An Introduction to MLOps
Unless you already have a specific amount of training, you may be asking yourself, “What is MLOps?” In machine learning engineering, MLOps is a critical component focusing on optimizing the process of both deploying machine learning models and maintaining machine learning models.
MLOps cannot be handled by a single position, but requires a team typically made up of data scientists, DevOps (development operations) engineers, and IT professionals. In this guide, we will break down what MLOps stands for, how it is used, and how to get started effectively using MLOps for your projects and goals.
What Does MLOps Stand For?
MLOps stands for machine learning operations. As mentioned before, it is a set of methods for data scientists and operations experts to collaborate and communicate. MLOps is gradually becoming a stand-alone solution to machine learning lifecycle management. From data collection to health and diagnostics, all of these and more can fall under the machine learning operations umbrella.
When DevOps, IT, and machine learning engineer professionals are able to sit down and create a streamlined process for machine learning model success, then they are participating in MLOps.
This improves not only the quality and process of starting and maintaining machine learning and even deep learning models, but improves the quality of the end-product of these Machine Learning (ML) and Deep Learning (DL) models.
When a proper MLOps system is in place, then businesses can choose the perfect machine learning models to use for their desired results. MLOps then allows for those models to align with business demands and follow specific regulations depending on the business.
After answering, “What is MLOps?” we need to be able to understand how to use and implement MLOps.
What Is the Use of MLOps?
To summarize everything so far, MLOps is a process for developing and improving machine learning and other artificial intelligence solutions. The reason why someone would want to invest in a proper MLOps department and team is that it's difficult for machine learning to be reproducible with the same or similar outcomes.
Machine learning is an incredibly complicated process that takes time, energy, and care to create an AI program to do exactly what a business wants. Therefore, a machine learning operations team is necessary to make these machine learning models and processes repeatable and effective for a business.
If a business is using machine learning models, then they want them to perform at their maximum potential. An MLOps platform can help accomplish this goal.
For example, the online supermarket, Ocado, uses an MLOps platform to personalize the experience for each Ocado customer, predicts customer demands and fills stock appropriately, and optimizes supply chain routes. It is this use of machine learning operations that allows Ocado to perform at peak potential for its customers.
Another, more well-known example, would be Uber. They use MLOps to optimize their app to have highly-accurate drive time estimations for both drivers and passengers coupled with improving location accuracy for riders being picked up.
It’s the machine learning operations team who ensures the machine learning models are running properly, being maintained, and are producing the results Uber wants over and over again.
In both of these examples, as well as many others, MLOps is used to create automated processes to make a product or service easier to use for the customer as well as improve and streamline processes for the business as well as the customer.
With a proper MLOps platform in place, the limits to machine learning applications for businesses are only limited by the creativity of the business.
What is the Difference Between MLOps and DevOps?
One thing to note is the difference between DevOps and MLOps. While these two have similar strategies, processes, and end results, they have fundamental differences requiring them to be separated while still working together.
The short answer: MLOps is focused on the machine learning engineering side of the process while DevOps focuses on the software and application side of the process.
DevOps team member helping with MLOps, Image Source
While they share ideas, they are working towards different goals that must ultimately coincide together for an optimized customer experience.
Here are just a few of the key differences between DevOps and MLOps:
- MLOps requires plenty of experimentation to create their end product. Model training is different from code testing because there is a lot of trial and error involved in creating a machine learning model. Failure is more of an option for MLOps because even failures can lead to greater success.
- The testing processes between the two are different. Testing for a machine learning model requires model validation and model training whereas DevOps is focused on code testing.
- An offline-trained machine learning model cannot be used as a prediction service right away. To automatically retrain and deploy a model you will need a multi-step pipeline where you can automate the manual activities of the data scientists who built the machine learning model.
- Machine learning models in production need to be monitored constantly. Even the data that built the model needs to be monitored so that you can refresh the model when needed.
What is MLOps technology and How to Use It
With an understanding of MLOps and the way these platforms and teams can be utilized in many industries, we can discuss how to use the technology and start putting it to use for your business.
One key piece of information to understand is that, while some businesses can and do build a machine learning operations platform and team from whole cloth, many utilize team members in different departments to serve as part of the MLOps team.
All of our suggestions here to get started can cover either current department team members or new hires entirely.
Putting together the right MLOps team is important, which is why we have decided to outline a few key players you need for a great team.
- Data scientists: depending on the size and scope of the machine learning model being created, a business could use one data scientist to collect and input data or have an entire team of data scientists. The role of the data scientist is critical to the success of the machine learning model, though.
- Operations: just as with any product or service, there will need to be operations team members to handle logistics, timelines, and supply chain if necessary.
- IT management: since machine learning models operate either in an online capacity, having IT members manage infrastructure as needed and ensure that all MLOps operations stay on track will be critical to the safety and success of the machine learning model.
- Machine learning enterprise leader: while this seems obvious, it is important to employ someone who has experience not only working with machine learning models, but also has experience in managing the operations of an MLOps team in order to handle the necessary daily tasks and meet goals for the machine learning model to be implemented within the business.
While there are other positions that have been created for MLOps teams, these are the crucial ones to get started. If your machine learning model is going to be implemented for customer use, then having a team of app developers and UX designers will also be important components of your machine learning operations team and platform.
Looking For More Information On Machine Learning or MLOps?
Hopefully this article gives you a fundamental understanding of MLOps and how to decide if it's the right choice for your business or organization.
Machine learning is growing and changing each year, with businesses all over the world learning how to use machine learning in new and creative ways for their businesses. So, if you think we missed something in this introduction to MLOps, then we would love to hear from you in the comments below.
If you’re wanting to get started with the right equipment to build an MLOps platform or have any questions about machine learning, then we would be happy to help in any way we can. Just contact us here.
If you're looking for a system to start working on AI/machine learning research, SabrePC has fully turnkey workstations with a 3 year warranty starting at only $3,700.
Feel free to browse other articles on the SabrePC blog, and keep a lookout for more helpful articles on the way soon!