Setting up an MLOps team at your startup can be a challenging task. It can range from buying an off-the-shelf solution or using open source technologies and building one yourself. Choosing between these alternatives will depend on several factors, including the time and resources you have to devote to this new role within your company and the nature of your company's challenges.
In this blog post, we're going to expand further on what the previous blog, "A Guide To Setting Up Your MLOps Team," talked about and explore whether you should buy or build your own MLOps platform by looking at the pros and cons of each option.
We will discuss the pros and cons of setting up your MLOps department on top of in-house built systems and outsourced bought or Open-Sourced systems, and by the end of the blog, I hope you have gotten some clarity for your startup or enterprise!
Let us first look at the steps involved in MLOps and what we need to achieve with the eventual solution!
What is an MLOps Pipeline?
Let us take a page out of NimbleBox's "Ultimate Guide to MLOps" to see what we mean by MLOps and an MLOps Pipeline!
MLOps lies at the very center of the union of Machine Learning and DevOps! MLOps is a field emerging to unify the launch cycles of Data Scientists and Software Developers. This launch cycle contains various steps and responsibilities that will spread across your new hires!
Data Orchestration: A promising data pipeline ensures a unified source of ground truth, which can be viewed through dashboards, enabling authors to get insights into various parameters that test your model's efficacy in the wild.
Model Development: Building the correct model, training it, and ensuring that it appreciates and maps the data well!
Model Deployment: Deployment, the action of bringing resources into practical action, is a process where most ideas and concepts smother out. Stemming from the contrast between perfect lab conditions and the actual raw world is far and wide.
Monitoring: Having the ability to watch and change the model's behavior on the go, with switches in place to revert to an older stable version.
Why would you want to Build an MLOps Platform in-house?
As developers, we believe we can build almost anything, as we should. We're hackers. But reality kicks in when we must maintain all that code once we have created a prototype. Remember the famous Elon tweet - Prototypes are easy; production is hard. (Taken directly from our CEO's mind, catch Anshuman on LinkedIn here 😉)
However, this may also make sense if you are dealing with a very niche field with a specific use case in mind. Especially in the medical and defense area, the Machine Learning pipeline requires specific inputs and care.
In contrast to an open-source or outsourced pipeline, an in-house pipeline gives you complete control over the specifications and data, allowing you to implement a different level of security according to your requirements.
Motivated to build your own Machine Learning Pipeline? How about checking out some resources from our side at "Top 10 Python Packages for MLOps"!
Why would you go for an Open-Sourced or Out-Sourced Pipeline?
As a developer, you are not only looking to work on the code for the project, but with it also comes all the tasks associated with MLOps, namely, pushing them out into production, collecting results, performing A/B tests, and fine-tuning the model.
Although you may be determined to build them in-house, these are some gigantic tasks over here! How about checking out individual packages and solutions for each specific use case?
While a Buy/Open-Source aided is enjoying and meticulously reworking and bettering their product with their reviews and monitoring, a Built one may still be, well, building.
This method often works by "building" on top of the open-sourced resources to ensure it fits your data, privacy, and efficacy needs. Still, it also saves a bunch of time working on the individual components!
However, your third-party resource may have to be changed occasionally because these tools may eventually stop being updated and have no support left!
Conclusion
Looking at both the spheres, the solution that seems to prevail is going for a mix of two. First, building every Pipeline component from scratch can be a costly task ranging from extra resources for staff, experimentation, and production.
Instead of investing time, money, and hands reinventing the wheel, how about going for an Open-Source resource or fully functional platform?
Start by scoping your idea, your needs, and your abilities. List off different aspects of your project, like, functionality, scalability, and maintenance. Next, thoroughly check out existing MLOps platforms and stacks that suit your team and use case.
Build the solutions that you aren't able to find in the market and open-source sector. Then, in that particular solution, too, only work on the aspect that needs your attention and integrate the rest with a platform or service.
After going through the blog, do you feel like getting to the experimentation but are confused? How about checking out NimbleBox.ai. Book a demo with us today to learn more about us and our services!
Written By
Aryan Kargwal
Data Evangelist