DataOps, a helper of AI innovation In the development of AI, especially a deep-learning technology, algorithm training is needed through data.
Such technology helps in constructing various and vast data into a learning dataset
and the algorithm to learn it and then evaluates the performance of the model.
Afterward, the model repeatedly goes through the processes of construction of datasets
to the evaluation of model performance to continuously improve its performance.
A total of 80% of the effort of AI development is exerte in
preparing that dataset to spend a lot of costs, time, and effort.
The quality of the constructed dataset affects the quality and performance of an AI model,
so data preparation and model development affect each other through continuous circular processes.

INFINIQ DataStudio

INFINIQ DataStudio is a data service framework
for an AI development environment.

infiniq Data Studio

The development and delivery of an AI model such as ML/DL utilize vast data and
various tools based on governance continuously and repetitively, where effective collaboration
and efficient data processing are necessary between different organizations.
A DataOps framework of INFINIQ, DataStudio is the best-fit composable framework
that helps manage AI data and algorithm lifecycle flexibly.
In gathering insights from soaring data, continuously preparing and analyzing data,
and repeating learning and evaluating an AI model, DataStudio effectively helps
in developing a high-quality model fast.

Features of INFINIQ DataStudio

Intensifying data value, a key element of AI-tech competitiveness,
and integrating and distributing data continuously is possible.

  • Flexibility of
  • Sustainable
    of Datasets
  • Life cycle monitoring
    over planning, production,
    and delivery of data
    and an AI algorithm
  • Intensified collaboration
    between participating
    over a lifecycle

Composition of INFINIQ DataStudio


    Collection Real-time, online/off-line, and infinite data collection
    Informatization Production of AI data through cleansing, processing, and examination of source data
    Continuous Integration Continuous integration to produce artifacts

    Storing Infinite storage and stable utilization of data
    AI Various AI models provided and optimal AI models produced
    Analysis and Visualization Data informatized through analysis and visualization

    Governance management Governance-based data process management
    Monitoring and collaboration Data lifecycle monitoring and enable collaborative environment

    Continuous Delivery Highly reliable AI production continuously distributed through pre-verification
    Orchestration Stable operation of DataStudio through orchestrating software and hardware

DataOps Platform

Managing the life cycles of AI data and algorithms flexibly is possible from the definition
of source data to data-based analysis and collaboration from the results of model training.

  • Collection and Assetization of Source Data Logging, transformation, and transmission of data,
    sensor fusion data connector, distributed storage,
    AI-based deidentification
  • Data Cleansing and Data Labeling (Informatization) AI-based technologies of automated sorting and
    recognition, data virtualization, Data profile and metadata,
    data quality control Data labeling, human-in-the-loop
  • Analysis and Visualization Data status, knowledge catalog,
    analytics/reporting of data and model
  • Monitoring and Collaboration Current source data, current data cleansing/processing,
    current data distribution, collaboration between data
    models, production, and customers

Expert Service

Platforms and micro-tools combined with the AI-model workflows of a customer are arranged,
and expert services to construct and manage governance are provided

  • Integrated Orchestration of Platforms and Services DataStudio, best-fit to ML developing processes
    of a customer, connects the micro-services of
    its framework to itself to maximize the stability
    and efficiency of their integrated operation.
  • Construction, Selection, and Management
    of Policy-Based Governance
    The whole data workflow is integrated by the policy-based
    governance to gain data stability, security, personal data
    protection, accuracy, availability, usability, and consistency
    of management.
  • Continuous Integration of Source Data,
    Virtualized Data, and Distributed Output
    Data is continuously integrated based on the governance
    in each step of the data life cycle. Then, the high-quality
    data are delivered through the continuous
    delivery platform that manages a change in artifacts.