How To Manage Machine Learning Pipeline

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:

• Data Collection: In this step, raw data is retrieved, collected, and merged to form a framework that aids in collecting more data from multiple sources.
• Data Cleaning: The collected data is then screened to eliminate missing values, duplicates, and other errors that could lead to incorrect software prediction.
• Data Pre-Processing: This step is also known as feature extraction. It's the transformation of data to create features for the machine learning model. The pipeline learns from the created features.
• Data Splitting: The processed data is split into training and validation subsets in this step. The training set is used to train the ML model, while the validated subset tests how well the software works.
• Model Validation: Model validation involves testing the data to validate the Machine learning software. Experts test the pipeline architecture to see how accurately it functions in this step.
• Visualization: Once the machine learning pipeline is validated, it can perform predictions for a wide array of users.

Here're other ways of managing a machine learning pipeline:

• Scheduling

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. 

• Synching

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.

• Clean-Up

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.

• Monitoring

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.