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Short Guide Through Automated MLOps Pipelines

Today, many organizations use ML and AI primarily to understand customer behavior, increase productivity, and make better decisions. In short, companies want to turn big data into actionable and valuable insights. However, to capture the full value of ML, MLOps must be adopted. It’s a way to automate machine learning processes without manual interference. This article will show you what MLOps Pipelines are and guide you through the three levels of automation.


MLOps is a set of practices that increase the efficiency of workflows. The purpose of these policies and practices is related to:

● Facilitating communication

● Faster experimentation and model development

● Deploying models faster in the production environment

● Quality control

● Comprehensive traceability of data

What are the Benefits of Automated MLOps Pipelines?

The use of automated MLOps pipelines enables efficient and repeatable data processing and deploying, evaluation and training models. Additionally, MLOps pipelines ensure consistency and allow tracking of models and data.

Other benefits:

● More efficient communication in the team,

● A faster and easier process of implementing the model into production

● Better use of data

● Seamless integration

Automated MLOps Pipelines

Many organizations didn’t benefit from automating data pipelines as the traditional development, implementation, and continual improvement of ML were too complex. Therefore, the MLOps pipeline is needed to meet this challenge. An MLOps pipeline automates creating and maintaining AI and ML models.

What Tasks Can You Automate?

Depending on the stages of machine learning, different tasks may be subject to automation. At the data engineering stage, it will be data acquisition, validation, and processing. During the models’ development, you can automate their training, evaluation, and testing. Building, testing, and deploying a new model implementation can also be automated. The last stage, i.e., monitoring, is setting alerts based on predefined indicators[1]. 

The Process of MLOps Automation

The maturity of the ML model depends on the level of automation of data flows, code, and machine learning models. And the more mature the model, the faster it can be trained. Therefore, the main goal of MLOps engineering teams is to automate all stages of the ML workflow. We can distinguish three levels of MLOps automation:

  1. A manual process
  2. ML automation
  3. Automation of continuous integration and continuous delivery.

Below, you find a description of each level in detail.

LEVEL 0 – A MANUAL PROCESS: It is a process where each step is performed manually. Therefore, data analysis, preparation, validation, and model training and testing require a manual approach. Generally, this science-driven process is being adopted by companies just beginning to implement ML. Moreover, it will be sufficient when the models are rarely modified and trained. Hence, CD (continuous delivery) and CI (continuous integration) are not considered. This process doesn’t monitor the performance of the model. What’s more, it often needs to be refreshed with new data.


This level involves performing continuous model training in production through ML pipeline automation. So if you have new data, it will be extracted and transformed to train and validate the model. Level 1 involves the deployment of a whole training pipeline which runs automatically to support the trained model. What’s more, it includes the steps of data and model validation. Several companies may benefit from this process if they operate in a dynamic environment.

Check out: How To Automate QA Testing: An In-depth Guide


In the last stage, we introduce the CI / CD system for fast and reliable implementation of ML models into production. The main difference from the previous stage is that we now automatically build, test, and implement data, the ML model, and components of the ML training stream. There are six stages of the CI/CD automated ML pipeline:

  1. Development and experimentation. Data scientists come up with new models and experiments and generate source code.
  2. Pipeline continuous integration. The result of building and testing the source code is the pipeline components, i.e., packages, executables, and artefacts.
  3. Pipeline continuous delivery. The artefacts created in stage one are implemented into the target environment, leading to a new model version.
  4. Automated triggering. The pipeline is automatically executed based on a trigger, and the target model is sent to a model registry.
  5. Model continuous delivery. The consequence of this stage is the implemented model prediction service.
  6. Monitoring: The last step is collecting statistics on the model’s performance. Statistics are collected based on real-time data.

Take the Lead in Automated Machine Learning Pipelines

A wide range of businesses has successfully used automated MLOps pipelines. For example, HPE has implemented them for predictive analytics and Capital One for real-time car loan decisions. You can build your MLOps using DVC, Kubeflow, MLflow, or Seldom Core tools. If you want to find out more about MLOps and machine learning pipelines, see MLOps consulting for details.

[1] https://mlops-guide.github.io/MLOps/PipelineAutomation/

[2] https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning#mlops_level_2_cicd_pipeline_automation

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