The machine learning pipeline is an often misunderstood concept. There are a variety of machine learning processes that need to happen to go from an idea to a working application with machine learning embedded. You can think of machine learning as a list of steps that need to happen to get to your finished working application. However, machine learning is special because it brings together a lot more steps than traditional software engineering. It makes the process of creating an automated machine learning pipeline more difficult.
A traditional machine learning pipeline starts with acquiring data, and it ends up with a machine learning engineer putting the model into your working application. That is why it is crucial for you to understand how everything works, as that is the only way for you to understand how to improve your processes and results. Most machine learning initiatives fail, and a reason for that is a misunderstanding of what the various machine learning processes are.
Stages of the Machine Learning Pipeline
The goal of every machine learning project is to put an accurate model into a production application. However, you also need to feed that model with good data. Building machine learning pipelines like this isn’t easy, and there are many machine learning processes you have to go through.
As a machine learning engineer, it is also crucial for you to automate portions of the pipeline. Automation streamlines the entire ML pipeline, making it possible to deploy successful models a lot faster.
It all starts with an idea and the data needed to test whether that idea is true or not. This is the part of the ML pipeline where your data scientists have come up with a hypothesis and are testing it in one of many statistical modeling tools in the industry. They can use these tools to ensure that they have a good handle on things. Most of the data ingestion and cleaning is done manually at this step, and business goals are evaluated.
The next step is to start training the model using the newly acquired data. This is still the step that is done by data scientists, but this is changing quickly with the introduction of MLOps. At this stage of the machine learning pipeline, machine learning engineers are usually not involved in the process of putting this model into use.
After this step, the model is optimized, evaluated, and it is eventually deployed by machine learning engineers. It is finally at this page where we learn whether the project was a success or not. In reality, 85% of models never make it into the application, meaning that the failure rate is quite significant.
Problems the ML Pipeline Solves
This pipeline is important because it solves a lot of challenges that businesses have. The main one is that most of these processes are repetitive. By using the ML pipeline, businesses can start to automate a lot of the processes that they would have to do manually in the past.
The next problem it solves is that it helps fix errors quickly. Because of the iterative nature of the machine learning pipeline, you can easily see how your changes lead to side effects. You can then make the necessary adjustments and continue iterating on your project.
Overall, this helps you reduce risk and create more accurate machine learning models.
Using an Off-the-shelf ML Pipeline Solution
It might seem like a handful to build an entire ML pipeline yourself. That is because it is, which is why you should focus on using off-the-shelf solutions like xpresso.ai. An off-the-shelf solution like this helps you streamline the machine learning process, making it easier for you to bring successful models to production.