At ING bank, machine learning models are a key factor in making relevant engagements with our customers, empowering them to stay a step ahead in life and in business. in our efforts to make the model building process more rapid, compliant, validated and accessible to roles other than data scientists (such as data analysts or customer journey experts), we have structured it for an easy creation of propensity models. in this talk, i will present this “model factory” structure, focusing on pipelining data science models in apache spark. in particular, i will address complying with GDPR, describe the workflow and monitoring of our processes, and describe the engagement analysts and customer journey experts have with the result set of the models created.
- Pipelining data science models in apache spark.
- Leveraging on shared data repositories – for accelerated model building.
- Monitoring and evaluating data science models.
➡ View/Download the PDF presentation file here https://datainnovationsummit.com/wp-content/uploads/2019/04/M1_11.45_Dor_Kedem_ING.pdf