Various Roles in a Typical Machine Learning Project

GUPTA, Gagan       Posted by GUPTA, Gagan
      Published: June 19, 2021
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Roles in an ML team align very much with the organization's hierarchy and how it's projects are designed. There is quite a difference in managing and delivering a Agile or a Waterfall based Project, in contrast to deliver a ML project. You see, the outputs in an ML project are not as clearly defined as they are in the normal projects. Multiple organizations use Machine Learning to manage and improve operations. While ML projects vary in scale and complexity requiring different data science teams, their general structure is the same. Organizations face challenges in scaling DS/ML projects because they lack the requisite skills, collaboration, tooling and know-how to create and manage a robust, production-grade DS/ML/AI pipeline.
Often, Data Scientists have to wear too many hats due to a dearth of talent across other roles in any DS/ML project.

Through 2023, the ML engineer role will be the fastest-growing role in the AI/ML space. Gartner estimates that today there is one ML engineer for every 10 data scientists, and it will likely change to between 5 and 10 by 2023.

Three Core Roles in a Machine Learning Team

- Data Engineers
Data Engineers makes the appropriate data available for Data Scientists. They focuses on data integration, modelling, optimization, quality and self service. Their responsibility is to prepare all the necessary data in a form that is consumable for their colleagues.They generally create a Data Lake for this purpose. AWS, Kafka, Airflow, Databases are some their key skills.

- Data Scientist
Based on the inputs from Engineers, Data Scientist identifies use cases. They are responsible determining appropriate datasets. They design algorithms, experiments and builds AI models. One of the key question data scientist asks is 'How can we use this data to build a machine learning model for predicting something?' Python, Machine Learning, SQL are some of the key skills for this role.

- ML Engineers
Deploys ML/AI models through effective scaling and ensuring production readiness, ensures continuous feedback loop. In the core team, act as the glue between data scientists and data engineers, operations (DevOps, DataOps, MLOps), and business unit leaders. Their focus is more on engineering than on modeling.

Product Manager

A product manager is someone responsible for developing products. Their goal is to make sure that the team is building the right thing. They are typically less technical than the rest of the team: they don't focus on the implementation aspects of a problem, but rather the problem itself. Product managers do a lot of planning; they need to understand the problem, come up with a solution, and make sure the solution is implemented in a timely manner, assign the proper resources. To accomplish this, PMs need to know what's important and plan the work accordingly.

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Various Roles in a Typical Machine Learning Project
Various Roles in a Typical Machine Learning Project

Other Specialist Roles in an advanced ML Team

Projects which can afford resources and have the desired funding includes following specialized roles in their ML team, as well.

- Data Analysts
Data analyst serves as a gatekeeper for an organization's data so stakeholders can understand data and use it to make strategic business decisions. They are responsible for data cleaning, performing analysis and creating data visualizations using BI tools like SAS, Tableau, powerBI. They discover insights in the data and then explain their findings to Business unit heads.

- Research Scientist
They conduct original research in machine learning related to (but not limited to): deep learning algorithms and applications to structured data such as in machine translation, speech recognition, computer vision, etc. They are responsible to publish academic papers in leading scientific conferences Contributions to research communities/efforts, including publishing papers in machine learning (JMLR, ICLR, NeurIPS, ICML, ACL, CVPR) and communicate their results. They follow trends and market.

Secondary Roles in a ML Team

Besides the roles mentioned above, we have some roles needed in a team for obvious reasons. ML is not their primary field of work but they are remarkably useful to the project in the area they hold expertise.

- Statistician
- Researcher
- Domain expert
- Software engineer
- Reliability engineer
- UX designer
- Interactive visualizer / graphic designer
- Data collection specialist

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Big Team or Small Team

It can be overwhelming, just to have so many roles and resources in a ML team. Take a deep breath !, Depending on one's project needs, one may get good enough value from the first three key roles, as mentioned.
In a resourceful and mature company, finding these specialized roles is more common. However, if one is working for a smaller startup, chances are, one might not find a separate research scientist or an analyst. In such companies, one or more data scientists are expected to handle almost all components - analysis, model development, and deployment. You may call such people Full Stack "whatever".

There are pros and cons of both small and big teams. Communication is easier in smaller teams, productivity is higher. On the other hand, having different people specializing in different parts of the project requirements ensures that you get high-quality work with best practices and more coordination is needed. Confusion arises, decision making can be lengthy in specialized teams with multiple members.
Remember, too many chefs might spoil the soup !

Conclusion

The ML has evolved and developed enough to identify key roles and responsibilities involved in an ML project. However, the scope of work each job title entails can vary, depending on the organizations type, size and hierarchy. If one is working or looking to set up your team or improve team output, there are many tools and role structures to choose from that could make workflow more efficient by allowing easier inter and intra-team collaboration.

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Various Roles in a Typical Machine Learning Project
                         



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