S2 EP 4 of Future Proof: Data Science Workflow: How Orchestration Optimizes Value

The power of predictive models hinges on the quality of data they use, both for training and production. Orchestrating the data pipelines that feed machine-learning algorithms is therefore a critical success factor for data science. This is especially true for solutions that rely on diverse data sets. And as many practitioners recently learned, disruptions like COVID19 can wreak havoc on the efficacy of such models. Agility is therefore paramount.

Hear Bloor Group CEO Eric Kavanagh explain why data science programs must embrace intelligent automation and orchestration. He'll be joined by Basil Faruqui of BMC Software, who will demonstrate why Control-M has emerged as a leader in data science workflow. He'll show how predictive capabilities can be optimized by abstracting away complexity, and how scalability can be achieved via automation.
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