Machine Learning Pipeline Deployment and Architecture

Discover the Concept of a Machine Learning Pipeline.

The machine learning pipeline helps automate ML workflow and enables the sequence data to be transformed and correlated in a model to analyse and achieve outputs. ML pipeline is constructed to allow data flow from raw data format to valuable information. It provides a mechanism to build a multi-ML parallel pipeline system to examine the outcomes of different ML methods. The objective is to exercise control over the ML model. A well-planned pipeline helps to make the implementation more flexible. It is like having an overview of a code to pick the faults 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 Models

Use Cases for Machine Learning Pipeline Deployments

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

Machine Learning Pipeline Architecture

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

Pre-processing

Data preprocessing is a Data Mining technique that transfers raw data into an understandable format. Real-world data is usually incomplete, inconsistent, and lacks certain behaviours or trends, and it is 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 Preprocessing is the final dataset used for training the model and testing purposes.

Learning

A learning algorithm processes 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.

Evaluation

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.

Prediction

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

Benefits of Machine Learning for Decision-Making Processes

There are many benefits, some of them are:

Flexibility—Computation units are easy to replace. A part can be reworked for better implementation 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 can be scaled separately.

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

Why does it matter?

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

Timely Analysis And Assessment

It helps to understand customer behaviour by streamlining Customer Acquisition and Digital Marketing strategies.

Real-Time Predictions

ML algorithms are super fast. As a consequence, large data processing takes place rapidly. This, in turn, helps make Real-Time predictions that are very beneficial for businesses.

Transforming Industries

It has already commenced transforming industries with its expertise to provide valuable real-time insights.

How do we adopt Machine Learning Pipelines?

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

Financial services—Financial industries such as Banks and other businesses use ML technology to identify essential insights into data and prevent fraud. These insights identify customers with high-risk profiles or use Cyber Surveillance to give warning signs of fraud.
Government—Government agencies use Machine Learning, such as Public Safety, to mine multiple data sources for insights. For instance, analyzing sensor data helps identify processes to increase efficiency and save money.
Healthcare—In Healthcare, ML technologies help medical specialists analyze data and identify patterns, improving diagnosis and treatment.
Marketing and Sales - Website recommendation items use ML techniques to analyze the history of users based on previous purchases and promote other relevant things.
Oil and Gas—In Oil and Gas fields, ML helps find new energy sources, analyze minerals in the ground, and make operations 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 for ML Deployments

Azure ML pipeline helps to build, manage, and optimize its 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 are highlighted below:

Unattended runs - Planned steps to run in parallel or in an unattended manner. Pipelines help focus on other tasks while the process is being processed.
Heterogeneous computing—The Azure Machine Learning pipeline allows multiple pipelines coordinated with heterogeneous and scalable computing resources and storage locations. This allows using available compute resources by running individual pipeline steps on different compute targets.
Reusability - Allows the creation of pipeline templates for specific scenarios to trigger published pipelines from external systems.
Tracking and versioning - Automatically track data and result paths as iterated and manage scripts and data separately for increased productivity.
Modularity - Splitting the areas of concern and isolating variances allows the software to evolve with higher quality.
Collaboration - It allows Data Scientists to collaborate with the area of the ML design process while working on pipelines.

Kubeflow for Machine Learning Pipeline Deployments

Kubeflow Pipelines is a platform for deploying and building ML 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:

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

AWS Sagemkaer for ML Pipeline Deployments

AWS Sagemaker enables developers and data scientists to build, train, and deploy its 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:

Step: Create the notebook instance
Step: Prepare the data
Step: Train the model from the data
Step: Deploy the ML model
Step: Evaluate your ML model's performance

Best Practices for Machine Learning Pipeline Deployment

Be specific about the assumptions so that ROI can be planned. To regulate business believability at the production level, we need to understand: "How acceptable is 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 software development. It also requires research and a review of the scientific literature.

Collect High-Quality Training Data

The greatest fear for any Machine learning model is the scarcity of the quality and 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 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 step is to perform as many tests as possible and conduct 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.

Building Decentralized, AI, Extended Reality and Quantum Computing Solutions. Artificial Intelligence Services and Strategy Consulting

ML Pipeline Infrastructure

ML Infrastructure comprises the resources, processes, and tooling required to develop, operate, and train ML models. Every stage of its workflow is supported by ML infrastructure, which makes it easy for data scientists, engineers, and DevOps teams to manage processes and operate the models. It has various processes, like data collection and processing numerous operations on collected data to provide pre-calculated results and guidance for the next operations.

In most industries, infrastructure is insufficient for ML applications, and their infrastructure is the base of their model, on which ML Models are developed and deployed. Because models differ between projects, their infrastructure implementations also vary. ML Infrastructure

ML Pipeline Tools

The table below describes the machine learning pipeline tools and their usage in the respective steps for building the ML pipeline.

Steps For Building a 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.
Modelling 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.

Machine Learning Pipeline Deployments with MLOps and MdoelsOps

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