<ul><li>Organizations need to focus on use cases and metrics first and realize that exceptional model accuracy may not deliver business value. </li> </ul>

<ul><li>MLOps will have a breakout year as organizations gain more experience scaling models for deployment faster while tracking business outcomes for greater results. </li> </ul>

<ul><li>DSML platform providers need to scale up and automate the entire ML workflow at scale. </li> </ul>

<ul><li>Adaptive ML shows potential for improving cybersecurity, remote site security, quality management in manufacturing, and fine-tuning industrial robotics systems. </li> </ul>

<ul><li>Privacy concerns will force every organization creating sensor-connected products and the services supporting them to use synthetic data to build, test, and refine models. </li> </ul>

<ul><li>Collaborative workflow support in DSML platforms becomes table stakes for competing in the market. </li> </ul>

<ul><li>Transfer learning will see rapid adoption across enterprises with DSML strategies operating at scale and in production today. </li> </ul>

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.g

DSML is delivering uneven value in enterprises today

Despite the growing enthusiasm for DSML's core technologies, getting results from its strategies is elusive for enterprises.

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