Applying devops in data science and machine learning

Having data scientists collaborate with devops and engineers leads to better business outcomes, but understanding their different requirements is key

Applying devops in data science and machine learning
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Data scientists have some practices and needs in common with software developers. Both data scientists and software engineers plan, architect, code, iterate, test, and deploy code to achieve their goals. For software developers this often means custom coding applications and microservices; data scientists implement data integrations with dataops, make predictions through analytical models, and create dashboards to help end users navigate results.

Devops engineers looking to automate and collaborate with operational engineers should expand their scope and also provide services to data scientists as part of their charter.

Larger organizations with multiple data science teams may invest in data science platforms such as Alteryx Analytics, Databricks, and Dataiku that provide a mix of tools for developing, testing, and deploying analytical models. These tools compete on dataops and analytics capabilities, integration options, governance, tools for business users, and deployment options.

Devops requirements for data scientists differ from application developers

Not every organization may be ready to invest in data science platforms, or it may have small data science teams who only need basic operational capabilities. In these cases, it may be better to apply devops best practices to data science teams rather than selecting and instrumenting a platform.

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