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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 and Use of Kubernetes

Gitesh Dhore
Gitesh Dhore Last Updated : 15 Sep, 2022
5 min read

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

Introduction

The constant rise prompted organizations to create a system that was implemented by building factories, assembly lines, and other elements of automated production. Soon after, the technology boom saw the emergence of agile systems that automate creation processes. This was achieved by operationalizing the product life cycle, which helped drive continuous innovation by eliminating waste.

MLOps
Source: www.soapboxlabs.com
Of course, all of these processes have brought us to the current world, where we are turning our attention to the insights of machine learning. This brings us to MLOps.

What is MLOps?

Machine Learning Operations, or MLOps, is a framework that focuses on collaboration between data scientists and the operations unit within an organization. The framework is designed to reduce errors, minimize waste, further improve automation, and produce more valuable insights with the help of machine learning. MLOps follows a similar path to DevOps. While DevOps focuses on shortening the product lifecycle by building better products every time, MLOps delivers insights that can be put to better use immediately.
MLOps is considered to integrate the best of both worlds because of its mandated role in improving organizational operations. MLOps encourages data scientists to view their roles with organizational interest, which helps ensure clarity and measurable metrics.

DevOps vs. MLOps: What is the Basic Difference?

Although many would believe that since machine learning is a software engineering discipline, DevOps principles can be applied to it. While this is true to some extent, the two have several key differences. DevOps is a practice involving building and operating software systems at scale.
MLOps
Source: https://coe-dsai.nasscom.in
MLS includes ML, a software system, and DevOps concepts that can be used to build ML systems at scale.
However, ML systems differ from other software systems in several ways –
  • Compared to DevOps, MLOps is more experimental. This new framework requires data scientists to experiment with different functions, parameters, and models.
  • When working with ML, the team will typically include data scientists and ML researchers who can help with model development, understanding exploratory data analysis, and experimentation. Although they can better understand the system, they cannot offer the production-level services that software engineers are capable of.
  • ML testing is comparatively more complex. The process would include data validation, model validation, and trained model quality assessment, along with unit and integration tests.
  • Deploying ML is also a complex process because it requires a multi-step pipeline to be in place, allowing you to automate the process of retraining and deploying models.
  • Constantly evolving data profiles in ML can result in reduced performance when paired with suboptimal encoding. Models can break down in more ways than other software systems, requiring experts to track aggregate statistics and monitor performance.

Similarities between ML and other software systems are continuous integration, resource management, integration testing, unit tests, and continuous delivery. In ML, however, continuous integration goes beyond code and components to include data: testing and validating data and data schemas. Continuous delivery also requires ML training that uses automation to deploy a model prediction service.

What are the benefits of MLOps?

Operationalizing data helps gain insight and leverage that knowledge to develop actionable business value.
Here’s how adding MLOps can help organizations get more value –
  • MLOps helps bridge the gap between the business knowledge of an operating unit in a company and the studies performed by the data science team. MLS seeks to take advantage of both spheres to create more valuable ML.
  • While data scientists may be working hard to gain better insight, all efforts can prove futile if your organization runs into trouble with regulators.
  • MLS helps drive investment in current machine learning and data science tools and technologies to a much greater extent. This helps build a record-keeping system between different teams and projects.

How can an organization implement MLOps?

Here are some basic points to consider before implementing MLO in an organization model –
Benchmarks – Organizational KPIs should be concise and measurable to engage all members. There must be an ongoing collaboration between data scientists and operations team members to understand their roles and leverage insights.
Monitoring – Both units must be monitored at every step of the process. Because ML requires data to be regularly retrained, the organization requires careful monitoring of the process to ensure that everyone is working in compliance and that the programs provide quality information.

Compliance – To ensure compliance with every step, MLOps requires a thorough management plan to help ensure the programs created are auditable and within the scope of operations.

How does Kubernetes advance MLOps?

Kubernetes is essentially an open-source container orchestration system used by organizations to automate desktop applications’ deployment, scaling, and management. As an orchestrator, Kubernetes is used to build scalable distributed systems and is also used to bring much-needed flexibility to the various machine learning frameworks that data scientists can work on.

This flexibility extends to the scalability and repeatability required by the units that run the machine learning systems in the products and the greater control over resource allocation required by the operational unit. Kubernetes can greatly facilitate the process for data scientists and business operators when used in machine learning.

Source: https://www.dkube.io

 

Data science and deployment paths are usually different entities. On the one hand, data scientists create experiments using one set of tools and infrastructure, while development teams recreate the model using different tools and infrastructure. To make the process more cohesive, organizations should look to implement a combined pipeline in the form of Kubeflow, which uses Kubernetes to train and scale models on multiple frameworks without requiring any expertise in infrastructure planning.

Conclusion

Machine learning is the future of data science, and integrating MLOps into the organizational structure can go a long way in reducing errors and building models with greater efficiency. MLOps can benefit from the tools used in DevOps today to implement CI/CD and production best practices. Kubernetes is very well-suited for machine learning.
    • MLOps is considered to integrate the best of both worlds in its mandated role in improving organizational operations. MLOps encourages data scientists to view their roles with organizational interest, which helps ensure clarity and measurable metrics.
    • It’s the perfect platform for deploying machine learning models to production, running scheduled jobs, distributed computing, and CI/CD pipelines. Even if you’re not a Kubernetes expert, platforms like CloudPlex allow you to create a Kubernetes cluster (on any major cloud provider or bare metal) for free and in minutes.
    • MLOps helps bridge the gap between the business knowledge of an operating unit in a company and the studies performed by the data science team. MLS seeks to take advantage of both spheres to create more valuable ML.
    • ML testing is comparatively more complex. The process would include data validation, model validation, and trained model quality assessment, along with unit and integration tests.

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

Gitesh Dhore
Gitesh Dhore

I am a Machine Learning Enthusiast. Done some Industry level projects on Data Science and Machine Learning. Have Certifications in Python and ML from trusted sources like data camp and Skills vertex. My Goal in life is to perceive a career in Data Industry.

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