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3 Crucial Concepts MLOps Engineers Should Teach Data Scientists

Best practice, Data science,
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MLOps (Machine Learning Operations) plays a critical role in modern data science, helping to streamline the process of building, deploying, and maintaining machine learning models. However, one challenge MLOps faces compared to DevOps is the lack of education about best practices among data scientists. In this article, we’ll discuss three essential concepts that MLOps engineers should teach data scientists to bridge this knowledge gap and improve collaboration.

1. Git

One common challenge that data scientists face is managing multiple versions of their code and notebooks. It’s not uncommon to see filenames like version1.ipynb, version2.ipynb, final.ipynb, and reallyfinal.ipynb. This approach is not only confusing but also makes it difficult to track changes and collaborate with other team members.

Teaching Git

To help data scientists overcome this challenge, MLOps engineers should teach them how to use Git, a popular version control system. Git allows users to track changes in their code, collaborate with others, and manage different versions of their work effectively. Here are some key concepts to cover when teaching Git:

By mastering Git, data scientists can better collaborate with their colleagues and maintain a clean, organized codebase.

2. Development Environments

Sharing a “requirements.txt” file is not sufficient for ensuring consistency in development environments. Data scientists need to understand the importance of hardware and software compatibility to prevent inconsistencies and potential issues in their work.

AWS SageMaker Studio: A Cloud-Based Solution

AWS SageMaker Studio is an excellent starting point for data scientists looking to adopt consistent development environments. This cloud-based solution offers a range of features to help teams manage their machine learning workflows more efficiently.

Introducing AWS SageMaker Studio

One way to start teaching data scientists about development environments is by introducing them to AWS SageMaker Studio, a fully managed development environment for machine learning. If your team is already using cloud-based notebooks, SageMaker Studio can be an easy transition. Key features to highlight include:

By adopting a consistent development environment, data scientists can ensure that their code runs smoothly across different platforms and team members.

3. CI/CD (Continuous Integration/Continuous Deployment)

In a well-designed ML infrastructure, the CI/CD process marks the point where data scientists say farewell to their models as they head for deployment. This separation between experimentation and deployment ensures a higher degree of safety and reliability for the business.

The Importance of CI/CD in MLOps

CI/CD is crucial for MLOps because it:

Teaching CI/CD to Data Scientists

When teaching data scientists about CI/CD, be sure to explain the benefits of automating the build, test, and deployment process, including increased efficiency, reduced risk, and faster time to market.


As the field of MLOps continues to grow and evolve, it’s essential for data scientists and MLOps engineers to collaborate effectively and share knowledge. By teaching data scientists about Git, development environments, and CI/CD, MLOps engineers can help bridge the knowledge gap and improve overall team productivity. By embracing these best practices, organizations can ensure that their machine learning projects run smoothly, from initial experimentation to final deployment, and unlock the full potential of their data science efforts.

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Unlock your future in MLOps with Navigating MLOps: A Beginner's Blueprint.

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