06-04, 10:30–10:55 (Asia/Jerusalem), Hall 2 (PyData)
This real-world model operationalization case study will highlight common mistakes, propose solutions, workarounds and practical tips to successfully deploy a ML model
A Case study: How to effectively operationalize a Machine Learning model
A European shipping company was looking to gain a competitive advantage by leveraging Machine Learning techniques. The aim was to create shipping-lane specific demand forecasting, and to implement it throughout its operations, in order to: save time and manual labor, adjust pricing and business agreements, and utilize smart resource allocation. Each percentage of improvement is worth $1.5 million.
In order to effectively operationalize a Machine Learning model you need to cross 3 chasms: the first is Business Relevance - avoiding model development before thinking the business value through. A clear vision of the desired business impact must shape the approach to data sourcing and model building.
The second is Operationalizing Models - migrating a predictive model from a research environment into production. This process can be difficult because data scientists are typically not IT solution experts and vice versa.
The third, and most critical chasm is Translating predictions to business impact - where a data scientist ensures the decision makers understand the predictions and have enough wiggle room to take action and turn it into a competitive advantage. Management must possess the muscle to transform the organization so that the data and models actually yield better decisions. Additionally, model outputs need to be integrated into well-designed applications making them easy to consume.
In this talk, I will explain these three elements using a real-world case study. I will highlight common mistakes to avoid when operationalizing a Machine Learning model in an enterprise environment. I will present specific lessons learnt and practical tips from this real world project.
Moran Haham is a senior data scientist at SparkBeyond, whose core business is a cutting-edge technology for data science. Moran is experienced in a wide range of client domains, e.g., retail, finance, banking and logistics where she delivered a massive impact to SparkBeyond’s clients altogether. Apart from the client-facing side, she contributes insightful leads to the R&D into the existing and to the future products. Moran also leads beginners and advanced training for data scientists. Previously Moran worked in Perion networks as a data scientist developing and deploying ML algorithms on large datasets. Moran has an M.Sc. in NLP and recommender systems from Ben-Gurion University and a B.Sc. in industrial engineering & management from Tel-Aviv University.