DataOps, a helper of AI innovation The development of artificial intelligence, especially deep learning technology, requires algorithm training through data.
Diverse and vast amounts of data are collected to build a dataset, which is used to train the algorithm.
To improve the model's performance, building and evaluating dataset is repeated. Preparing such datasets requires 80% of the effort in AI development, and involves significant costs and time. The quality of the dataset has a significant impact on the quality and performance of the AI model.
Therefore, data preparation and model development influence each other through a continuous iterative process.

INFINIQ DataStudio

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

infiniq Data Studio

Developing and deploying ML/DL and other AI models is an iterative and continuous
process based on governance, utilizing vast amounts of data and various tools,
requiring effective collaboration and efficient data processing among multiple organizations. INFINIQ DataOps Framework DataStudio is the optimal composable framework for the total
experience of AI data, supporting flexible management of the AI data algorithm life cycle. It provides insights from massive data, continuously preparing and analyzing data,
and iterating through AI model training and evaluation, enabling rapid development
of high-quality models.

Features of INFINIQ DataStudio

Expands the value of data, which is a key factor in the competitiveness
of AI technology, and enables continuous integration and deployment.

  • Flexibility of
    Governance-Based
    Frameworks
  • Sustainable
    Integration/Distribution
    of Datasets
  • Life cycle monitoring
    over planning, production,
    and delivery of data
    and an AI algorithm
  • Intensified collaboration
    between participating
    organizations
    over a lifecycle

Composition of INFINIQ DataStudio

  • PRODUCE DATA

    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

  • ENRICH DATA

    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

  • COLLABORATE DATA

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

  • OPERATE DATA

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

DataOps Platform

Flexibly manage the life cycle of AI data and algorithms from defining raw data to building datasets,
training models, analyzing data based on the results of model training, and collaborating.

  • 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

Professional services for deploying a platform and micro tools catered to a customer's AI model workflow.

  • 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.