The final goal of all industrial machine learning (ML) projects is to develop and rapidly bring ML products into production. However, it is highly challenging to automate and operationalize ML products, and thus many ML endeavours fail to deliver on their expectations. The paradigm of Machine Learning Operations (MLOps) addresses this issue. MLOps includes several aspects, such as best practices, sets of concepts, and development culture. It is designed to bridge the gap between data science projects and production-grade ML applications.
MLOps helps organizations create a streamlined process for building, managing and deploying ML products. The result is improved efficiency, increased agility, and enhanced compliance. All of these benefits ultimately help organizations make better decisions faster, improve overall performance, and stay ahead of the competition. With MLOps in place, organizations can adopt ML technologies quickly to drive innovation and remain competitive in a rapidly changing environment.
MLOps is an interdisciplinary group process, and the interplay of different roles is crucial to designing, managing, automating, and operating an ML system in production. By collaborating closely, they can ensure that the ML product meets the needs of the business while also being reliable, secure, and compliant.
In this blog post, we will discuss the key roles in MLOps and explain why collaboration between these roles is so important. We will also describe what each role does and how they work together. Finally, we will offer businesses tips on creating an effective MLOps team.
The main roles in MLOps are the business stakeholder, solution architect, data scientist, data engineer, software engineer, DevOps engineer and ML (or MLOps) engineer. Each of these roles has different responsibilities and brings a unique set of skills to the team.
Business Stakeholder: The business stakeholder sets the ML project goals and defines the metrics that will be used for measuring success. They may also be involved in assessing the return on investment (ROI) generated with an ML product.
Solution Architect: The solution architect designs the architecture and defines the technologies to be used, following a thorough evaluation.
Data Scientist: The data scientist converts the business problem into an ML problem and takes care of the model engineering, including selecting the best-performing algorithm and hyperparameters.
Data Engineer: Data Engineer (similar role: DataOps Engineer). The data engineer builds up and manages data pipelines while ensuring proper data ingestion to the databases of the feature store system.
Software Engineer: The software engineer applies software design patterns, widely accepted coding guidelines, and best practices to turn the raw ML problem into a well-engineered product.
DevOps Engineer: The DevOps engineer bridges the gap between development and operations and ensures proper CI/CD automation, ML workflow orchestration, model deployment to production and monitoring.
ML Engineer/MLOps Engineer: The ML engineer or MLOps engineer combines aspects of several roles and thus has cross-domain knowledge. This role incorporates skills from data scientists, data engineers, software engineers, DevOps engineers, and backend engineers (see Figure 3). This cross-domain role builds up and operates the ML infrastructure, manages the automated ML workflow pipelines and model deployment to production, and monitors both the model and the ML infrastructure.
By working together closely, all of these roles can ensure that businesses get the most out of their ML investments. From setting clear objectives to deploying a reliable product, each role plays an important part in creating a successful MLOps implementation.
To create an effective MLOps team, businesses should ensure that all roles are properly staffed, and that team members have the necessary skills and training. Additionally, businesses should strive to foster open communication and collaboration between all of the roles involved in the project. By doing so, they can ensure that everyone is working towards a common goal.
Finally, businesses should ensure that each role is aware of its responsibilities and knows how to best collaborate with each other. With this in mind, businesses can create an effective MLOps team and achieve success with their ML projects.
To ensure long-term success, businesses should also commit to regular reviews of their MLOps team. This means assessing the team’s performance and making necessary changes to ensure that the ML product meets its objectives and delivers the desired results. Additionally, businesses should strive to develop an organizational culture that values learning and innovation. By doing this, they can foster a positive environment that encourages the continuous development of skills and strengthens the team’s overall performance.
By creating an effective MLOps team and committing to ongoing reviews and learning, businesses can ensure successful implementations of their ML projects. This will help them make better decisions in the future and get the most out of their ML investments.
