Best Practices to become a Good Data Scientist or Machine Learning Engineer

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Best Practices to become a Good Data Scientist or Machine Learning Engineer

Learning the important practices done by data scientists and machine learning engineers ensures that one produces work that is high quality and impactful in the organization.

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There has been a large number of courses that teach the fundamentals of programming and data science. They do a good job in reinforcing various concepts in machine learning and show various steps that are usually followed when building a project with ML capabilities. While these courses mostly focus on the theoretical aspects of machine learning, it can be handy if one learns to put more emphasis on the good practices when building applications related to data science and machine learning.

With the rise in data and an exponential increase in the compute power, there has been a rapid increase in the demand for people who would make use of the data and generate predictions along with useful insights depending on the use case of the project. Furthermore, there are numerous data related positions such as data engineer, data architect, data scientists, deep learning engineer and machine learning engineer. These positions usually require that one has a good understanding about data processing, feature engineering, extracting, loading and manipulation of data. For the positions such as data scientists or machine learning engineers, it is often important to build state-of-the-art models that perform really well on the test data (data which was not seen by the models before). With a lot of steps involved in the data science workflow, it becomes important, therefore, that one also learn the useful practices when building an ML application. Below are some of the best practices that a data scientist or a machine learning engineer could follow to build a higher quality code and better outcomes for the project.

Get an Accurate Understanding of Business Problem

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With a large number of responsibilities along with way, it is often true that one might get caught up in the tide without defining the deadlines or the business goals of a project. What makes things complicated is that there is a possibility that things related to ML in the project are not clearly stated or defined. In this case, it can be good to take action by understanding the requirements of the project along with its scope and learning how feasible is machine learning. Recognizing these key measures and acknowledging whether one can actually implement artificial intelligence and whether it can have a good impact in the value created can push your efforts and impact in the project.

Start with a Simple Metric

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There are a lot of metrics in machine learning such as the mean absolute error, mean squared error, mean absolute percentage error, root mean squared errors in the case of regression type problems. If we consider the classification problems, we have metrics such as precision, recall, accuracy, f1 score, micro f1 score, macro f1 score and many others. By looking at all these metrics, one can be convinced to use all of these metrics when testing and understanding these models. Nonetheless, it can get tricky to accept the right kind of metric due to the vast number of them. The best thing to do, in this case, would be to choose the simplified metric that is highly interpretable and understandable, depending on the problem. After learning about this metric and analyzing the impact, it is possible to add it to our ML for predictions.

Build a Strong Data Science Team

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Data science is about communication, action and automation of the systems so that it reduces the human effort and helps companies gain large profit margins. To build tools that have AI capabilities, it is important to work with a team with vast amounts of knowledge and insights starting from data collection, data preparation, training the models, and deploying the service in the cloud so that it is accessible to the end-user. In other words, data scientists might not be adding a lot of value if the product that they produce is not consumed by the end-user. Therefore, they must work with a team of people with knowledge from different domains so that they can build and ship a fully functional product.

Learn to Impress the Business Stakeholders

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While the technical capabilities of a machine learning and deep learning products are impressive, they might have little to no value if they fail to impress the business stakeholders and deploying them does not have a large margin gain in terms of the profits to the organization. What I basically mean by the above statement is that though we have results from ML models that have exceedingly low mean absolute error, mean squared error or any of the errors for that matter, if they fail to make a business impact though they are technically well structured and feasible, it means that the organization is not able to monetize on the outcomes based on artificial intelligence. Therefore, the problem must be defined on the basis of the overall increase in revenue as a result of deployment, profits and whether a customer is more engaged or not. By taking these factors into consideration, it is possible to better define the goals and outcomes from a project along with additional infrastructure spending needed to run the algorithms.

Communicate your Results

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You have spent a significant amount of time say a month to collect additional data, generate key insights along with find the most important features that are useful for the ML models and in general to determine the outcome, it is now time to articulate your results to the team so that they take the necessary time to take action based on your outcomes. Though it is impressive that you have spent a good amount of time to understand the business problem and also learn the most important features in the data, failing to elucidate what you have learned and worked can often slow the progress of the project. Hence it could be very useful to let the team know about the areas that you are tackling along with the outcomes obtained as a result of the work.

Constantly Monitor the Results after Deployment

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After the deployment phase, it is time to constantly monitor the performance of the ML models and see whether there is a degradation in the performance based on the predictions. There are important Key Performance Indicators (KPIs) that can help monitor how the ML model is doing in production. Therefore, keeping an eye out for the model performance can help in ensuring that the model is generating the business impact and profits for the organization.

Conclusion

We have seen some important good practices to become a good data scientist or a machine learning engineer. While this article does a good job in highlighting a lot of good practices, there are still some other practices that are still important and could be considered that are not highlighted. But going over this article should hopefully give you a good picture about the things that could be done to become an effective data scientist or a machine learning engineer. Thank you for taking the time to read this article.

Below are the ways where you could contact me or take a look at my work. Thanks.

GitHub: suhasmaddali (Suhas Maddali ) (github.com)

LinkedIn: (1) Suhas Maddali, Northeastern University, Data Science | LinkedIn

Medium: Suhas Maddali — Medium

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