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Understanding Machine Learning Pipeline Deployment and Architecture

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What is a Machine Learning Pipeline?

Machine learning pipeline helps to automate ML Workflow and enable the sequence data to be transformed and correlated together in a model to analyzed and achieve outputs. ML pipeline is constructed to allow the flow of data from raw data format to some valuable information. It provides a mechanism to build a Multi-ML parallel pipeline system to examine different ML methods' outcomes. The Objective of the Machine learning pipeline is to exercise control over the ML model. A well-planned pipeline helps to makes the implementation more flexible. It is like having an overview of a code to pick the fault and replace them with the correct code.
The common challenges Organizations face while productionizing the Machine Learning model into active business gains. Source - MLOps Platform – Productionizing Machine Learning Models

With Machine Learning, Enterprises can:
  • Facilitate Real-Time Business Decision making
  • Improve the performance of predictive maintenance
  • Fraud Detection
  • Building Recommendation Systems

How does a Machine Learning Pipeline work?

A pipeline consists of several stages. Each stage of a pipeline is fed with the data processed from its preceding stage, i.e., the output of a processing unit supplied as an input to the next step. Machine Learning Pipeline consists of four main stages as Pre-processing, Learning, Evaluation, and Prediction.


Data preprocessing is a Data Mining technique that involves transferring raw data into an understandable format. Real-world data is usually incomplete, inconsistent, and lacks certain behaviors or trends, most likely to contain many inaccuracies. The process of getting usable data for a Machine Learning algorithm follows steps such as Feature Extraction and Scaling, Feature Selection, Dimensionality reduction, and sampling. The product of Data Pre-processing is the final dataset used for training the model and testing purposes.


A learning algorithm is used to process understandable data to extract patterns appropriate for application in a new situation. In particular, the aim is to utilize a system for a specific input-output transformation task. For this, choose the best-performing model from a set of models produced by different hyperparameter settings, metrics, and cross-validation techniques.


To Evaluate the Machine Learning model's performance, fit a model to the training data, and predict the labels of the test set. Further, count the number of wrong predictions on the test dataset to compute the model’s prediction accuracy.


The model's performance to determine the outcomes of the test data set was not used for any training or cross-validation activities.

What are the Benefits of Machine Learning Pipeline?

Constructing ML Pipelines provides many advantages. Some of them are:
  • Flexibility - Computation units are easy to replace. For better implementation, it is possible to rework that part without changing the rest of the system.
  • Extensibility - When the system is partitioned into pieces, it is easy to create new functionality.
  • Scalability - Each part of the computation is presented via a standard interface. If any part has an issue, it is possible to scale that component separately.

Many different approaches are possible when using ML to recognize patterns in data. Source -Machine learning workflow

Why ML Pipeline Matters?

As machines begin to learn through algorithms, it will help companies interpret uncovered patterns to make better decisions.

Timely Analysis And Assessment

ML helps to understand customer behavior by streamlining Customer Acquisition and Digital Marketing strategies.

Real-Time Predictions

ML algorithms are super fast. As a consequence that Large Data Processing takes place rapidly. This, in turn, helps in making Real-Time predictions very beneficial for businesses.

Transforming Industries

Machine learning has already commenced transforming industries with its expertise to provide valuable insights in Real-Time.

How to Adopt a Machine Learning Pipeline?

Nowadays, most industries working with massive amounts of data have understood the value of Machine Learning Pipeline technology. By gaining insights from this data, companies work more efficiently.
  1. Financial services - Financial industries such as Banks and other businesses uses ML technology to identify essential insights into data and prevention of fraud. These insights identify customers with high-risk profiles or use Cyber Surveillance to give warning signs of fraud.
  2. Government - Government agencies use Machine Learning such as Public Safety to mine multiple data sources for insights. For instance, analyzing sensor data helps to identify processes to increase efficiency and save money.
  3. Healthcare - In Healthcare, ML technologies help medical specialists to analyze data and identify patterns improving diagnosis and treatment.
  4. Marketing and Sales - Websites recommendation items use ML techniques to analyze buying the history of users based on previous purchases and promote other relevant things.
  5. Oil and Gas - In Oil and Gas fields, ML helps find new energy sources, analyze minerals in the ground, etc., to make it more efficient and cost-effective.

Transforming the way businesses work by unlocking the power of Artificial Intelligence. Source: AI Transformation Road Map

Azure Machine Learning Pipelines

Azure ML pipeline helps to build, manage, and optimize machine learning workflows. It is an independently deployable workflow of a complete ML task. It is so simple to use and provides various other pipelines, each with a unique purpose. The key benefits of Azure Machine learning Pipelines are highlighted below:
  1. Unattended runs - Planned steps to run in parallel or an unattended manner. Pipelines help to focus on other tasks while the process is in processing.
  2. Heterogeneous compute - Azure Machine learning pipeline allows using multiple pipelines coordinated with heterogeneous and scalable compute resources and storage locations. To use available compute resources by running individual pipeline steps on different compute targets.
  3. Reusability - Allow creating pipeline templates for specific scenarios to trigger published pipelines from external systems.
  4. Tracking and versioning - Automatically track data and result paths as iterated and manage scripts and data separately for increased productivity.
  5. Modularity - Splitting the areas of concern and isolating variances allows the software to evolve with higher quality.
  6. Collaboration - Azure Machine learning pipeline allow Data Scientists to collaborate with the area of the ML design process while working on pipelines.

Kubeflow Pipelines

Kubeflow Pipelines is a platform for deploying and building Machine learning Workflow based on Docker containers. Its primary goals are End-to-end orchestration, Easy experimentation, and Easy re-use of components and pipelines to quickly create end-to-end solutions.

Features of Kubeflow Pipelines:

  1. UI for managing and tracking experiments
  2. Engine for scheduling multiple-step Machine learning workflow.
  3. An SDK for defining pipelines and components.
  4. Notebooks for interacting with the system with SDK.
  5. Enabling the orchestration of machine learning pipelines.

Machine learning Pipeline AWS

Machine learning pipeline AWS services enable developers and data scientists to build, train, and deploy Machine Learning models at scale. Which includes processes such as data preprocessing, feature engineering, data extraction, model training and evaluation, and model deployment. Below given are the steps involved in the whole process:

  1. Step: Create the notebook instance
  2. Step: Prepare the data
  3. Step: Train the model from the data
  4. Step: Deploy the ML model
  5. Step: Evaluate your ML model's performance

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Best Practises of ML Pipeline

Be specific about the assumptions so that ROI can be planned. To regulate business believability at the production level, there is a need to understand: "How acceptable the algorithm so that it can deliver the Return on Investment?”

Research about the "State of the Art"

Research is the fundamental aspect of any software development. In fact, a Machine Learning process is not different from the software development process. It also requires research and needs to review the scientific literature.

Collect High-Quality Training Data

The greatest fear for any Machine learning model is the scarcity of the quality and the quantity of the training data. Too boisterous data will inevitably affect the results, and the low amount of data will not be sufficient for the model.

Pre-processing and Enhancing the data

It is like, "Tree will grow as much high as the roots are in-depth." Pre-processing reduces the model's vulnerability and enhances the model, Feature Engineering used, which includes Feature Generation, Feature Selection, Feature Reduction, and Feature Extraction.

Experiment Measures

After all of the above steps, the data will be ready and available. The next is to do the tests as much as possible and do the proper evaluation to obtain a better result.

Purifying Finalized Pipeline

Till now, there will be a winner pipeline moreover the task is not finished yet. There are some issues which should be considered:

  • Handle the overfitting caused by the training set.
  • Fine-tuning the Hyperparameters of the pipeline.
  • To obtain satisfaction with the results.
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Machine learning Pipeline Infrastructure

ML Infrastructure consists of the resources, processes, and tooling required to develop, operate, and train ML models. Every stage of Machine learning workflow is supported by ML infrastructure. Therefore, makes it easy for data scientists, engineers, and DevOps teams to manage processes and operate the models. Machine learning Pipelines have various processes like data collecting and processing numerous operations on collected data to provide pre-calculated results and guidance for the next operations but this works in most industries, but it is insufficient when it comes to ML applications. Machine learning infrastructure is the base of machine learning models. On which ML Models are developed and deployed. Because models differ between projects, machine learning infrastructure implementations also vary. ML Infrastructure

ML Pipeline Tools

Given below table describe the Machine learning pipeline tools with their usage in respective steps for building ML Pipeline.
Steps For Building Machine Learning Pipeline Tools Which Can be Used
Obtaining the Data Managing the Database - PostgreSQL, MongoDB, DynamoDB, MySQL. Distributed Storage - Apache Hadoop, Apache Spark/Apache Flink.
Scrubbing / Cleaning the Data Scripting Language - SAS, Python, and R. Processing in a Distributed manner - MapReduce/ Spark, Hadoop. Data Wrangling Tools - R, Python Pandas
Exploring / Visualizing the Data to find the patterns and trends Python, R, Matlab, and Weka.
Modeling the data to make the predictions Machine Learning algorithms - Supervised, Unsupervised, Reinforcement, Semi-Supervised, and Semi-unsupervised learning. Important libraries - Python (Scikit learn) / R (CARET)
Interpreting the result Data Visualization Tools - ggplot, Seaborn, D3.JS, Matplotlib, Tableau.

A Holistic Strategy

But the main focus of the Machine Learning Pipeline is to help businesses to enhance their overall functioning, productivity, Repeatability, Versioning, tracking, and Decision-Making process.

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