Mastering MLOps: A Journey Through Four MLOps Components
This article aims to decode four main MLOps components. MLOps is an extensive field that includes numerous elements. In this piece, Shoera Sels, Head of Solutions at Radix, provides you with the MLOps structure, helping you understand what it encompasses.
If you've been following our series, you'll recall that in the first installment, we discussed the reasons for developing an MLOps framework and how it can benefit your organization. In the second one, we explored how to initiate this journey, which can initially seem quite vast.
! Do you prefer to watch/listen instead of reading? No problem, watch Shoera take you through our six-step approach to MLOps in this presentation ⬇️
The Importance of MLOps
Firstly, let's revisit why MLOps is essential. While we have an entire video dedicated to this topic, let's recap briefly. The fundamental aim of MLOps is to streamline the process from the initial idea to a fully operational, mature AI solution. This is critical when your goal is to implement several AI solutions, as you would want to avoid encountering the same roadblocks repeatedly.
Think of MLOps as the race track for your AI model, which is the racing car. Even if you have the best car, if it's driven on a cobblestone track, it won't perform optimally. The race track and the pit stops need to be as good as possible to extract maximum value from the car. Similarly, focusing solely on developing an accurate AI model won't guarantee value extraction or timely results. Optimal operations can be ensured through the right processes, infrastructure, and team collaboration.
Four Components of MLOps
For a better understanding of the different components of MLOps, let's walk through the stages an ML engineer goes through while developing a model.
Data Acquisition
The first step in model building involves getting the right data into the hands of the data scientist. Various questions may arise at this stage, such as ensuring easy access to the correct data, standardizing data processing, and ensuring data labeling. Addressing these questions helps identify some building blocks that fall under data acquisition, including data access and management, data versioning, data pipelines, and data labeling.
Model Development
After deciding on data usage and tracking, we move on to the model development phase. The ML engineer, at this stage, focuses on selecting the appropriate model and features to ensure the model's efficacy. To speed up this process and to make collaboration between different data scientists run smoothly, ideally, the team uses the same best practices. This can go from using templates to doing code reviews to making libraries. Next to that, we also want to make sure our training process is as reproducible and automated as possible. Therefore, having training pipelines, which clearly state which actions to perform when training a new model, is a very big win. Lastly, tracking your experiments will make it easier to select and reproducing the winning model at the end of the experimentation phase.
Model Integration
An AI model in itself is often not very useful. It needs some extra elements around it, in order to make it work in a production environment. This requires model packaging and model deployment. The model packaging stage involves placing the model into a container, like an API, that facilitates communication with it. The model deployment stage involves setting up an environment for the model to run continuously and make necessary computations.
Model Operations
Even after successful model integration and deployment, several challenges may arise during model operation. These involve verifying the infrastructure's trustworthiness, ensuring smooth inference processes, and monitoring the model. It is also important to consider security aspects and the need for continuous operations if real-time predictions are necessary.
Conclusion
To summarize, an optimized MLOps process involves four key steps: data acquisition, model development, model integration, and model operations. By ensuring that each of these steps runs smoothly, with clear roles and automated processes where possible, you can establish a mature MLOps solution.
Even within these components and subcomponents, there are varying levels of maturity. A process may be manual with low repeatability, or it could be automated and self-improving. Depending on your needs, you can transition from manual to automated processes.
Remember, not every MLOps solution needs to be at the highest level of maturity. The effort to set up might be excessive for a model that is run infrequently. Therefore, it is important to identify the unique requirements of your organization and adapt your MLOps practices accordingly.
Stay tuned for our next blog post, where we delve deeper into these components and explore their various levels of maturity.
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