Machine Learning Pipeline Architecture, Best Practices - XenonStack

What is a Machine Learning Pipeline?

In Machine Learning (ML), a pipeline 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 the outcomes of different ML methods.With Machine Learning Enterprises can

  • Facilitate Real-Time Business Decision making
  • Improve the performance of predictive maintenance
  • Fraud Detection
  • Building Recommendation Systems

How Machine Learning Pipeline(ML) Works?

A pipeline consists of several stages. Each stage of a pipeline 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 such as Pre-processing, Learning, Evaluation, and Prediction.

Pre-processing – 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, and 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 final dataset used for training of the model as well as testing purpose.

Learning – A learning algorithm is a method used to process understandable data to extract patterns appropriate for application in a new situation. In particular, the aim is to utilize a system to 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.

Evaluation – To estimate the performance of the Machine Learning model, 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.

Prediction – The performance of the model to determine the outcomes on the test data set not used for any training or cross-validation activities.


Benefits of Machine Learning Pipeline

Constructing pipelines provides many advantages. Some of them are –

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

Why Machine Learning Pipeline Matters?

As machines begin to learn through algorithms, it will help companies to 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 made possible through Fast Processing – ML algorithms are super fast, as a consequence of that Data Processing from multiple sources 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.

MLOps is the communication between data scientists and operations teams focussed on automation in ML pipelines and get more precious insights in production systems. It provides reproducibility, visibility, and the computing resources to test, train, and deploy AI algorithms

From the Article, MLOps Platform – Productionizing Machine Learning Models


How to Adopt a Machine Learning Pipeline?

Nowadays, most of the industries working with massive amounts of data have understood the value of Machine Learning technology. By gaining insights from this data, companies work more efficiently.

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.

Government – Government agencies such as Public Safety uses Machine Learning to mine multiple sources of data for insights. For instance, by analyzing sensor data helps to identify processes to increase efficiency and save money.

Healthcare – In Healthcare, ML technologies help medical specialists to analyze data and identify patterns improving diagnosis and treatment.

Marketing and Sales – Websites recommendations item use ML techniques to analyze buying the history of users based on previous purchases and promotes other relevant things.

Oil and Gas – In Oil and Gas fields, ML helps to find new energy sources, analyze minerals in the ground, etc. to make it more efficient and cost-effective.


Best Practises of Machine Learning Pipeline

Be specific about the assumptions so that ROI can be planned – At the production level to regulate business believability, 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” of the decided solution – The research is the fundamental aspect of any software development, in fact, a Machine Learning process is not different from 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-process and Enhance the data – It is like “Tree will grow as much high as the roots are in-depth”. Pre-processing reduces the vulnerability of the model and for enhancing the model, Feature Engineering used which includes Feature Generation, Feature Selection, Feature Reduction, and Feature Extraction.

Do the number of experiments measures – After all the above steps the data will be ready and features available. The next is to do the tests as much as possible and do the proper evaluation so that a better result to be obtained.

Purify the finalized pipeline – Till now there will be a winner pipeline but 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 the satisfaction with the results.

Tools for Building Machine Learning Pipeline

 

Steps For Building Machine Learning PipelineTools Which Can be Used
Obtaining the DataManaging the Database – PostgreSQL, MongoDB, DynamoDB, MySQL.

Storage which should be distributed – Hadoop, Apache Spark/ Apache Flink.

Scrubbing / Cleaning the DataThe language which can be used for scripting – 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 trendsPython, R, Matlab, and Weka.
Modelling the data to do the predictionsMachine Learning algorithms – Supervised, Unsupervised, Reinforcement, Semi-Supervised and Semi-unsupervised learning.

Some important libraries – Python (Scikit learn) / R (CARET)

Interpreting the resultData Visualization Tools – ggplot, Seaborn, D3.JS, Matplotlib, Tableau.

A Holistic Strategy

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


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