Achieving AI success (with DevOps & DataOps)


Success of AI / ML adoption requires alignment of multidisciplinary teams, collaboration practices (such as DevOps + DataOps) and access to large volumes of (relatively) clean (curated) data. 

 

AI requires selecting platforms, tools, and infrastructure that needs to be ramped up and down as experiments are conducted. Teams can consider a multitude of platforms (TensorFlow, Keras, PyTorch, Caffe), cloud provider (AWS, Azure, Bluemix, Google), and a growing number of collaboration platforms (Dataiku, H20.ai, Databricks, Anodot, Clusterone and others) as part of their AI and machine learning environment. The job of architecture and product management teams.

 

With data ready, teams need a working process for running experiments using Agile  practices and governance mechanisms to capture metadata and results – for analysis and review – to determine success levels and what follow-on experiments to prioritise. This involves a level of strategic thinking and portfolio planning.

 

When results yield satisfactory results, teams need to determine how to establish a production process to run new data through the AI models.

 

The article references tangible success stories.


 

 #dataops #devops #enterprisearchitecture #productmanagement #emergingtech #analytics #AI #ML #Agile 


 

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