Summary

In this chapter, we have discussed a number of important concepts that help define why reproducibility is important and why it should be a part of a successful data science process.

We've learned that data science models are used to analyze historical data as input with a target goal to calculate the most probable and most successful result. We've established that replication, the ability to reproduce the results of a scientific experiment, is one of the fundamental principles of good research and that it is one of the best ways to ensure that your team is doing everything to reduce bias in your models. Bias can creep into a calculation from misrepresentation in a training dataset. Often, this reflects historical and social realities and norms accepted in society. Another way to reduce bias in your training data is to have a diverse team that includes representatives of all genders, races, and backgrounds.

We've learned that data dredging, or fishing, is an unethical technique used by some data scientists to prove a predefined hypothesis by cherry-picking the results of an experiment and only selecting the results that prove the desired outcome and ignoring any inconvenient trends.

We've also learned about the MLOps methodology, a lifecycle of a machine learning application, similar in its principle to the DevOps software lifecycle technique. MLOps includes the following main phases: planning, development, training, validation, deployment, and monitoring. All of the phases are continuously repeated, creating a feedback loop that ensures seamless experiment management from planning through development and testing to production and post-production phases.

We've also reviewed some of the most important aspects of ethical AI, a discipline of data science that focuses on ethical aspects of artificial intelligence, robotics, and data science. A failure to implement an ethical AI process in your organization might lead to undesirable legal consequences if deployed production models are found to be discriminatory.

In the next chapter, we will learn about the main concepts of the Pachyderm version-control system, which can help you address many of the issues described in this chapter.