Datatron Blog

Stay Current with AI/ML

What is a Machine Learning Pipeline?

Today, we look forward to learning about an interesting and blossoming process of Artificial Intelligence, i.e., Machine Learning Pipelines.

Before we start, let’s try to contemplate the terms for better understanding.

Machine Learning’ – this is the red-hot iron about which we all have read dozens of articles, compositions, and more. The inference has always been the same, ‘machine learning is an approach to data analysis that involves building and adapting models, which allow programs to learn through experience.’

Pipeline’ – Have you ever visited a manufacturing plant and seen the assembly lines there where a component progresses through the assembly lines and while passing it is processed on, at different stages simultaneously?

Similarly in computer architecture, Pipelining is a technique where commands that are manifold get intersected during execution. The computer pipeline is split into stages. Each stage completes a certain part of a command in parallel. The stages are interconnected to form a pipe in such a way that instructions enter at one end, progress through the stages, and exit at the other end.

Now we will try to integrate the above terms and dive a bit deeper into our today’s subject.

What is a Machine Learning Pipeline?

Software developers often talk about ‘build pipelines’ to narrate how software is taken from source code to deployment. Just like developers have a pipeline for code, data scientists have a pipeline for data as it flows through their machine learning solutions. But the term ‘pipeline’ is misleading as it means a one-way flow of data. Generally, a machine learning pipeline encapsulates and automates the multiple Machine Learning processes such as performing data extractions and wrangling, creating training models, model validation, and model deployment for predictions.

As we know MLOps refers to the practice that aims to make machine learning in production efficient and seamless, the Machine Learning pipelines act as the catalyst to this process as it automates the frequent workflow, which in turn accelerates the operation and deployment of the machine learning models. It is easier to fix the bugs in a Machine Learning pipeline that help to create a smoother flow of quality data and reduce the overall cost of development. A well-organized pipeline makes the model implementation more flexible and productive.

Steps involved in a Machine Learning Pipeline

Pipelines are essentially development steps in building and automating the desired output from a program. A machine learning pipeline starts with ingesting new training data and ends with receiving a response on how the recently trained model is performing. The pipeline includes a variety of steps including data processing, model training, and model validation, as well as model deployment and maintenance. One can imagine the fact that going through these steps manually is cumbersome and a very error-prone process.

The machine learning pipeline consists of the following stages:

  1. Data preprocessing (exploration and governance)
  2. Training models
  3. Model Optimization
  4. Model Evaluation
  5. Model Deployment

Depending on the specific cases, the final machine learning pipeline may seem to be different. For example, we can train, evaluate, and deploy several models in one pipeline but there are common components that are similar in most of the machine learning pipelines.

Pipeline in Machine Learning for Business Challenges

Following are some of the advantages of using ML pipelines:

  • Automates the repetitive processes.
  • Easy error identification and fixing.
  • Helps to standardize the code.
  • Helpful in iterative hyperparameter tuning and cross-validation evaluation.

But what do the above-stated interests offer the businesses? Let us try to answer this question in the section below.

1) Develop more accurate ML models

Using the automated ML pipelines, a smooth flow of quality data can be created that helps the data engineers to tune the ML models that generate more precise outputs.

2) Reach the market expeditiously

Machine Learning pipeline enhances the process of training and fitting ML models so that one can operationalize and deploy them faster, tap into predictions earlier, and get the product to market sooner.

3) Ameliorated business forecasting

ML pipeline helps to build a better model and allows the businesses to strengthen the business forecasting abilities. Improved sales and demand forecasting allow them to stay ahead of the trends, provide a better user experience, and increase profits.

4) Reduces risk

Regular improvements in the ML models through the ML pipeline help to spot risk and opportunities earlier, analyze possibilities more thoroughly, and improve the business strategies.

Summing up

Real-life ML caseloads customarily require processes way beyond training and prediction. Data often demands to be processed, sometimes in more than one step. Thus, data scientists must train and deploy not just one algorithm but rather a series of algorithms that synergize to deliver robust predictions from raw data. So, after a glance at the composition above, we can infer that Pipelines in Machine Learning can assist the MLOps engineer to deploy the ML models efficiently and effectively for highly accurate outputs.

Datatron works hard to enable businesses to deploy ML models efficiently so that their customers can focus on tackling the organization’s most pressing problems with data-driven insights and decisions.

Follow us on Twitter and LinkedIn.

Thanks for reading!


Datatron 3.0 Product Release – Enterprise Feature Enhancements

Streamlined features that improve operational workflows, enforce enterprise-grade security, and simplify troubleshooting.

Get Whitepaper


Datatron 3.0 Product Release – Simplified Kubernetes Management

Eliminate the complexities of Kubernetes management and deploy new virtual private cloud environments in just a few clicks.

Get Whitepaper


Datatron 3.0 Product Release – JupyterHub Integration

Datatron continues to lead the way with simplifying data scientist workflows and delivering value from AI/ML with the new JupyterHub integration as part of the “Datatron 3.0” product release.

Get Whitepaper


Success Story: Global Bank Monitors 1,000’s of Models On Datatron

A top global bank was looking for an AI Governance platform and discovered so much more. With Datatron, executives can now easily monitor the “Health” of thousands of models, data scientists decreased the time required to identify issues with models and uncover the root cause by 65%, and each BU decreased their audit reporting time by 65%.

Get Whitepaper


Success Story: Domino’s 10x Model Deployment Velocity

Domino’s was looking for an AI Governance platform and discovered so much more. With Datatron, Domino’s accelerated model deployment 10x, and achieved 80% more risk-free model deployments, all while giving executives a global view of models and helping them to understand the KPI metrics achieved to increase ROI.

Get Whitepaper


5 Reasons Your AI/ML Models are Stuck in the Lab

AI/ML Executive need more ROI from AI/ML? Data Scientist want to get more models into production? ML DevOps Engineer/IT want an easier way to manage multiple models. Learn how enterprises with mature AI/ML programs overcome obstacles to operationalize more models with greater ease and less manpower.

Get Whitepaper