Successful MLOps: Best Practices for Startups

Mar 2, 2023

5 min read

With all or most of your team in your Machine Learning startup directly coming from their undergraduate studies, they are implementing your machine learning model using the same practices they self-taught themselves. These practices, however attractive for a hackathon-level project implementation, may not bode well for your startup.

With all of NimbleBox’s efforts centered around MLOps, we have identified what are some of the general practices that your team may be carrying forward from their amateur days. These practices, however minuscule, can result in a heavy loss of time, money, and computing power. So let us take you through these points and how you can, as the head of your MLOps Team, lead them to success while keeping your investors happy.

Best Practices for MLOps Implementation in Startups

MLOps, or machine learning operations, is a set of best practices for deploying and managing machine learning models in production environments. For startups, implementing these best practices can be critical for ensuring the success of machine-learning projects. Let us take a look at some of these points that you need to clear before deploying your model for the wider audience.

1. Using version control for machine learning models: This allows startups to track and manage changes to their models over time, making it easier to reproduce results and roll back to previous versions if necessary. Additionally, using version control allows startups to collaborate more effectively with their teams and ensure that everyone is working with the latest version of the model.

Version control services like GitHub and GitLab can help create and manage a bunch of “screenshots” of your code which can be easily viewed, managed, edited, etc., by a huge number of collaborators. Such tools, however, may not be the best for your Machine Learning models where we are dealing with GBs of datasets and parameters. You can check out NimbleBox Build, wherein you can manage your instances version to ensure smooth rollback to desired ML Models.

2. Use of continuous integration and deployment (CI/CD) for machine learning models: This involves automating the process of building, testing, and deploying machine learning models, which can save time and reduce the risk of errors. Using CI/CD allows startups to quickly and easily update their models in response to changing data and business requirements.

An extension or rather an upgradation to versioning, CI/CD ensures that the model pipeline is rerun every time there is an update in the code. This ensures that the version accessible to everyone is the latest version, but this, in hand with good Version Control, also ensures that we can roll back in case of bad deployment.

3. Use of monitoring and alerting for machine learning models: This involves tracking the performance and behavior of the model in production and setting up alerts to notify stakeholders when problems or issues need to be addressed. Monitoring and alerting allow startups to quickly identify and resolve issues with their models, ensuring that they are operating optimally and delivering value to their users.

In the latest market, where more often than, your team is working in real-time ever-updating data, monitoring becomes an integral part of your machine learning pipeline. To make the most of your already established resources, make sure you have an efficient monitoring system in place. How about checking out our checklist to ensure the same?

4. Leveraging the power of Kubernetes: A developer tool introduced all the way back in 2014, Kubernetes may be the missing piece of the El Dorado of a machine learning pipeline deployment. It is essentially a container orchestration tool that works for the mass-scale deployment of machine learning algorithms.

Kubernetes helps create scalable distributed systems and can be used to bring much-needed flexibility to the various machine learning frameworks that data scientists can work on. The container-based model helps deal with distributed systems which is often the need when dealing with deployed machine learning models.

5. A/B testing post Deployment: A/B testing refers to a randomized monitoring process wherein two or more different versions of the model are shown to various segments of the users. A/B testing is also known as split testing.

Having a system like this post-deployment ensures a well-tested method of comparing the performance of the existing process and system with your current model. This can improve the performance of your model by miles by generating easily comprehensible data for updates in the model itself.

6. Cost Optimization: One of the most difficult aspects of training huge machine learning models is just the sheer computing power that goes into training these models. Now, this can be efficiently sorted by using Jobs. Jobs in Nimblebox are a simple set of codes that perform batch processing and shut down the machine after processing.

Such optimizations can be easily done by investing in a good machine learning pipeline orchestration tool such as NimbleBox, where other features like “Deploy” lead to a live endpoint and ever-running machines which make the life of ML Engineers much easier.

7. Soft Skills >>> Technical Skills: Lastly, we would like to talk about something that engineering hardly teaches us about, soft skills and project management. There is a good chance your Machine Learning team comprises more than 6 people. To maintain this well-oiled machine and ensure it doesn’t implode with overwhelming data. This can be easily achieved by using NimbleBox Workspaces, where you can collaborate with your team in a centralized platform for build, deployment, and automation.

Methods like Agile and Scrums can be included in your machine learning pipeline. Defining tasks with deadlines, tags, priorities, assignee, reportee, time dedication, etc., is essential to manage issues and tasks at hand. A hackathon setting for bug bashes for internal teams could also be a fun way to promote communication among team members.


In conclusion, implementing MLOps best practices is critical for startups that want to deploy and manage machine learning models in production environments. By using version control, CI/CD, monitoring and alerting, and IaC, startups can ensure that their machine-learning projects are successful and deliver value to their users.

Written By

Aryan Kargwal

Data Evangelist

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