When it was introduced, machine learning (ML) was just a science fiction concept. However, through advanced technology and the continuous demand for data to run operations, machine learning is currently a basic need for running a successful business or company. Think of any business today; both small and large can benefit from machine learning. For example, supply chain management companies are using ML to ensure merchandise reaches retailers in time. Similarly, lawyers use the software to predict the likelihood of a successful lawsuit. They can choose the correct cause of action to benefit the case using the prediction information.
Due to this widespread use of machine learning, there's a need to ensure that the software is reliable and efficient. This has led to the introduction of machine learning pipelines. This article will help you understand what machine learning pipelines are and how to manage them for optimum benefits. You can visit cnvrg.io to learn more about machine learning and other artificial intelligence technologies.
What Is Machine Learning Pipeline?
Machine learning pipeline is an end-to-end program that is used to automate the workflow of machine learning (ML) through the input, transformation, correlation, and output of data from a machine learning model. Machine learning pipeline helps to oversee the operation of ML software. You have an overview of the ML system, its operations, and outcomes through the pipeline. As such, you can identify parts of the software that need repair and improvement and repair them without having to dismantle and disrupt the entire system.
The first step to managing a machine learning pipeline is understanding its composition and how it works. Different pipelines will adopt different structures depending on the ML software design, learning libraries, and processor requirements like storage and memory collectively known as runtime environments. However, a basic machine learning system consists of:
Here're other ways of managing a machine learning pipeline:
Scheduling ensures that the pipeline learns and frequently updates to give reliable, up-to-date predictions. Different parts of the machine learning software can be scheduled independently through the pipeline. Alternatively, you can schedule the entire software at once.
Data Quality Testing
Carrying out frequent data quality checks helps ensure that the data fed into the pipeline is correct and of quality. It, therefore, makes the ML model dependable with its predictions.
Since the entire system is made to ensure data is collected and transformed to make meaningful output, there's a need to make sure the framework components can communicate with each other. This is achieved through synching. When the parts of the pipeline are in sync, they can communicate when and how a given component should receive data and where to send the data.
System Health Checks
If you aren't checking on the system health of the pipeline, you’re barely managing it. Checking on the health helps you establish if the software is performing as it should. You can also determine if it's delivering value to the users. In case it's not, you should have a cleanup schedule that will be discussed in the next point.
Since the software learns with every task, it consistently grows and changes. This change creates a pool of components that should be cleaned up while others update into the software, therefore necessitating a cleanup.
A cleanup schedule helps to free up space in the pipeline. It also comes in handy to pinpoint issues in the framework that need maintenance. When possible, defects are identified early, maintenance is done, which helps you save up on time that would have otherwise been wasted on downtime.
You can barely perform the above-discussed management routines without monitoring your machine learning pipelines. Wholesomely, monitoring helps to ensure the pipeline doesn't stagnate or deteriorate in its functioning. If the system does any of that, prompt actions are taken to ensure it gets back to its feet. Other than that, monitoring helps you understand the data and how it keeps evolving. With that, you can better understand the user or specifically your target customers if you're in business.
To Sum It Up
If you want to incorporate machine learning software in your business, you must familiarize yourself with the pipeline framework. Understanding how it works and practicing other management routines like scheduling, data testing, synching, cleaning up, and monitoring will help you effectively manage the system.