Closing the gap between their organization’s choice to invest in a data science and machine learning (DSML) strategy and the needs that business units have for results, will dominate data and analytics leaders’ priorities in 2022. Despite the growing enthusiasm for DSML’s core technologies, getting results from its strategies is elusive for enterprises.
Copyright: venturebeat.com – “How to build a data science and machine learning roadmap in 2022”
Gartner notes that organizations undertaking DSML initiatives rely on low-cost, open-source, and public cloud service provider offerings to build their knowledge, expertise, and test use cases. The challenge remains of how best to productize models to be deployed and managed at scale.
DSML is delivering uneven value in enterprises today
Data scientist teams in financial services, health care, and manufacturing tell VentureBeat their enterprise’s DSML strategies are the most effective when they anticipate and plan for uneven initial results by business unit. The teams also say producing models at scale using MLOps is fundamentally different from producing mainstream internal apps with DevOps. They add that the more complex the operating model of a business unit, the steeper the MLOps learning curve. DSML’s contributions to business units vary by the availability of reliable data and how clearly defined problem statements are.
O’Reilly found that “enterprise AI won’t have matured until development and operations groups can engage in practices like continuous deployment until results are repeatable (at least in a statistical sense), and until ethics, safety, privacy, and security are primary rather than secondary concerns.
Kaggle indicated that 80.3% of respondents use linear or logistic regression algorithms, followed by decision trees and random forests (74.1%) and gradient boosting machines (59.5%). Enterprises are just scratching the surface of DSML’s potential, with adoption slowed by several factors that need to improve in 2022.