Best practices for ML teams

What does it take to get the best model into production? Clear goals, ML tools, regular model monitoring, and standardized workflows are essential. This article outlines best practices for ML teams to create positive business impact and generate value.

Published on:

March 22, 2024

According to Mckinsey, AI has the potential to grow to a CAGR of 40% by 2030. It uncovers diverse use cases, such as improving productivity leading to operational gains and introducing new features to enhance customer experience and engagement. 

How to Build an ML team?

Business leaders are investing significantly in ML teams to deliver the promise of machine learning. 

Building ML teams requires several steps:

  1. Define the team's goals and objectives: Building successful ML teams depends on your organizational goals and vision. Clearly define the goals you are trying to achieve and the desired outcome of your ML project. This will help you identify your team's critical roles and skill sets. 
  2. Build a diverse team: Having a team with various skill sets and backgrounds can bring different perspectives and approaches to problem-solving. It is essential to have a mix of data scientists, engineers, domain experts, and project managers. 
  3. Foster a culture of learning and experimentation: Encourage team members to learn and stay current with advancements in the field continuously. Also, create an environment where experimentation is encouraged, and failure is viewed as an opportunity to learn. 
  4. Use collaboration tools: Utilize tools such as version control, documentation, and project management software to facilitate collaboration and communication within the team. 
  5. Establish clear roles and responsibilities: Assign specific roles and responsibilities to team members to ensure everyone knows what is expected of them and minimize inefficiencies. 
  6. Implement a robust pipeline: Develop a strong pipeline for data collection, preprocessing, model development, testing, and deployment to ensure that your models are robust, accurate, and deployed promptly. 
  7. Continuously monitor and improve your models: Monitor and evaluate your models' performance and make adjustments as necessary. This will help you identify areas for improvement and ensure that your models meet your organization's needs.

Organizing an ML team

Centralized ML team

People from different backgrounds, like a product, DevOps, engineering, and ML, come together and collaborate under one big team. Such a team becomes a task force for all your ML initiatives that an organization plans to employ. This way, the entire ML process, from initiation to execution, becomes effortless and fast. 

However, such a team organization's downside is that knowledge gets limited. And with barriers to knowledge, such a model leads to increased dependency and hinders democratization. 

Decentralized ML team

A decentralized ML team is a small team that includes technical experts delivering a specialized solution or feature. Here the structure is very agile as the team from diverse backgrounds comes together for a specified deliverable and dissolves. 

Various Roles in ML teams

A matured ML team consists of the following:

  1. Data Analysts 
  2. Data Engineers 
  3. Data Scientist
  4.  Research/Applied Scientists 
  5. ML Engineers 
  6. Developers

Let’s discuss them in detail. 

Data analysts

They work closely with product managers and business analysts to derive actionable insights from user data which will then be used to drive the product roadmap. The core skill of data analysts is to analyze data using inferential and descriptive statistics. The most popular tools they use are SQL, Excel, and data visualization tools like tableau and Power BI. 

Data Engineers

ensure the infrastructure to collect, store data, and transform processes are well-built. They are responsible for managing how data from the application is ingested and transferred across databases and storage. They use tools like Spark or Hadoop to handle large volumes of data; they work with cloud platforms for building data warehouses. They are responsible for ETL jobs, which means taking data from sources, processing it, and storing it in data warehouses. 

Data Scientist

They analyze, process, and interpret data. Data scientists use advanced statistical strategies to derive insights from data and communicate their findings to business stakeholders. They are responsible for building ML models that become part of the product. 

Research Scientist

They generally are required by organizations that focus on bleeding edge technologies to develop new algorithms for various product-related areas. Research scientists have specialized knowledge in NLP, computer vision, speech recognition, or robotics which they acquire through a Ph.D. research experience. 

Machine learning Engineer

They focus heavily on ML models and the infrastructure. Their job is to build tools for updating models and creating end-user prediction interfaces. They are known to leverage cloud services like FastAPI or cortex to create endpoints. 


This is one of the most critical roles that help you integrate all your models with the main application. They design the APIs and format the model prediction into something user-friendly. 

Why is ML collaboration essential?

Understanding requirements

Most of the time, ML collaboration is not often paid much attention to because it leads to gaps in communicating the requirements that need to be understood and appropriately documented. As a result, the project might experience some setbacks regarding repetitive tasks scraping the work that has already expended effort. 

Pursuing the right direction

The clear focus on ML collaboration ensures that the project is progressing in the right direction and any unforeseen risks are communicated well in time. 

Approvals from stakeholders

ML projects are iterative. Data scientists frame the business problem into a statistical solution, the first step of data exploration. EDA is the most critical phase of the project, where numerous discoveries are made. During this stage, data scientists need better quality signals or patterns in the data to measure the success or encountering problems. Such findings often result in revised business goals. This is why clear communication is crucial to ensure that all stakeholders are on the same page. 

Union of data teams and business

The success of ML projects lies in the strong collaboration between the data team and the business team. Such continuous communication between both teams creates ML models that have the potential to add significant business value. 


Whatever model you opt for, one thing is sure to turn that model into business value. And only 13% of AI projects make it to production. However, by leveraging MLOps tools like Attri, you can build, deploy, and monitor successful models from raw data to production-ready models. 

With this, you can improve your operational processes and end-to-end customer experience and align product design with principles and practices. 

You can learn more about Attri by getting in touch with us here.