Training and Operationalizing Interpretable Machine Learning Models
Training and Operationalizing Interpretable Machine Learning Models


AI offers companies the possibility to transform their operations: from AI applications able to predict and schedule equipment’s maintenance, to intelligent R&D applications able to estimate the success of future drugs, until HR AI-powered tools able to enhance the hiring process and employee retention strategy. However, in order to be able to leverage this opportunity, companies have to learn how to successfully build, train, test, and push hundreds of machine learning models in production, and to move models from development to their production environment in ways that are robust, explainable, and repeatable.

Nowadays data scientists and developers have a much easier experience when building AI-based solutions through the availability and accessibility of data and open-source machine learning frameworks. However, this process becomes a lot more complex when they need to think about model deployment and pick the best strategy to scale up to a production-grade system.

In this talk, we will introduce some common challenges of machine learning model deployment and we will discuss the following points in order to enable you to tackle some of those challenges:

1. How to select the right tools to succeed with model deployment
2. How model interpretability toolkits can be used for model training and deployment
3. How to use automated machine learning to optimize your machine learning deployment flow
4. How to build multiple robust machine learning pipeline using tools such as Jupyter Notebooks, Virtual Machines and Containers
5. How to register your model and transform it into a web service that can be easily consumed by other data scientists and developers


Francesca Lazzeri, PhD is a machine learning scientist, author and speaker. She leads an international team of data scientists, developers and cloud advocates at Microsoft. Before joining Microsoft, she was a research fellow at Harvard University in the Technology and Operations Management Unit. She is also board member of Microsoft "Women@NERD" association, data science mentor at the Massachusetts Institute of Technology and Columbia University, and active member of the AI community. Find her on Twitter: @frlazzeri and Medium: @francescalazzeri

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