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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

MLOps – 5 Steps you Need to Know to Implement a Live Project

Aishwarya
Aishwarya Last Updated : 06 Aug, 2021
7 min read

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

Introduction

MLOps - 5 Steps you Need to Know to Implement a Live Project

Image link: https://medium.com/sciforce/mlops-comprehensive-beginners-guide-c235c77f407f

Is your company looking to expand in the area of machine learning? A lot of companies are looking to explore machine learning to help bring more benefits. More often than not, it is not easy to begin implementing a live machine learning project without any prior experience. ML can boost up business in all the industries quite easily. The fintech sector has in fact taken to adopting scorecards for all their lending automation. Similarly, FMCG companies have taken up customer segmentation and personalized offers to bring in more business as is evident from all the e-commerce websites.

So What is MLOps?

So What is MLOps

Image link: https://nealanalytics.com/expertise/mlops/

MLOps is nothing but the amalgamation of machine learning, data engineering as well as DevOps. Every company is looking to expand in the field of machine learning. To develop and deploy an ML project you cannot use a regular DevOps technique. You need to use something called MLOps. It is nothing but the combination of all three factors – data, ML, and regular DevOps.

Scope of MLOps

Although tremendous amounts of new projects have been developed using machine learning, there is still scope for more. Such is the nature of ML. Every company is tapping into the potential offered by machine learning. Take the BFSI (Banking and Finance sector) for example, ML-based scorecards have been built by many of the institutions in order to automate the loan processing journeys.

All the tasks which used to rely on human instinct-based resolutions are being automated using ML-based approaches. Every vertical, right from the automobile industry, finance, e-commerce, etc has begun tapping into the huge potential offered by ML.  Companies offer you products based on your search histories, personalize their offerings for you, and do much more. These are some of the interesting use cases of ML.

Benefits of MLOps

Some of the benefits of MLOps are as follows:

  • Creating ML models is one thing and deploying them is another thing. MLOps helps in deploying them in a much faster and easier way.
  • Consider this scenario. You create an ML model and deploy it separately. After some time validating it and fine-tuning the parameters and then redeploying the model becomes a huge hassle involving multiple people and multiple tasks. This takes up a huge chunk of time, effort, and money. MLOps avoids all this since the whole project is integrated in a seamless manner.
  • You can also easily reuse the model for other use cases.
  • Since the validation and reporting are integrated into the system, monitoring becomes a very easy job.

Challenges of MLOps

So what are the common challenges faced while deploying an MLOps project?

  • For any ML-based project, the first and foremost requirement is the infrastructure. A huge amount of computing power and memory capacity is generally required. If only on-premises servers are relied upon, then adequate planning and arrangements need to be made for the project.
  • It would be a good idea to expand to cloud architecture to avoid this hassle as scaling up and scaling down becomes an easy task. A lot of companies are moving towards AWS services for this purpose.
  • A process of continuous monitoring is required. ML models may have been validated before moving to the production environment but it doesn’t mean that they will stay relevant forever. Frequent monitoring of the parameters and changing the model variables are required periodically.

Process of MLOps

MLOps has 5 main steps to be taken note of. These steps can help you to complete the project in an easy and seamless manner.

To begin with, if you have a good ML project idea and a team that includes a data scientist, data engineer, developers, testers, you are good to start. In a lot of ways, developing an ML project is similar to a regular software project except that it has a few added steps.

Process of MLOps

https://twitter.com/lawrencehecht/status/1164470771026190341?lang=ga

Let us take a look at the 5 important steps required to implement a successful ML project.

1. Team Integration

Planning and assembling the right team is the first step. You need one or more machine learning engineers (ML engineers), data engineers, and DevOps engineers. The number of people required will be based on the complexity of the project. Data engineers are required for manipulating data from various sources, data scientists for the modeling part, and the DevOps team for the regular development and testing.

2. ETL step

This step is aimed more at the machine learning part than at the DevOps part. For the modeling, extract the data from all the sources and create a pipeline to ensure that the data extraction will be seamless even in the system.

Work through the data to identify any nulls, exceptions, or useless values. You need to identify the ideal replacement mechanism for it. It could be anything ranging from replacing them with the average, median, mode, etc. In some cases, you could also remove the entire row.

Create an automated system, wherein the data will flow from the various sources into your system with all the necessary transformations in a seamless manner.

3. Version Control

Version control is followed quite strictly with respect to DevOps. In the same manner, it is important to have strict version control in place for the ML model as well. You can use a Git repository for this as well.

So why is versioning important for ML modeling? Well, each time you run the model, you may be changing the various parameters. This in turn will give different results. If say in the future you wish to go back to the previous set of parameters, versioning will help. another important point is that versing can help you study the various changes done to the model and how it has evolved over the years/months. To help with the pipelining activity a lot of companies rely on end-to-end solutions such as KubeFlow or MLFlow.

Image Link: https://predera.com/end-to-end-ml-platform-are-we-there-yet-part-4/

4. Testing

Now, this step can get you thinking. A DevOps project has unit testing, integration testing, etc. In an ML project what exactly constitutes testing?

Well, in simple terms, the model validations are what is considered testing for an ML model. So the testing phase should ideally have 2 steps – one for model validation and the other for data validation.

Model validations include checking for the bad rates, accuracy, ROC, area under the curve, Population Stability Index (PSI), Characteristic Stability Index (CSI), and so on. The parameters for validation ned to be set based on the models being used and the use cases. The validation parameters will vary based on the models being used. For example, if you are using an unsupervised ML model like k-means clustering then accuracy, F-measure, precision, and recall are good validation parameters. On the other hand, if you are using logistic regression or linear regression models, then PSI, CSI, GINI are good validators.

So what does data validation do? ML models are oddly specific in a certain way. If the input changes, then the output will also vary. As long as the model is able to make sense of the input, it will give out sensible predictions. So the data validations are done to ensure that the input from the various sources does not change the format. They should be present in the format required for processing the ML model. This is also an important step in the lifecycle of MLOps.

5. Monitoring

So, once your project is properly integrated and has gone live, the work doesn’t end. In a regular DevOps project, once the project goes live, the work of the developer is completed unless there are further enhancements. In an MLOps project, it is important to periodically monitor the performance of the ML model. In any ML modeling, periodic monitoring ad validation is an integral component.

The periodicity of monitoring will depend on the model used. However, it has to be regularly validated against the live data using the same validation parameters used to ensure that the model is working as expected. If at any point you find that the validation parameters are not working properly, you will need to rework the modeling activity. This could either be a minor exercise where a small level of fine-tuning might suffice or a major activity where the model parameters need to be completely reworked.

You can even set up automated reporting to enable easier tracking of these parameters. A periodic reporting can help you notice even the minor deviations in the performance metrics which can be adjusted through a little bit of fine-tuning.

 

Final Thoughts

To conclude, although it is not a child’s play to implement an MLOps project neither is it a gargantuan task. The first-ever MLOps project might seem very complicated and time-consuming but the subsequent projects will become easier to implement and will definitely take less time. The first one is always where you get to learn the basics of implementing an MLOps project. So do not get bogged down and get going as the second, third, and subsequent projects will be a breeze to manage.

Final Thoughts

About the Author

Hi there! This is Aishwarya Murali, currently working in the analytics division of Karnataka Bank’s – Digital Centre of Excellence in Bangalore. Some of my interesting projects include ML-based scorecards for loan journey automation, customer segmentation, and improving the market share via selective profiling of customers using some machine learning analytics.

I have a master’s in computer applications and have done certification in Business Analytics from IIM-K. Currently, I am working on R&D innovations at my workplace.

You can connect with me at

https://www.linkedin.com/in/aishwarya-murali-b710a978/

You can also mail me at

[email protected]

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

Aishwarya
Aishwarya

Hi there! This is Aishwarya Murali, currently working in the analytics division of HSBC in Bangalore. Some of my interesting projects include ML-based scorecards for loan journey automation, customer segmentation, and improving the market share via selective profiling of customers using some machine learning analytics.

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