dotData Ops democratizes enterprise AI and ML, empowering BI & Analytics professionals with streamlined data, features, and ML pipeline operationalization.
SAN MATEO, Calif., August 29, 2023 – dotData, a pioneer and leader of enterprise feature discovery platforms, has unveiled dotData Ops, a next-generation, no-code MLOps platform. It empowers BI and analytics professionals or ML engineers by providing an intuitive, self-service environment for efficiently deploying and operationalizing data, feature, and prediction pipelines.
“dotData Ops was designed to bridge the gap between existing MLOps solutions and the real-world needs of ML Engineers or BI & analytics practitioners. With dotData Ops, enterprise organizations can effortlessly operationalize advanced predictions and unearth deep business insights, empowered by our AI-powered features,” said Ryohei Fujimaki, Ph.D., founder and CEO of dotData.
dotData Ops distinguishes itself from conventional MLOps solutions by managing and streamlining pipelines for data and feature transformations and ML scoring models. This integration introduces the critical innovation that sets dotData Ops apart. The primary features of dotData Ops are summarized as follows:
Rapid Business Validation With Self-Service Feature & Model Deployments
Navigating the hurdles between Proof of Concept (PoC) and the operationalization of models is a persistent challenge in ML and predictive analytics projects. Building a successful PoC model is merely the beginning; transforming it into a business application demands significant effort in system integration, data migration, and alterations to business workflows – tasks that go well beyond just deploying ML models.
dotData Ops is poised to tackle the most demanding aspects by enabling the deployment of data and feature pipelines, often the most time-consuming and costly components. Deployment of data and feature pipelines, in tandem with ML models, is achieved effortlessly with just a few clicks and without any IT intervention. It provides an agile, self-service platform that empowers analytics teams to expediently deploy minimum viable pipelines and promptly test and validate business values in real-world settings, transcending the limitations of “desktop PoC.” Rapid “field” validation expedites investment decisions and garners business leaders’ confidence.
Business Impact Monitoring of Your Features and Models
While traditional MLOps platforms often focus on monitoring model health, they sometimes neglect the real-world impact on business performance. Understanding the actual effect of models and features in practical scenarios demands more than just ML-related metrics; it necessitates closely monitoring business metrics.
dotData Ops excels by enabling BI and Analytics teams or ML Engineers to couple and track business KPIs with model performance, health metrics, and feature metrics. This integration creates a unified view of model and business performance and facilitates the automated detection of issues in models and features that could influence business results. With dotData Ops, teams can monitor both model and feature quality alongside business metrics on a single dashboard, gaining a comprehensive understanding of how changes in model performance directly impact business fundamentals.
Source Data Diagnosis for Enhanced Insight into Feature Drift
Traditional MLOps platforms typically track drifts in ML model accuracy and feature distribution. dotData Ops improves on this by operating and managing the ML model and the entire pipeline that generated features, all the way to source data. dotData’s end-to-end capabilities can diagnose the fundamental causes of accuracy and feature drift by tracing the source data from which features were derived. dotData empowers teams to detect drift caused by data errors or changes in the source data and to swiftly take prescriptive actions such as data correction or rectification of models and features.
Feature Re-Engineering to Combat Data Drift Beyond Model Refitting
Traditional MLOps platforms’ ability to retrain models and account for feature drift is limited and often insufficient when source data distributions show significant changes. dotData Ops addresses this problem by using feature discovery technology to re-engineer new features to account for changes when data drift occurs. This capability enables BI and Analytics teams or ML Engineers to discover new data patterns that emerge as data changes and evolves. This marks a significant advancement in continuously discovering key insights and maximizing business impact.
dotData’s commitment to innovation and excellence in AI and ML continues with the launch of dotData Ops. By seamlessly empowering ML Engineers or BI and Analytics professionals to operationalize data, feature, and ML pipelines, dotData Ops is set to become a game-changer in the enterprise space. As businesses increasingly recognize the value of data-driven insights, dotData Ops is poised to play a critical role in accelerating the adoption and maximizing the impact of AI and ML solutions. Through streamlined deployment, comprehensive monitoring, source data diagnosis, and feature re-engineering, dotData Ops is redefining what is possible in MLOps. Experience the future of MLOps with dotData Ops and unlock the full potential of your data and analytics endeavors.
dotData’s pioneering automated feature discovery and engineering platform solves the hardest challenge of AI/ML projects. Our Feature Factory technology discovers hidden gems for empowering your business as transparent, explainable features by connecting the dots within large-scale data sets in hours, without human bias. It enables data scientists to explore 100X more features, including those you’ve yet to imagine, and arguments AI/ML projects in an agile manner to deliver business value faster. In an era of rapid change, AI-discovered insights can be a game changer for business growth and innovation across industries. The power of dotData’s platform and ability to provide game-changing insights is why Fortune 500 organizations across the globe use dotData.