent of the underlying legacy systems
  • Consolidate and enrich data from heterogeneous sources
  • Apply best data preprocessing rules to increase the quality of data for machine learning
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Inside this section: [Customer Information Management] [Data Sources Management] [Master Data Management for Data Science]

Data Enrichment

Feature Engineering Management


Feature engineering is the craft of transforming raw data into a set of features whose properties are well-adapted to what machine learning algorithms can handle.

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Inside this section: [Data Quality Management] [Feature Engineering]

Data Pipelines

Data Science Pipelines Management


The pipeline jungle concept has been crafted by a team of Google researchers in a paper aiming at evaluating the costs of poorly designed ML system architecture and business processes.

Pipeline jungles can only be avoided by thinking holistically about data collection and feature extraction. This engineering effort can dramatically reduce ongoing costs and speed further innovation.

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Inside this section: [Data Acquisition Pipelines] [Feature Extraction Pipelines] [Scoring Pipelines]

Models Governance

Data Science models governance

The process of developing data science models is complex and can result in quality problems further down the line if it is not well documented and industrialized.

The documentation should be self-contained and extensive at all level of machine learning workflows.

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Inside this section: [Data Science Projects Management] [Data science teams Management] [Ethics and transparency for Data Science]