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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Applications of EdgeML

Drishti
Drishti Last Updated : 08 Aug, 2022
7 min read

This article was published as a part of the Data Science Blogathon.

Edge computing examples                                                   Source: https://bit.ly/3cIGhUt

Introduction

EdgeML is a technology that allows Smart Devices to process data locally (through local servers or at the device level), leveraging machine and deep learning algorithms, decreasing reliance on Cloud networks. The term edge refers to the processing at the device- or local-level (and closest to the components collecting the data) by deep- and machine-learning algorithms.

Edge ML is groundbreaking. It addresses security concerns about storing personal user information in the Cloud and decreases the burden on Cloud networks by processing data locally. It also helps in real-time data processing, which is currently unachievable with typical cloud-operated smart devices but is important for technologies like driverless vehicles and medical equipment. In this article, we’ll look at some machine learning applications at the edge, so let’s jump in.

Applications of EdgeML

1. Healthcare: Chronic diseases can be tracked with health monitors and other wearable devices. In the case of a medical emergency, there’s a dire need to get quick access to medical history. The results could be fatal if these devices rely on transferring data to the cloud before making decisions. To circumvent this, EdgeML assists in the instant and timely evaluation of complex data that necessitates immediate attention by physicians to improve patient care and eliminate other potential health-related risks. In addition, caretakers can be notified in such a scenario.

An illustration of AI/ML at edge in an IoT empowered healthcare environment. Source: https://bit.ly/3OK1NFE

2. Virtual assistant: Voice-enabled gadgets do not constantly capture audio and send it to the cloud to ascertain if they are being instructed. Today’s voice interfaces often employ keyword or “wake-word” detection. EdgeML can help to quickly process microphone signals while the rest of the system stays idle. This power-efficient approach is especially crucial for extending usage time in portable battery-powered devices like smartphones and wearables.

Furthermore, the EdgeML technology can help separate the user’s voice from other sounds in the environment. For example, beamforming techniques process audio from multiple microphones in the device to focus listening in the direction the user is speaking from – like a virtual directional microphone.

A full guide on how to change the Alexa wake word for Amazon Echo devicesSource: https://bit.ly/3zI4FPo

3. Monitoring Safety in Oil and Gas facilities: Most oil and gas-related facilities are located remotely with poor connectivity. Locating sensors and edgeML at or near such systems allows for improved connectivity and constant monitoring. EdgeML can also analyze problems with equipment in real-time, aiding in predictive maintenance. The sensors can monitor humidity, temperature, and energy generated by all equipment, including electric vehicles and wind farm systems, and gathered data can be further analyzed using EdgeML. The resulting output could be to manage the energy grid to help with cost reduction and efficient energy creation.

cloud based video solutions for oil well monitoring

Source: https://bit.ly/3Jekgcs

4. Detecting Network Intrusions: Due to the significant increase in potential vulnerabilities or attack channels within a network, cyber security is a major concern for many enterprises. Deploying ML techniques at the network edge suggests that network security software and devices can constantly increase. ML techniques can be used to identify threats with continuous improvement by simulating different kinds of cyber attacks. In critical situations,  the time saved by eliminating cloud servers could be priceless.

Network Intrusion - Detection and PreventionSource: https://bit.ly/3PLEzjO

5. IoT device Activity Detection: With the rapid growth of the Internet of Things and connected devices, there has been an increase in interest in health and fitness apps and wearables. These apps typically get information from the gyroscopes and accelerometers found in many current smartphones. However, as the amount of personal data collected and communicated by these apps grows, so do worries about personal privacy and secure data transfers. Machine learning on edge devices, like smartphones, enables secure models to be learned directly on the devices, eliminating the need to send data to the cloud or externalize it.

Living on the Edge: What is edge computing and why do we need it? - WeAreBrain BlogSource: https://bit.ly/3z9tHoM

6. Prediction of Product Ratings: ML algorithms are heavily used in areas like consumer sentiment analysis to predict product ratings based on customer-written reviews. The text data from the reviews is examined as a word sequence before edge machine learning identifies relevant word sequences and provides insights. Any e-commerce outlet trying to capitalize on a consumer base to predict product ratings can leverage the obtained insights.

Written in the Stars: Implementing a Product Rating SystemSource: https://bit.ly/3oB6l6K

7. Autonomous Vehicles: Any pedestrian who crosses in front of an autonomous vehicle must be stopped timely to avoid any unfortunate instance. Making this decision based on the remote server is unreliable and unfair. Furthermore, vehicles that use edge technology can interact more efficiently since they can communicate with one another first, rather than sending data about accidents, weather, traffic, or diversions to a remote server.

Source: https://bit.ly/3OHJQaQ

8. Environmental and Wildlife Management: EdgeML can be leveraged to predict and optimize the selection of tree species for reforestation purposes using cartographic data. The best species is then inferred based on variables like altitude, exposure, soil type, and moisture; hence automatic reforestation efforts could be made.

Perspectives in machine learning for wildlife conservation | Nature CommunicationsSource: https://go.nature.com/3S3GuSB

9. Agriculture: The data related to ambient temperature, soil moisture level, and other variables can be gathered with the help of sensors. Further, this sensor data can be analyzed with the help of EdgeML solutions to analyze the nutrient density and water consumption to optimize and increase harvesting.

Agriculture Use Cases & Machine Learning Applications - Data Analytics                                      Source: https://bit.ly/3JtbxDz

10. Construction: EdgeML can be used in the construction sector. The data from the safety equipment, cameras, sensors, etc. It can be gathered and analyzed to increase the worker’s safety. Furthermore, it provides an overview of workplace safety conditions and ensures that workers follow safety regulations.

6 Amazing Applications of Artificial Intelligence in Smart ConstructionSource: https://bit.ly/3vqdgDz

11. Manufacturing: Environmental sensors, machine learning, and real-time analytics can monitor industrial processes to improve product quality and detect production mistakes. It also provides information on the available components and how long they will last, helping the producer make more accurate and rapid operational and facility decisions.

Groundbreaking benefits of Machine Learning for Manufacturing Source: https://bit.ly/3oGa5UK

12. Monitoring the health of the components: In various sectors like the energy, industrial, residential, healthcare, etc. EdgeML, in conjunction with other sensors, can be used to monitor the health of components and alert specialists when a repair is required. Hence it can help delay or avoid machine breakdown/meltdown situations and increase the lifetime of the machine.

3 Reasons Machine Monitoring is A Priority - IndMacDig | Industrial Machinery DigestSource: https://bit.ly/3bcoAfU

13. Advertising: Targeted marketing and information for retail enterprises rely on critical aspects defined in on-field equipment, like demographic data. Considering this, EdgeML can help to protect user privacy. Rather than sending unprotected data to the cloud, it may encrypt the data and keep the source.

Is Momentum Shifting Toward a Ban on Behavioral Advertising? – The MarkupSource: https://bit.ly/3PLvoA7

14. Developing Edge ML-based systems in hospitals and assisted living facilities: Leveraging EdgeML in conjunction with the data gathered from wearable and environmental sensors can aid in monitoring a patient’s heart rate, glucose levels, and other vitals. This has the potential to save lives because the data is analyzed locally at the edge, and staff would be notified in real-time when a speedy response is required to save lives.

Role of Machine Learning and AI in Healthcare SectorSource: https://bit.ly/3OCUfVr

Conclusion

In this blog post, we learned that EdgeML can be used for the following purposes:

1. EdgeML can be used in the health sector to provide quick access to a patient’s medical history and assist in the instant and timely evaluation of complex data, hence helping in avoiding any casualties. Furthermore, it can help develop Edge ML-based systems in hospitals and assisted living facilities.

2. EdgeML can be used in virtual assistants and sound systems. It can specifically help in wake word detection and processing and can aid in separating the user’s voice from other sounds in the environment.

3. EdgeML can also help monitor the subject’s safety in various domains like oil and gas, driverless vehicles, construction site, etc. Also, it can be employed to ensure the user’s safety from cyber network intrusions.

4. EdgeML can be leveraged by e-commerce outlets trying to capitalize on a consumer base to predict product ratings. Also, it can be leveraged for targeted marketing.

5. EdgeML can be used in industries to make more accurate and rapid operational and facility decisions. Furthermore, it can be leveraged to monitor the health of the components.

6. EdgeML can be leveraged for environmental, agricultural, and wildlife management.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Drishti
Drishti

I'm a Researcher who works primarily on various Acoustic DL, NLP, and RL tasks. Here, my writing predominantly revolves around topics related to Acoustic DL, NLP, and RL, as well as new emerging technologies. In addition to all of this, I also contribute to open-source projects @Hugging Face.
For work-related queries please contact: [email protected]

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