• The Analytics Life Cycle

    DataOps • Artificial Intelligence • ModelOps

    The Analytics Life Cycle Graphic

    Organizations have been pouring money into analytics initiatives for years, but too few are seeing a payoff because models aren't making it into production. To enable data-driven decisions at scale, the analytics life cycle must be highly operational and seamless. By connecting all aspects of the analytics life cycle – DataOps, artificial intelligence and ModelOps – SAS helps you turn your critical questions into trusted decisions and gain real value from your analytics investments.

  • The Analytics Life Cycle


    Borrowing from agile software development practices, DataOps provides an agile approach to data access, quality, preparation and governance. It enables greater reliability, adaptability, speed and collaboration in your efforts to operationalize data and analytic workflows.​


    Access data, regardless of size or complexity.


    Transform raw data, including AI-powered suggestions.


    View important relationships in data and share insights.


    Build trust in data, understand lineage and gain transparency.

  • The Analytics Life Cycle

    Artificial Intelligence

    Data scientists use a combination of techniques to understand the data and build predictive models. They use statistics, machine learning, deep learning, natural language processing, computer vision, forecasting, optimization and other techniques to answer real-world questions.


    Build models with multiple AI techniques to solve real-world problems​.


    Automate manual tasks for feature engineering and model tuning​.


    Enable users with different skill sets to collaborate on solving analytic problems.​


    Work smarter using SAS Viya and open source analytics.

  • The Analytics Life Cycle


    ModelOps focuses on getting AI models through validation, testing and deployment phases as quickly as possible while ensuring quality results. It also focuses on ongoing model monitoring, retraining and governance to ensure peak performance and transparent decisions.​


    Ensure models will perform as expected in the real world​.


    Embed models into operational systems and monitor them​.


    Ensure decisions are safe and transparent over the life of the model.


    Integrate business rules to ensure up-to-real-time results.

    Connect with SAS and see what we can do for you.