In this blog post by Head of Solutions Shoera Sels, we will walk you through the steps needed to broaden your MLOps maturity within your organization. In the first piece, MLOps is an essential component for companies looking to maximize the value of their AI initiatives. Shoera explained why in a previous blog article.
Welcome to this blog post, the second in our three-part series on MLOps (Machine Learning Operations). MLOps is a trending buzzword that is often shrouded in mystery and uncertainty, which is why the goal of this series is to demystify it. In the first article, we explored the key benefits of MLOps for an organization. This post will delve into how organizations can kick-start their MLOps journey, and the third post will go deeper into the different building blocks of MLOps. By the end of this series, you will have a clear understanding of what MLOps is, its benefits, and how it is crucial for organizations looking to capitalize on AI-driven growth and to maintain a competitive edge in today's rapidly evolving business landscape. So, let's dive in and get started!
The full scope of MLOps can be very broad, which might be overwhelming when you just get started. Following this guide will teach you how to assess your organization's current MLOps situation, determine your AI needs, and create a solid plan for implementing and maturing your MLOps framework. Understanding these steps will enable your organization to effectively harness the power of AI and drive transformative change across various aspects of your business.
1. Define the MLOps vision:
To kickstart your MLOps journey, you first need to define a clear MLOps vision. This vision should take into account your organization's current needs and needs in the near future. Make sure to identify and involve all stakeholders, such as management, operations, and IT, to ensure alignment and understanding of their roles and responsibilities.
2. Define the MLOps tech stack:
Next, identify the tools and technologies you will use in your MLOps implementation. For this, you can use use the Superlinear MLOps framework (more on this soon!). Discuss with other departments to see what tools are already being used and which integrations will be necessary for the long term.
3. Focus on a pilot project:
Start with an average machine learning project that represents the typical ML work your organization handles. This pilot project will help you learn and adapt as you go, creating an impact within the organization and showcasing the value of MLOps. Make sure to automate and use templates for the steps which will be repeated often in the next projects.
4. Onboard pilot engineers:
MLOps is a way of working. As long as it is not applied across the team, it will not bring the value you want. Train and onboard the pilot engineers who will be using and implementing the MLOps framework. Start with demos, followed by training sessions, and finally, let them develop their own projects within the framework while being coached by someone with more experience.
5. Extend the MLOps framework:
As your team gains experience, focus on extending the MLOps framework by adding more complex projects or developing solutions for different environments. This will help broaden the tools and capabilities available within the MLOps framework. Again, make sure to automate and use templates for the steps which will be repeated often in the next projects.
6. Write documentation:
Ensure that there is clear documentation outlining the steps, guidelines, and best practices for using the MLOps framework. This will empower your team to solve their own problems and contribute to the knowledge base around MLOps.
In conclusion
By following these six steps, you can successfully kickstart your MLOps journey and set your organization on the path to fully harnessing the power of AI. Implementing and maturing your MLOps framework will enable your company to remain competitive and thrive in a rapidly evolving technological landscape.
Questions on how you can implement MLOps? Don’t hesitate to reach out!
