Managing Successful Machine Learning Teams

GUPTA, Gagan       Posted by GUPTA, Gagan
      Published: June 18, 2021

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Managing any technical team is hard, Managing a Machine Learning team is even harder.

Machine learning solutions and workflows are meant to save time and vastly improve operational efficiency, but you still need the right human team to ensure every aspect is optimized and running in all dimensions. These folks know enough to have a sense of good use cases for machine learning. Where everyone gets stuck is actually making it work, hiring people and making them successful.

There is no "one size fits all" solution, when it comes to manage successful ML teams.

The Business Problem needs to be Solved

Before getting started with finding the right people, you should take stock of the business problem at hand. The goal of an ML initiative may be to optimize rote business processes (e.g. automation) or it may be to establish a core piece business offering. No matter the case, it is imperative to first establish how the ML model fits within the greater workflow. Once your organization understands the implications of ML on the business, then it can begin to assemble the optimal team. And in most cases, you won't need to hire a full-stack ML team. Identify your organizations archetypes and their Machine Learning maturity level.

Most successful data-driven companies address complex data science tasks that include research, use of multiple ML models tailored to various aspects of decision-making, or multiple ML-backed services. In the case of large organizations, data science teams can supplement different business units and operate within their specific fields of analytical interest. Obviously, being custom-built and wired for specific tasks, data science teams are all very different. Find ways to put data into new projects using an established Learn-Plan-Test-Measure process.

Challenges for startups

Startups, in the early stages of operations, are typically bootstrapped and have limited budgets to deploy for building machine learning teams.

It might feel impossible for a small, unfunded or underfunded company to get machine learning expertise in-house. At this size, you are going to rely on your ML practitioner to implement everything end-to-end from data collection and cleanup to deployment. The actual machine learning-specific part of the task is almost certainly very small. If your startup has a core product or service founded on ML, then it's imperative to hire machine learning talent early on to build the MVP, and raise funding to hire more talent and scale the product.

Require more hands-on machine learning talent that can operate across the entire machine learning lifecycle - from data engineering, algorithmic and model development to deploying and monitoring machine learning models in production instead of specialized talent to focus individually on the various aspects of the machine learning lifecycle.

A seasoned engineer who has gone back to school or done some online work in machine learning can work out well because the goal isn't perfection- it's getting a system working end-to-end and then slowly optimizing all the steps. Hiring someone who is heavier on the engineering and data side of things is definitely the way to go.

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Managing Successful Machine Learning Teams
Managing Successful Machine Learning Teams

Central, Functional Approach

Companies usually start with the central, functional approach, and often fail with it. Some manage to transition to a decentralized or mixed model, and some simply cut off the losses, and a few lucky stays with it.
The functional approach is best suited for organizations that are just embarking on the analytics road. They have no need to analyze data from every single point, and consequently, there are not so many analytical processes to create a separate and centralized data science team for the whole organization.
Its a different approach when building a team in a growing startup or mid-size business that is interested in multiple machine learning applications. This model has a bunch of key strengths, first, it minimizes the idle time of the data science/ machine learning function in the company. This in turn means companies can kick off such a function extremely early.
Second, it maximizes knowledge sharing across the function, thus allowing a depth of knowledge to be developed, standards to be set, a shared tech stack to be created. All of this minimizes the time to value the function needs.

Some companies build a functional machine learning group and some companies embed the machine learning into teams. Many people have strong opinions on this, but I'm pragmatic and I've seen both structures work. A single machine learning team can really help with talent which is often the biggest bottleneck. In this approach, you need to manage the team carefully to make sure they are working on the most relevant tasks for the organization. Dispersing machine learning expertise throughout an organization can make practical machine learning experts happy because they are closer to the end user, but it can make recruiting harder.
Companies who choose to employ such an organization form thus aim to unleash these strengths by employing a shared tech stack, regular workshops & lots of knowledge sharing tools across the function, and distribute the workload across the function by one central product manager. They have clear boundaries on the collaboration with engineering teams like '3-month projects' and hand over the code, ownership & expertise if reasonable as so as not to create coupling.

Often the thing that the machine learning team is optimizing with their algorithm is fundamentally different from what the business needs. Without a working end to end system, this misalignment can go undiagnosed for months. Therefore, it's crucial to get end to end systems working as fast as possible before iterating on them.

Decentralized Approach

Decentralized machine learning work might integrate machine learning engineers straight into product engineering teams or it might put a machine learning team together with its product team beneath one single product manager. The first strength of this model is the concentration of product ownership. This results in a fast cycle time. Taking a new machine learning idea to production takes just as long as it would take adding a new drop-down option. No need to consolidate roadmaps of different teams. This of course unleashes the true potential of machine learning since it's really the only thing that allows experimentation, which is almost impossible in a collaboration context. The second strength is the concentration of product knowledge. Machine learners and frontend developers participate in all team events alike and get all the knowledge they need to deliver the right thing at great speed.

Elite teams at FAANG are using standard, open source models and architectures. It's always best to start with the simplest, most standard thing and layer in complexity. Every additional piece should make a major difference in the algorithm's performance. The decentralized model works best for companies with no intention of spreading out into a data-driven company. It may also be applied to the early stages of data science activities for the short-term progress of demo projects that leverage advanced analytics.

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Team assembly and scaling

The initial challenge of talent acquisition in data science, besides the overall scarcity of experts, is the high salary expectations. Expectations vary depending on geography, specific technical skills, organization sizes, gender, industry, and education. Hiring a generalist with a strong STEM background and some experience working with data, is a promising option on the initial levels of machine learning adoption. A way to address the talent scarcity and budget limitations is to develop approachable machine learning platforms that would welcome new people from IT and enable further scaling.

Manage Machine Learning projects can be very challenging. Machine Learning progress is nonlinear. It is very common for projects to stall for weeks or longer. In the early stages, it is difficult to plan a project because it's unclear whether what will work. As a result, estimating Machine Learning project timelines is extremely difficult. And often, leadership just does not understand it. The secret sauce is to plan the Machine Learning project probabilistically! Building a successful ML team means bringing together people who can successfully address a business problem.

You need talent that has a deep knowledge of modeling. They need to understand the capabilities of computer learning. Then this centralized team can work cross-functionally with all business leaders to develop specific AI applications. Ultimately it will be the teamwork that drives the application.


Choose the wrong model for your company and your efforts will waste money, and eventually shut down the efforts. Choose the right model, unleash the strengths of a model instead of playing to its weaknesses, and you will be able to integrate machine learning & data science deep into the company, far beyond the analytics function. The only thing that counts at the end is turning this new stuff into business value. The question you need to ask yourself is : Is the problem I'm trying to solve by ML, crucial to my business? If you don't have a clear application for machine learning, you're going to regret your investment.

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