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Reading list

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

Understand Machine Learning In Production for Data Science

A
Ayush Last Updated : 30 Jun, 2021
4 min read

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

Introduction  

In Real-world, machine learning is not just about making your model and achieving high accuracy, this is only a small percentage of the total machine learning project.

MLOps are the must-know skills that machine learning engineers should know about. MLOps is a process of taking your model into the production system. A vast amount of work was done in achieving model accuracy in this era. But, if you’re building your product it’s not just about models.

In this article, we will be discussing MLOps, from Scoping a problem to deployment, Monitoring, and Maintaining the system.

What is MLOps?

The way I like to define MLOps is that it’s a process for developing an end-to-end product from scoping a problem to the deployment, monitoring, and maintaining the product.

Nowadays, many companies are integrating ML into their product and the rise of MLOps has increased drastically.

Here’s the pic that you can see that there is a small percent of ml code in the whole ml product.

 

understand mlops
What is MLOps? 

In the above figure, you can see that there is a small percentage of code contributed to the whole machine learning project. Below are the explanation of the important terms shown in the above picture:-

  1. Data Collection:- Collection of data through various sources.
  2. Data verification:– Validating our data by doing data analysis.
  3. Feature Extraction:- Extracting the features from text or image data.
  4. Serving Infrastructure:- Basic things needed for serving the model
  5. Monitoring:- After deployment, you monitor your model using some metrics.

MLOps Lifecycle

understand mlops lifecycle
MLOps Lifecycle 

1.) Scoping

Scoping or defining a project is one of the key parts of the MLOps Architecture. In this stage, you decide on which to work on in the particular project.

Eg:- Let’s you’re building a Speech recognition system, then In that case you will tend to work on some of the key metrics such as accuracy, latency, etc.

Eg:- Let’s say you’re working on an image recognition system, then In that case you will tend to more work on some of the key metrics like Image preprocessing, normalizing and etc.

The main problem that machine learning engineers fail because they don’t understand the problem statement and they haven’t scoped the problem. So I recommend spending some amount of time on this also with your team.

Tip:- Whenever you’re working on this stage, try to discuss the things with your team on giving priority to the metrics.

2.) Data

In this stage, you’ll define data and establish the baseline and you’ll work on labeling and organizing the data.

Eg:- Let’s say you’re working with audio data, for example, you will normalize the volume of the audio and do some more preparation of the data.

Eg:- Let’s say you’re working on image data, you’ll normalize your image, crop your image, resizing and etc.

The challenge you might face is that your data is in very bad condition, I recommend trying to label or organize the data manually.

Tip:- Before preparation Try to do the data analysis to get a better sense of the data, you’re working on.

3.) Modeling

In this stage, you’ll work on making your model, which includes Research and Algorithm and Hyperparameter tuning to make your model work.

Here, you select and train the model and perform some error analysis and try to improve the performance with the help of error analysis.

In this stage the challenge you might face challenges like Hyperparameter tuning and selecting the best algorithm.

Tip:- Whenever you’re struggling in selecting the best hyperparameter, I did recommend reading the paper and try with the hyperparameters that others have selected.

4.) Deployment

This is also the crucial stage in MLOps. You deploy your model to be used by the people, or you deploy your model in production.

After deployment, you monitor your system and you maintain your system.

Eg:- After deployment, let’s say you have deployed a credit card fraud detection system, then it might be that hackers change their strategy, so that system will fail, so you have to continuously monitor and maintain your system. In that case, you will retrain your model with other data.

Below I have listed one deployment pattern that if often used in the industry

Canary Deployment:– In this deployment pattern, you roll out a small percentage of traffic to your system, and then you monitor it, if all good then you go further or you come back again to tune your model.

Deployment is a very Iterative process, so don’t get offended in this case, try to ask some people who are experienced with respect to your problem.

5.) Monitoring

In MLOps, Monitoring is one of the important stages. You monitor your system, how well it is performing when the traffic is coming?

Eg:- Fraction of Non-Null Outputs from the model, may happen that your model is printing nothing.

Eg:- Fraction of Input Missing Values

You can set up some metrics or alerts like Input metrics, Output metrics, etc.

Suggestions on setting up monitoring metrics:-

  • Brainstorm the number of things with your team that can go wrong with your model.
  • Brainstorm a few metrics to detect the problems in your model.
  • It is okay to use many metrics initially but remove the ones that you do not find useful.

ML Model development is an iterative process, so is deployment. 

Endnotes 

Thanks for reading!

I hope that you’ll implement these concepts & strategies into your ML product Or Project.

Something not mentioned or want to share your thoughts? Feel free to comment below And I’ll get back to you.

About the Author  

Ayush Singh  

I am a 14-year-old learner and machine learning and deep learning practitioner, Working in the domain of Natural Language Processing, Generative Adversarial Networks, and Computer Vision.  Also, make videos on machine learning, deep learning, Gans on my youtube channel Newera.  I am also a competitive coder but still practicing all the techs and a passionate learner and educator.

You can connect me on Linkedin:- Ayush Singh

Your suggestions and doubts are welcomed here in the comment section. Thank you for reading my article!

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

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