More and more organizations are incorporating data science and machine learning (DSML) technology into their business strategies, with 56 percent of enterprise executives planning to invest further in DSML adoption this year. 

A recent VentureBeat article examined some of the critical capabilities that companies need to make sure are on their DSML roadmap, which we’ve provided a handy breakdown of below, including: 

Adaptive ML 

According to VentureBeat, one of adaptive ML’s greatest potentials lies in the manufacturing sector, where telemetry data from visual IoT sensors could be combined with adaptive ML-based predictive analytics applications to identify defective products and remove them from the production line. Other prime examples of adaptive ML include autonomous, self-driving vehicles and smart, collaborative robots that quickly learn to complete simple tasks through iteration. 

Collaborative Workflow Support 

If a DSML platform doesn’t offer collaborative workflow support, the workarounds can eat up significant time and resources. As adoption accelerates, it’s imperative that collaboration tools and workflows also evolve. Considerations include communication and code sharing across each step in the modeling process, data lineage and model tracking, and version control and model lineage analysis. 

More Experience with MLOps 

One of the key success measures for DSML projects is reducing the cycle times for creating and launching new models, and this is an area in which MLOps can assist. In addition to providing greater model scalability, MLOps can also help organizations track back-to-business outcomes using metrics and KPIs relevant to the C-Suite and individual line of business owners. 

Increased Adoption of Transfer Learning 

Transfer learning allows companies to get a jumpstart on new model development by reusing existing trained ML models. The technology is especially beneficial for teams working with supervised ML algorithms that require labeled data sets to deliver correct analyses. Rather than starting from scratch each time with a new supervised ML model, teams can use transfer learning to customize models for individual business goals quickly. Transfer learning modules are also becoming more common across process-centric industries that rely on computer vision because of the scale they provide for labeled data.

The above are just a few DSML capabilities gaining traction—head over to VentureBeat for more on why these and other considerations belong on your organization’s 2022 roadmap.