Machine learning operations, aka MLOps, emerged from the need to rapidly and effectively deploy machine learning (ML) models. It’s best defined as a set of tools, practices, techniques, and culture ensuring dependable and scalable deployment of machine learning systems.
But have you ever wondered about the keys to successfully achieving your MLOps goals?
Look no further than an adept MLOps team – they handle the heavy lifting to hasten the artificial intelligence model’s development and deployment, from continuous integration/deployment to continuous training, validation, and governance. By establishing the right team structure, you’ll be able to lay a solid groundwork for your ML project and enhance the success rate of ML model deployment.
No need to sweat. We’re here to help!
This in-depth article will walk you through the 6 essential roles of a well-established MLOps team, as well as common MLOps team structures available, and then suggest the right structure for your own business.
Let’s dive right in!
6 Important Roles in a Matured MLOps Team
Creating successful machine learning projects necessitates diverse skills and collaboration among team members. A robust MLOps team doesn’t just comprise data scientists; it requires a multifaceted team of experts offering numerous important skills.
An ideal MLOps team structure should involve the following:
- Data scientists to craft, build, and adjust the model
- Platform or machine learning engineers to furnish a model-hosting environment.
- Data engineers to establish production data pipelines for model retraining.
- Software engineers to embed the model into business systems.
It’s worth noting that in smaller businesses, these roles might be part-time or done by one person only, or one individual might take over multiple positions simultaneously. They handle everything from model development to deployment and monitoring, known as Full Stack Data Scientists. In larger organizations, there might be more resources so each personnel could fulfill one specific role.
Below are the 6 main roles in a matured MLOps team: data analysts, data engineers, data scientists, research/applied scientists, MLOps engineers, and developers.
1. Data Analyst
Data analysts collaborate closely with product managers and business teams to derive insights from user data collected through surveys. They aim to analyze the connections between different insights and perform necessary statistical analysis.
- Skills needed: Descriptive Analysis, Comprehensive Analysis.
- Tech Stack: SQL, Excel, Tableau.
2. Data Engineer
Data engineers in data science teams construct a robust data infrastructure that manages data collection, transformation, and storage. They also determine the format and entry points of data into the machine learning pipeline. In addition, they oversee Transform Load processes involving sourcing, processing, and warehousing data.
What’s more, they’re in charge of tailoring ML solutions to meet stakeholder requirements while carefully considering trade-offs
- Skills needed: Distributed System Fundamentals, Data Structures, Algorithms.
- Tech Stack: Spark, Hadoop.
3. Data Scientist
Data Scientists, a widely recognized profession, specialize in obtaining value from processed data. They’re responsible for data analysis, processing, and interpretation while developing machine learning models.
These people can leverage the data engineer’s infrastructure to accomplish their objectives and might also need to create metrics for production monitoring.
- Skills needed: Subject Knowledge, Communication, Interpersonal Skills.
- Tech Stack: SQL, Python/R, Machine Learning.
4. Research/ Applied Scientist
Organizations planning to pursue innovation, cutting-edge technology, and groundbreaking solutions should give heightened consideration to this role. Personnel in this position focus on developing new algorithms to push the boundaries of MLOps. However, their job doesn’t necessarily require adapting their innovations to practical production.
Typically, a research scientist possesses specialized expertise in NLP, computer vision, speech, or robotics, often acquired through a Ph.D. or extensive research background. Their responsibilities encompass conducting research, publishing academic papers, and solving challenging research dilemmas.
- Skills needed: Specialized expertise in NLP, Computer Vision, and Statistics.
- Tech Stack: SQL, Python/R, Machine Learning.
5. MLOps Engineer
MLOps engineers specialize in the operational aspects of deploying, monitoring and managing a machine learning model. Their primary duties are setting up and sustaining ML pipelines, integrating CI/CD pipelines for machine learning projects, and guaranteeing the scalability and dependability of ML infrastructure.
- Skills needed: Programming, Cloud Services, Machine Learning, IaC, and Communication.
- Tech Stack: Terraform, AWS CloudFormation, Google Cloud, Python, Java, Scala.
6. Software Developer
Developers function as the final link in the chain, connecting your entire machine learning pipeline to the core application. They must smoothly integrate the entire ML pipeline, from digesting data to generating output, leading to the final product. Additionally, they may perform as on-call resources for monitoring and acting as versatile engineers, addressing any gaps.
- Skills needed: Back-end Development, API Generation, Integration.
- Tech Stack: Rest, AWS Lambda, AppSync, etc.
2 Common MLOps Team Structures
There are 2 primary frameworks commonly adopted by companies to organize MLOps teams with key benefits and challenges.
1. Centralized/ Functional Teams
The centralized MLOps team structure needs one team managing the entire MLOps lifecycle, including data engineering, model deployment, monitoring, and more. This team typically comprises data scientists, ML engineers, and software engineers.
- Talent allocation
A standalone group of ML engineers and data scientists results in a concentrated pool of expertise within the ML team. This enables the company to engage the most skilled MLOps practitioners for any task, regardless of its origin within the organization.
- Speed and knowledge sharing
An autonomous ML team enables the swift initiation of new MLOps concepts. Also, this structure fosters widespread knowledge exchange, facilitating the development of in-depth expertise, shared standards, and a common technological foundation.
Plus, this approach works as a hub to disseminate ML know-how to other teams over time, empowering different parts of the organization to conduct their AI experiments.
While centralized teams offer an effective method to structure your MLOps workforce, this approach comes with certain limitations.
Due to its autonomous nature, without proper integration, the team and other departments might lack essential knowledge. Thus, it’s vital to implement measures to share knowledge and educate other teams.
- Resource management
Since the team often lacks software development resources, it’s necessary to collaborate effectively with other teams. While a shared responsibility approach can be successful, you must ensure team alignment regarding work priorities and project objectives.
- Overwhelming number of applications
Centralized ML teams often confront the challenge of having an abundance of use cases to select from across the company. Therefore, it requires transparent and reasonable processes to prioritize tasks and handle business stakeholders’ requests.
For example, it’s justified for the data science team to prioritize the development of MLOps infrastructure for faster AI project progress in the future despite the extended time required. However, because of differing expectations and standpoints, some other departments or leaders might perceive it as slow progress or fruitless results, leading to potential frustrations.
- Lack of business insight
Another issue is that the team is short of understanding of the business aspects. Even with a highly competent data science team, without comprehending the company’s core business, customer needs, and overall strategy, they might still produce an irrelevant output. Hence, other teams should also help and share this knowledge with the ML team.
It’s usually difficult to estimate an ML project, especially in large organizations where many teams collaborate. In such settings, clear project plans with set deadlines and budgets are common. But MLOps implementation is often trial-and-error, so an ML team finds it hard to have accurate and fixed answers to questions like “How much data is required?” and “How long does it take to develop a machine learning model that yields X result?” without experimentation first.
Given this inherent unpredictability of AI, it is recommended that the centralized team have their separate budget and that any project planning take experimentation into account.
- Defined function
The MLOps team needs a clear function beyond merely “developing AI.” What exactly is the primary team role? Is it dedicated to researching and developing future technologies? Or does it focus on enhancing business metrics in the upcoming months by expanding the product range through AI capabilities?
Without a clear role definition, it’ll be challenging for data science teams to align with organizational expectations, which could result in discontent among business stakeholders and employees.
2. Decentralized/ Squad Teams
A decentralized MLOps team is made up of a complete “feature” group consisting of members from product, marketing, software engineering, design, and MLOps. These are often called squads, aiming to build a particular feature or product.
A decentralized team, aka a cross-functional team, focuses more on the product and possesses a wealth of product expertise. Coupled with data science knowledge, this kind of team structure, therefore, could help the company rapidly and effectively deliver intended outcomes.
Besides, this MLOps setup also facilitates seamless experimentation with different MLOps concepts.
These diverse teams offer more independence, so you don’t have to depend on other teams to develop a complete product. This fosters faster development times by eliminating the need for different teams to align their objectives and priorities.
For example, ML engineers at LinkedIn could first not test their “recommendation engine” due to a lack of a front-end, so they formed a decentralized squad to test the “People You May Know” feature. This group, comprising design, web, product marketing, and engineering, was then able to deliver one of LinkedIn’s most successful features – “People You May Know.”
- Clear focus
Decentralized teams typically have a clearer emphasis than centralized teams, often existing to create new products and services. This aids in task prioritization and prevents a loss of focus.
- Organizational understanding
Since this kind of team consists of multiple people from various parts of the company, chances are they understand the overall organizational landscape better.
For instance, including a backend engineer in a decentralized team with previous experience in the company enables a better understanding of integrating with other tech products.
- Knowledge sharing
ML engineers and data scientists in these teams can help enhance the team’s understanding of machine learning. Conversely, the larger team contributes knowledge from organizational operations to development procedures. Together, high-performing decentralized teams foster mutually beneficial internal relationships.
- Research and development
Two key advantages of decentralized teams are speed and independence. While decentralized teams excel in practical work and delivering business value to the organization, they might not be the best fit for an AI research and development setup.
Another possible challenge is that these teams occasionally isolate themselves from other development initiatives, potentially leading to limited awareness of external developments.
Therefore, robust processes are needed to ensure these teams stay informed about the progress of others and share their advancements with relevant stakeholders, aiming towards the organization’s overarching strategy.
Recruiting talent might also pose a challenge, particularly if you aim to employ one MLOps professional for each team. This role, where one individual has to handle all responsibilities, might be perceived as less appealing than being part of a centralized ML team.
However, with appropriate strategies, this could no longer become a drawback for potential candidates.
How to Choose the Right Structure for Your MLOps Team?
A key factor in determining the appropriate structure for your MLOps team is your business’s maturity and the objectives of your AI endeavors.
1. Small Startups
For startups, especially those with a limited headcount and a strong focus on product development, a centralized team may not be the most suitable option.
Speedy development and quick decision-making are typically top priorities, so for the majority of small startups, a decentralized approach is much preferred.
However, an exception arises for research and development-oriented startups working on cutting-edge AI, as a centralized MLOps team can be advantageous for their exploratory work, with long-term rather than immediate benefits.
2. Large Startups
The suitable approach for larger startups varies based on whether the goal is on research and development (R&D) or practical AI implementation. If you aim to implement AI into your product, decentralized teams are recommended. But if you intend to invest considerable time and resources in exploring new AI approaches and gradually implementing them, a centralized team proves to be more appropriate.
Many enterprises may benefit from a combination of both approaches. You can establish a centralized core team for infrastructure development and best practices alongside decentralized units for practical application across various business functions. This hybrid approach offers the benefits of both systems but necessitates clearly defined responsibilities to avoid conflicts.
Another enterprise option is to form a centralized team with ML product development capabilities. These teams operate as independent startups, facilitating better project prioritization and integration within the organization. This approach also allows for improved prioritization, where the team can select an ML project that offers the most benefit to the entire company rather than just their specific area. And to deliver value, effective integration with the rest of the organization is extremely needed.
Netflix serves as a notable example, utilizing a centralized MLOps team structure for deploying and managing ML models, powering their recommendation system, personalization features, and content creation.
Form a MLOps Team Now!
In conclusion, a fully developed MLOps team often consists of 6 essential roles that work harmoniously to bring your AI initiatives to life. They are data analysts, data engineers, data scientists, research/ applied scientists, MLOps engineers, and developers.
Choosing between the centralized or decentralized MLOps team structure depends largely on your company’s unique characteristics and AI objectives. Both models have strengths and weaknesses, providing opportunities to expedite AI implementation. Ultimately, the key is to align the structure with your business and objectives while maintaining adaptability to tackle emerging challenges.
At Neurond, we offer MLOps consulting and development, catering to businesses implementing centralized and decentralized structures. We can serve as a dedicated development team, extend your current team, and even offer a project-oriented approach. All are available to help you accomplish your specific project goals. Contact us now!