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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

8 Reasons Why Analytics / Machine Learning Models Fail To Get Deployed

Tavish Srivastava
Tavish Srivastava Last Updated : 30 May, 2016
5 min read

Introduction

Don’t be a data scientist whose models fail to get deployed!

An epic example of model deployment failure is from Netflix Prize Competition. In a short story, it was an open competition. Participants had to build a collaborative filtering algorithm to predict user rating for films. The winners received grand prize of US$1,000,000. In the end, the complete model never got deployed.

Not just Netflix, such dramatic events occurs in most of the companies. Recently, I have been talking with C-Suite professionals of many leading analytics companies. The biggest concern I hear about is 50% of the predictive models don’t get implemented.

Would you want to build a model which doesn’t gets used in real world ? It’s like baking a cake which you’ve tasted and found wonderful but would never be eaten by anyone.

In this article, I have listed down all the possible reasons which you should keep in mind while building models. In my career, I’ve faced such situations many a times. Hence, I think my experience could help you in overcoming such situations.

Model Deployment failure in analytics / data science

 

8 Reasons For Model Deployments Failure

1. High amount of false positive : This might seem a bit technical. Here, it’s important to understand what is false positive?In a classification model, assume that we want to predict whether the customer is a responder (one who give answers) or a non-responder (one who doesn’t).

Imagine that you predict that a person X will be a responder but in reality he does not respond. Person X in this case is called a false positive. So how does this effect in real world ? I knew you would ask this question. 

Let’s take an example. For instance, you have given the responsibility to build a retention campaign for 1000 customers. Out of these 1000, 100 customers will actually attrite (leave). You create an amazing model which has a 4X lift on top decile (10 equal large subsections).

This means, out of your top 100 predicted customers, 40 customers will actually attrite. So, you recommend the business to target all these 100 customers with a fascinating shopping offer which can stop them from attriting. But, here’s the challenge.

The challenge is that for every dollar you spend on each of these customers, only $0.4 get used to stop attrition. Rest $0.6 just go to false positive customers who were really not in a mood of attrition. This calculation will some times make these models less likely to be implemented as a result of negative P&L (Profit & Loss). 

1

2. Low understanding of underlying models with business : Lately, there has been a rising requirement of using machine learning algorithms and more complex techniques for model building. In other words, companies are drifting away from using traditional models techniques.

Undoubtedly, using ML techniques lead to an incremental power of prediction, but the businesses are still not very receptive to such black box techniques. In my experience, this leads to a lot longer lead time for a predictive strategy to get implemented. And as most of the applications in business are highly dynamic, the model become more and more redundant with higher lead time.      BlackBoxQuestion

3. Not enough understanding of the business problem: Predictive models are good for resumes of both analyst and the business counterparts. However, that is not the purpose of the model you would build. In some cases, analyst run into creating model phase and try to cut down the time that should have been allotted to understanding the business problem.

businessacquisition03

 

4. Too complex models for implementation : Predictive power of models is the soul of these exercises. But in general, predictive power comes at a cost of complexity of models. We start bringing in bivariates and tri-variates to make models stronger, even when these variables make no sense as per business. Such models might be amazing in books, and hence, they just stay in these books and never see the actual light of real-world.

analytics (1)

 

5. Not addressing the root cause just trying to improve the effect of a process : Why do we make models? The most important reason is to find the drivers of a particular response. What are these drivers? Drivers are always the root cause of response rate. What will happen if you bring in all the effects as the input variable and these variables also come out as significant? It will hardly be of any use as you are not changing things that can really bring changes .

PPT graphic - RootSource: ThinkReliability

 

6. Training population significantly different from Scoring Population : In many cases, we end up creating models on a population which is significantly different from the actual population. For instance, if you are creating a campaign targeting population and you have no previous similar campaign. In such cases we start with the basic assumption that a population with high response rate might also have a high incremental response rate. But this assumption is rarely true and hence the model would be hardly used.

 

7. Unstable models : High performing models are often highly unstable and do not perform at par with time. In such cases, business might demand high frequency model revision. With higher lead time in model creation, business might start going back to intuition based strategy.

unstable-tower-of-blocks-225x300

8. Models dependent on highly dynamic variables : Dynamic variable are those variables which bring in the real prediction power to the model. However, you might have a culprit variables which might bring in such values which have never been seen in training window.

For instance, you might get number of working days as a significant variable to predict monthly sales of a branch. Let’s say this variable is highly predictive. But for our scoring window, we have months which have just 10-15 working days. If your training data does not have any such month, your model might not be capable of making this prediction accurately.

 

End Notes

I believe if we do understand these challenges, we can think of better ways of not getting entangled with such catches. Also, knowing them makes us see what business is really looking out for. I welcome you to append this list which will make our analysis more comprehensive and take the team a step ahead.

Did you like reading this article ?  Do share your experience / suggestions in the comments section below.

You can test your skills and knowledge. Check out Live Competitions and compete with best Data Scientists from all over the world.

Analytics Industryanalytics modelbusiness analytics industryfalse positiveimplementationmachine learning modelModel deploymentPredictive-power
Tavish Srivastava
Tavish Srivastava

Tavish Srivastava, co-founder and Chief Strategy Officer of Analytics Vidhya, is an IIT Madras graduate and a passionate data-science professional with 8+ years of diverse experience in markets including the US, India and Singapore, domains including Digital Acquisitions, Customer Servicing and Customer Management, and industry including Retail Banking, Credit Cards and Insurance. He is fascinated by the idea of artificial intelligence inspired by human intelligence and enjoys every discussion, theory or even movie related to this idea.

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Responses From Readers

Clear

Gianni
Gianni

Good job Tavish, thank you !

123

james
james

Hi Sounds like a complaint on a data scientist job. Sounds demoralizing LOL I do agreed with your points. and I hope analytics won't become a dot-net bubble. It is better to start small scale and bring small value (let's call it SMALL DATA analytics), than to do BIG DATA analytics which will end up in black hole. .Honestly, when I do my own mini projects and as I came across many algorithms, I start asking myself : what does the better algorithm provide in incremental value ? Maybe I end up more confused ! Worst, people who are non-believer of analytics will always disagree with people like us.

123

Ganesh
Ganesh

Nice article Tavish.

123

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