Agile Machine Learning

The future of software is being driven by intelligent applications. By the year 2020, more than 85% of customer-to-business interactions will be carried out without humans (Gartner). 81% of IT leaders are already investing or plan to invest in artificial technology solutions.

In regards to rapid development, rapid response feedback loops, and continuous improvement, ML projects are well suited for Agile methodologies. However, there are some considerations for successfully using AI and machine learning for agile development, such as:

  1. Sprint Planning – Breakdown and define agile experiments to support rapid iteration and deliver incremental value.
  2. Rapid Iteration – Create data-driven feedback loops for research and production environments
  3. Agile Team Roles – Expand development team roles to include engineers, data scientists as well as Subject Matter Experts

This presentation covers best practices for successfully integrating agile development cycles with machine learning workflows.

Learning Outcomes:

  • Best practices for successfully integrating agile development cycles with machine learning workflows, including considerations for:
  • Sprint Planning – Breakdown and define agile experiments to support rapid iteration and deliver incremental value.
  • Rapid Iteration – Create data-driven feedback loops for research and production environments
  • Agile Team Roles – Expand development team roles to include engineers, data scientists as well as Subject Matter Experts.

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