As Artificial Intelligence (AI) and Machine Learning (ML) initiatives mature, the biggest challenge faced by technical professionals is operationalizing ML for effective management and delivery of models. EndurOps defines a unique operational framework and provides a set of tools that identify key supporting components like model management systems that are critical toward building and delivering effective AI-based systems. Transitioning ML models to real-world production applications are becoming more critical as AI and ML initiatives mature, and the emphasis has shifted from development toward deployment and continuous delivery. Many organizations are struggling to systematically productionize ML and face significant scalability and maintenance challenges. Existing IT DevOps tools are ineffective because ML projects are inherently different from traditional IT projects in that they are significantly more heuristic and experimental, requiring skills spanning multiple domains — statistical analysis, data analysis, platform engineering, and application development.

While MLOps also started as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management. MLOps applies to the entire lifecycle – from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics.

MLOps is a practice for collaboration and communication between data scientists and operations professionals to help manage production ML (or deep learning) lifecycle. Similar to the DevOps or DataOps approaches, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. In order to finally realize the value of machine learning, machine learning models must run in production and support efforts to make better decisions or improve efficiency in business applications. 

An ML system is a software system, so similar to DevOps practices apply to help guarantee that you can reliably build and operate ML systems at scale. EndurOps systems differ from other software systems in the following ways:

  • Simplified model deployment. Data scientists use a variety of modeling languages, frameworks, and tools. With EndurOps, IT operations teams can quickly deploy models from a variety of languages and frameworks in production environments.
  • Monitoring for machine learning. Tools for monitoring software do not work for machine learning. EndurOps provides monitoring designed for machine learning. Key capabilities include data drift detection for important features and model-specific metrics.
  • Production life cycle management. The initial model deployment is the beginning of a long life cycle of updates to keep a machine learning model running. EndurOps provides a means to test and update models in production without interrupting service to business applications.
  • Production model governance. Machine learning models used in production applications will need to be tightly controlled to prevent unwanted changes and to comply with regulations. EndurOps provides access control, traceability, and audit trails to minimize risk and ensure regulatory compliance.