DataOps and MLOps: An extension of the DevOps framework for Data Analytics
Updated: Sep 1
As digitization becomes increasingly important for businesses to compete in today’s landscape, data and analytics is front and center. Providing organizations access to high-quality, reliable data sources for building visualizations and generating machine learning (ML) algorithms is essential to leverage analytics for strategic decision-making. DataOps and MLOps are emerging frameworks to help realize this goal and minimize the bottlenecks that can occur during implementation.
DevOps: Automated deployment of software applications
It’s hard to talk about DataOps or MLOps without mentioning DevOps. DevOps started over a decade ago and has become a standard in software development. It accelerates and streamlines the software development life cycle significantly by merging the development and operations teams. This results in increased collaboration, continuous integration and delivery (CI/CD), and automation.
Rooted in agile methodology, DevOps practices include developing, testing, and deploying software in smaller iterations using automated systems. As changes in the code are made, issues are identified immediately resulting in shorter development cycles and the ability to maintain high quality software. Continuous monitoring of these automated processes provides a view of the system health at all times.
DevOps teams typically include cloud architects, software engineers, and quality engineers. DataOps: Automated deployment of data analytics
DataOps extends the DevOps framework and statistical process control (SPC) to data analytics. It promotes the collaboration between DevOps teams and data teams to manage and maintain the quality of data pipelines and reduce cycle time of data insights to end-users. It encompasses the entire data analytics lifecycle – data ingestion, data transformation, data modeling, and data visualization and reporting – using CI/CD agile practices to accelerate time to value.
Because data changes over time, what was a valid business rule previously, may produce errors in the future. To maintain end-user confidence in the data, SPC, a key concept from Lean manufacturing, is used to monitor and control the data pipeline operations. It measures and monitors the data flowing through the pipeline to ensure its operational characteristics fall within acceptable ranges. SPC verifies the inputs and outputs as data progresses through each step of the pipeline. As a result, data anomalies are quickly identified and addressed before moving to the next stage.
DataOps teams include data engineers, data scientists, data analyst, and DevOps team representative(s).
MLOps: Automated deployment of machine learning algorithms
MLOps combines data scientists with operations to facilitate the automated deployment, management, and monitoring of machine learning models into large-scale production environments. Although machine learning (ML) systems are similar to software systems, DevOps isn’t directly applied to ML because it is more than code; ML is code and data.
MLOps takes the DevOps practices of CI/CD and applies it to machine learning with the following differences:
Continuous Integration (CI) not only includes testing and validating code, but also includes testing and validating data to ensure data quality.
Continuous Delivery (CD) may not refer to deploying a single software package or service. Deployment of a ML system could include an entire ML training pipeline that involves data extraction, data processing, feature engineering, modeling training, model registry, and model deployment.
Continuous Training (CT), which is specific to MLOps, automatically retrains and redeploys models whose performance has degraded over time.
ML teams include data engineers, data scientists, data analysts, and DevOps team representative(s).
Ops work together to form a Platform Ops for AI
DataOps and MLOps, together with DevOps, provides organizations the optimal framework to maximize the use of data with analytics. The framework supports the reproducibility, traceability, integrity, and integrability of analytics and ML assets. Gartner refers to this overarching framework as Platform Ops for AI. As you can see in the image below, the orchestration of data flows between the Ops activities to create a more seamless process for collection, cleansing, model building, and deployment within a secure environment.
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DataOps, MLOps, and DevOps techniques to optimize your data and analytics deployment thus efficiently giving your organization the edge to succeed.