The term ‘machine learning (ML) pipeline’ refers to the most efficient methodology for creating a machine learning model. It comprises multiple steps, and substantial quantities of data before deployment become a possibility – and, of course, for interventions to be made by the most central component to developing artificial intelligence: the human being.
Read more below.
The Data, and the Human in the Loop
These days, it goes without saying that data really is everything. In fact, many ML projects perform poorly simply because of a lack of data, without which we cannot hope to teach a machine to think like a human being.
A successful machine learning model – one that is underpinned by mountains of data – can be used to respond to a near-countless list of challenges that arise in a business. However, models such as this depend heavily on the size of the training data they can access. Collecting data is a complicated process, particularly if it involves a large company. At the same time, the data must be validated, deficiencies must be addressed, and dirty data must be ‘cleaned’.
Thus it is essential to utilise a platform that uses a combination of machine and human intelligence to improve the speed of data set production. You need quality data to improve the accuracy level, so the collection and preparation are continuous. You need a human in the loop to audit the data in every batch so that errors can be quickly spotted and corrected.
Importance of a Machine Learning Pipeline
While many teams start by using the manual workflow, this approach is not suitable for collaboration. Most teams today use automation.
With an ML pipeline, you automate the machine learning workflow. One way to achieve this is by dividing the ML workflows into several independent, modular, and reusable parts you can pipeline together when needed to develop other models. This helps make the process of building models simpler and more efficient by removing redundant work.
Machine learning workflow
The importance of pipelining is more evident when you look at what is involved in a typical ML workflow.
In the conventional system design, all the tasks will emanate from one place. The same script will be used to extract data, clean, and prepare the data sets, create the model, and use it. Keeping all the assets in one place makes sense because ML models require less code.
But you will encounter some problems when you try to scale the single-source architecture.
If you want to deploy different versions of the same model, you need to run the entire workflow twice. If you need to expand the model portfolio, you need to copy and paste code from the beginning stages of the workflow. When you change a data source’s configuration or some of its parts, you’re creating a new version. Therefore, you must manually update all the scripts, leading to errors, aside from it being a time-consuming process.
If you have a pipelining architecture, you will only call the workflow parts that you need and store the results that you can reuse. Similarly, when you want to expand the model portfolio, you use the pieces of the beginning stages of the workflow. You do not need to replicate them, as you can pipeline them into the new model. Even if the data is in a central location, pipelining them together to create several versions only requires updating the original.
Automated ML pipelining lets you increase the iteration cycle and scale different models by choosing the elements you need – but a human in the loop, able to offer QA on data preparation, is also essential at every stage of development. They can review and approve the product to ensure its quality.