Testing a Data Science Model

Laveena has heard from other senior testers that they know of data science teams but no testers testing the models. How do we have enough confidence what is produced is good enough? A model is a statistical black box, how to test it so we understand its behaviors to test is properly. Main aim would be to help inspire testers to explore data science models. Laveena invites you to her talk where she will share her journey of discovering data science model testing and find the following takeaways useful not just for testing a data science model but day to day testing too. Key takeaways:

• Have a better understanding of what data science is
• Know how we can test models
• Know what existing skills we already have as testers, that can be applied in a data science team
• Leave with resources to help our teams’ better structure itself to have confidence in the data produced
• How can we have enough confidence in the results a data science model produces?
• We'll look at what did or didn't work + a little exercise
• Some useful tips to takeaway