10 Steps to make ML operational across organizations

1. Take Holistic approach to Machine Learning: Machine Learning should be viewed holistically as integral part of the companies strategy. By integrating it and running it alongside existing IT environments, processes, applications and workflows, greater results can be achieved.
2. Experiment and be wiling to Fail: Machine Learning algorithms are scientific in nature and their applications can only drive business results not the algorithm itself. Hence, the business problem in hand needs to be approached as experiment which can also fail sometimes.
3. Build a Multi-disciplined team: The Platform considered needs to provide the team with access to data, compute resources and libraries they need. The team needs to collaborate efficiently across disciplines, and the platform needs to enable the access and collaboration in a governed and secure manner.
4. Iterate quickly and optimize later: Let the Data Scientist’s have the freedom to iterate quickly through models that can be optimized later. Do not spend too much time in trying to perfect a model only to learn that the enhancement you were hoping for wasn’t achievable through machine Learning. Let the team experiment early, fall early and often, continuously learn and try new things.
5. Choose the right technology for ML model Optimization: Pick a platform that prioritizes holistic collaboration and streamline your ML workflow from data to production in a secure, interpretable and scalable manner. Make sure you are not choosing point solution or “Black-Box” ML platforms that create silos and compromise security.
6. Embrace Machine Learning by evolving your organization: In order to embrace machine learning, organizations need to be flexible in terms of efficiently weaving Machine learning development, production and maintenance into their existing processes, workflows, architecture and culture.
7. Maintain the integrity of Machine Learning Models: As the underlying data changes and shifts, the models using that data have to be updated and improved upon. Maintaining the integrity of the model demands vigilance otherwise the models may drift and become inaccurate and ultimately impact business.
8. Close the skills gap: Build a team whose experience, talents and capabilities, including Data Engineering, Data Science, DevOps, Software Engineering, product development and Domain expertise overlap. Look for candidates with core skills necessary to accomplish important task, get them together and let them learn from one another.
9. Treat models in production as living software: To protect models in production, it’s important that platform has the ability to secure them. That means having the visibility into model lineage and the control over who can access and make changes to the model.
10. Understand and abide by ethical obligations: Make sure you truly have the consent from the customer and other stakeholders to apply the necessary data against a machine learning model.

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