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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

MLOps – the Why and the What

m
manoabz Last Updated : 26 Nov, 2020
4 min read

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

Introduction

This article outlines the motivation behind MLOps, its relation to DevOps, and the different components that comprise an MLOps framework. The article is arranged as follows:

  1. MLOps motivation
  2. MLOps challenges similar to DevOps
  3. MLOps challenges different from DevOps
  4. MLOps components

 

1. MLOps Motivation

Machine Learning (ML) models built by data scientists represent a small fraction of the components that comprise an enterprise production deployment workflow, as illustrated in Fig. 1 [1].​​​​​​​ To operationalize ML models, data scientists are required to work closely with multiple other teams such as business, engineering, and operations. This represents organizational challenges in terms of communication, collaboration, and coordination. The goal of MLOps is to streamline such challenges with well-established practices. Additionally, MLOps brings about agility and speed that is a cornerstone in today’s digital world.

MLOps Motivation

Fig. 1: Only a small fraction of real-world ML systems are composed of the ML code, as shown by the small box in the middle. The required surrounding infrastructure is vast and complex.

2. MLOps challenges similar to DevOps

The challenges of ML model operationalization have a lot in common with software production, where DevOps has proven itself. Therefore, adopting the best practices from DevOps is a prudent approach to help data scientists overcome challenges common to software production. For example, the use of agile methodology promoted by DevOps in contrast to waterfall methodology is an efficiency boost. Additional DevOps practices used in MLOps are listed in Table 1.

Table 1: MLOps leveraging DevOps

Table 1

3. MLOps challenges different from DevOps

According to industry parlance, MLOps is DevOps for ML. While it is true to some extent, there are challenges typical to ML that need to be addressed by MLOps platforms.

An example of such a challenge is the role of data. ​​​​​​​​​​​​​​​​​In traditional software engineering (i.e., software 1.0), developers write logic/rules (as code) that are well defined in the program space, as demonstrated in Fig. 2 [2]. However, in machine learning (i.e., software 2.0), data scientists write code
that defines how to use parameters to solve a business problem. The parameter values are found using data (with techniques such as gradient descent). These values may change with different versions of the data, thereby changing the code behavior. In other words, data plays an equally important role as the written code in defining the output. And both can change, independent of each other. This adds a layer of data complexity in addition to the model code as an intrinsic part of the software that needs to be defined and tracked. ​​​​

MLOps

Fig. 2: Software 1.0 vs Software 2.0

 

The various challenges that need to be taken care of by the MLOps platform are listed in Table 2.

Table 2: ML specific challenges

Table 2

 

4. MLOps Components

​​​​​​​With the background in MLOps, its similarity to, and the difference from DevOps, the following describes different components that comprise an MLOps framework, as shown in Fig. 3. The underlying theme of the workflow is an agile methodology, as indicated in Section 2.

Components

Fig. 3: MLOps Framework

  1. Use case discovery: This stage involves collaboration between business and data scientists to define a business problem and translate that into a problem statement and objectives solvable by ML with associated relevant KPIs (Key Performance Indicator).
  2. ​​​​Data Engineering: This stage involves collaboration between data engineers and data scientists to acquire data from various sources and prepare the data (processing/validation) for modeling.
  3. ​Machine Learning pipeline: This stage is designing and deploying a pipeline integrated with CI/CD. Data scientists use pipelines for multiple experimentation and testing. The platform keeps track of data and model lineage and associated KPIs across the experiments.
  4. Production deployment: This stage accounts for secure and seamless deployment into a production server of choice, be it public cloud, on-premise, or hybrid.
  5. Production monitoring: This stage includes both model and infrastructure monitoring. Models are continuously monitored using configured KPIs like changes in input data distribution or changes in model performance. Triggers are set for more experimentations with new algorithms, data, and/or hyperparameters that generate a new version of the ML pipeline. Infrastructure is monitored as per memory and compute requirements and is to be scaled as needed.

References

  1. D. Sculley, G. Holt, D. Golovin, E. Davydov, T. Phillips, D. Ebner, V. Chaudhary, M. Young, J. Crespo, and D. Dennison, “Hidden technical debt in machine learning systems”, in Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, 2015, pp. 2503–2511. [Online]. Available: http://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems
  2. A. ​​​​​​​Karpathy, “Software 2.0”, November 12, 2017 [Online]. Available: https://medium.com/@karpathy/software-2-0-a64152b37c35
  3. E. Breck, M. Zinkevich, N. Polyzotis, S. Whang and S. Roy, “Data validation for machine learning”, in Proceedings of the 2nd SysML Conference, Palo Alto, CA, USA, 2019. Available: https://mlsys.org/Conferences/2019/doc/2019/167.pdf

 

Author

Arnab Bose, Chief Scientific Officer, Abzooba
Aditya Aggarwal, Advanced Analytics Practice Lead, Abzooba

blogathonMLOps
m
manoabz

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