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.

Total
1
Shares
Related Posts
Read More

Data Iceberg Model for Machine Learning

One of the pitfalls to developing production-ready machine learning solutions is the failure to identify the appropriate data assets. In evaluating the data assets to be used for your project, use the Data Iceberg Model approach to determine the underlying (i.e. not visible) structures that triggered the creation of the dataset. The following Data Iceberg Model can be used to evaluate the quality & limitations of the data used to train your machine learning models.