Build High-Performing Teams for Computer Vision Projects

Build High-Performing Teams for Computer Vision Projects

A diverse set of skills is needed to successfully develop a computer vision project. While trying to adapt to the different operational workflows of professionals, computer vision teams face workflow challenges when delivering ML. The maturity level of a project dictates a team's structure and infrastructural setup. At the beginning of a CV project, you will need data engineers and data scientists to manipulate and experiment with the data. Once you start getting to a point where the data has translated into a machine learning (ML) solution, you need to scale up and manage complicated workflows, and an ML engineer is introduced into the mix. These different roles that work in disparate environments using disparate tools are sometimes error-prone and nonoptimal. Ideally, utilizing an ML/MLOps platform from the start manages the complexity of various tools and infrastructure in a CV team for multiple disciplines, enabling all experts to build and interact seamlessly.

This article will discuss different roles in a typical computer vision team and their responsibilities. We also discuss the most effective way to promote optimal collaboration between CV team roles and their workflows. 

What roles do you need in a computer vision team?

Computer vision projects necessitate forming multidisciplinary teams comprising data scientists, machine learning engineers, and data engineers. Apart from their technical proficiency, these individuals must be able to connect theoretical concepts with practical implementations. This challenge is exacerbated by the necessity for seamless collaboration between these technical experts to guarantee that the models are accurate, relevant, and ethical for the problem being solved.

The key to establishing high-performance teams for computer vision projects is facilitating collaboration among experts with varied skill sets and roles, such as data scientists, data engineers, and ML engineers, by providing them with shared tools, platforms, and abstractions.

Core Team Roles and Responsibilities

What is the role of a data engineer in a computer vision project?

The data engineer is the backbone of a computer vision project, ensuring smooth data flow. Their primary responsibility is constructing the data pipelines—the intricate behind-the-scenes infrastructure enabling ingestion, storage, transformation, and data distribution across the project. Due to the unique requirements of computer vision projects, data engineers do a lot of groundwork to prepare all the necessary data in a consumable form for the data scientist. Visual data – images and videos — are significantly large, requiring more storage space than tabular data. Data engineers must design efficient storage solutions that are scalable and cost-effective. Vision data also come in various formats and often require extensive cleaning and pre-processing before data scientists can use it for training. It involves removing irrelevant information from images, correcting format inconsistencies, and adequately labeling objects within the data. 

What is the role of data scientists?

In a computer vision project, data scientists take on the roles of strategy leader and performance regulator. Thanks to their data analysis and model validation knowledge, they can connect the dots between the technical parts and the practical objectives. They collaborate closely with data engineers to explore the project's goals with the help of the prepared data. Data scientists figure out what the goals are before they establish success criteria for the computer vision model. 

Data scientists consistently evaluate the model's efficacy throughout the project's lifetime. They use various methods to find inefficiencies, biases, and mistakes. They may, for instance, check the model's accuracy in distinguishing between fruit types or its performance in a facial recognition system across various demographics. To improve the model's accuracy, efficiency, and fairness, data scientists conduct analyses and then suggest changes to the model's training data, algorithms, or hyperparameters.

What is the role of a machine learning engineer?

The machine learning engineer is the architect and builder of a computer vision project. It entails overseeing the entire life cycle of the computer vision software, from its initial design to real-world deployment. They orchestrate scalable workflows for processes like model deployment to enable data scientists to deliver their solutions faster and more efficiently. On a more technical level, they develop tools, pipelines, and workflows that allow users (data scientists) to update models and inference interfaces seamlessly. They leverage machine learning frameworks and algorithmic expertise to translate the data scientist's strategic vision into a functioning model. ML engineers utilize existing machine learning (ML/MLOps) platforms or build their own to accomplish their tasks.

Managing ML Teams Workflow With a Platform for a Computer Vision Project?

Other specialized roles like SRE, Ops (i.e., DevOps/MLOps), or platforms engineers can contribute and manage the processes of the core ML team, as they provide complementary skills focused on the automation of the machine learning processes, coordinating their implementation in production, system monitoring, etc. With more team players, it takes less work for the team to deploy and operate in a production environment. However, with a robust machine learning (ML/MLOps) platform, the functions of these roles are streamlined. Sometimes, having a DevOps / SREs is an overkill in these scenarios. 

Picsellia is one such robust ML/MLOps platform primarily focused on abstracting and managing the operational workflows in computer vision projects. It empowers core ML teams to do without an Ops or SRE professional. The platform handles the heavy work with features like pre-configured settings, automated model training and deployment pipelines, and built-in monitoring dashboards. This enables the machine learning team to zero in on core functions such as model development, research, and experimentation.


Building high-performing teams for computer vision projects necessitates a strategic combination of expertise. Leveraging a robust computer vision platform that automates the operational aspects (ML/MLOps), teams can streamline workflows, reduce development time, and ensure efficient model deployment. This allows data scientists, researchers, and machine learning engineers to focus on the core aspects – model development, algorithm exploration, and continuous refinement. This combined approach fosters innovation and collaboration, enabling teams to push the boundaries of what's achievable in a computer vision project.

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