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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

How Open Source Beats Proprietary Software Helps for MLOps?

Mrinal Singh
Mrinal Singh Last Updated : 01 Jun, 2021
5 min read

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

Introduction

 

Open Source MLOps image

It’s necessary to keep your ML pipeline for compliance and authority.

Machine learning teams can practice proprietary programs like Knime or SageMaker or create their open-source servants.

Companies frequently sell proprietary policies as more compelling, more productive, and more comfortable to use.

But in actuality, they’re usually extra complicated and limited potent than open-source choices.

Open source is a theory as beloved as the software industry itself. The original open-source software employment became commonplace concurrently as computer instruments, in the form of the essential codes that came beside them. These advancements took place in the educational world, where the fundamental operating policies were collaboration and participation.

We create our own MLOps structure from utterly open source organizations. Here’s why control of your program is crucial.

Open source is tremendous

 

Open Source MLOps tremendous

 

  • People are sometimes skeptical of free tools, but open-source software is frequently of higher quality than funded alternatives.
  • Megacorps like Google and Microsoft rely massively on open-source software and often have their most skilled engineers operating on these projects.
  • Open-source projects also profit from having many added eyes on them than closed-source choices. If a developer finds a defect, they can offer a fix that benefits everyone.
  • Eventually, open-source software is customarily not marketed; determining incentives are more beneficial because the feature is the critical factor defining success.
  • By diversity, the achievement of business projects can depend far more on selling, partnerships, and branding. This suggests companies are often incentivized to pay more time and energy on these measures than engineering.

Open source aims to interchangeable skills, which makes employing easier.

Open Source MLOps skills

 

  • Engineers need to discover open source tools because they can utilize this information at other organizations, too.
  • No one desires to be trapped in their prevailing job because they wasted ten years studying the ins and outs of some bodily platform.
  • Training your company to use devices relevant to other organizations, like TensorFlow and Kubernetes, might make any people annoyed.
  • Still, honestly, it’s the single sustainable way to pull top engineering expertise.
  • Your team can profit from those transferable skills in different ways, too: it’s far more comfortable to find specialist consultants or even pure community-sourced guidance if you use similar tools as everyone other.

Open source solutions are further modular.

Open Source MLOps modular

 

  • Being “all-encompassing” is a significant way business platforms try to distinguish themselves.
  • Alternatively of building only an education platform, or only a deployment device, they sell themselves as “all-in-one” answers or “the last machine your organisation will need”.
  • But being the greatest at everything is unreliable. And the monolithic reality of these answers makes it more difficult to swap out different components.
  • If your team requires using the most suitable model registry, they often can’t secure it into their current platform.
  • By distinction, open-source software is regularly more granular and entirely focuses on combining with other principles.
  • This involves an open-source solution that is more like performing with lego: if part of it provides you trouble, you can detach it and switch to an option.

There are unseen costs in established programs.

 

There are unseen costs in established programs.

 

  • Proprietary platforms are incentivized to sell upgrades and innovations to existing customers continually.
  • In many circumstances, this means they’re not upfront regarding their conditions.
  • During trades, they might persuade you that a low or mid-level program suits your requirements perfectly.
  • Only after purchasing in do you understand they’ve strategically extracted key features or appended specific limits intended to disable your workflow until you pay for the next upgrade.
  • These platforms can also grow their pricing with the next-to-no announcement, so your co-workers can either struggle to rewrite everything or pay higher than you planned.

 

When does it make sense to practice exclusive platforms?

 

When does it make sense to practice exclusive platforms?

 

  • If your company doesn’t have much planning expertise, especially when it gets to DevOps and designing and maintaining infrastructure, fixing up and managing open source clarifications can be challenging.
  • Open source instruments tend to be developed “by developers for developers”, so non-technical organisations like retailing might also fancy proprietary platforms in some positions.
  • Lastly, if you’re operating on small internal designs or proof-of-concept purposes, there’s usually little need for foundation.
  • If people aren’t running your solution as a commodity, an off-the-shelf mechanism for something like an intelligent visualisation is typically good enough.

Challenges With Proprietary Softwares

  1. Products can be massive: Proprietary software units may include a lot of bloat and useless items.
  2. Cost shocks: Aside from the cost, sometimes pricing methods can include surprises that can blindside all of you.
  3. Complex license designs: I used to be the principal Microsoft licensing person for a business I operated for. I have exerted science tests that held scarce complex than deciding out their licensing agreement.
  4. Confidence in businesspeople: The flip bottom to the “one-stop shopping” benefit is that you may end up happening overly reliant on the vendor, bolted into a shut system.
  5. Switching can be challenging: Spending in proprietary software generates a repetitive model whereby corporations generally continue their results based on their previously spent capital.

Final Decision

However, it depends on the choices and requirements of the business and the open-ended project for their digital enterprise.

Open-source software can be regarded as a befitting answer than a locked source or proprietary software.

By excluding barriers connecting innovators, open-source encourages a free interchange of thoughts within a community to promote creative, scientific, and technological progression.

By installing an open-source license on new work, a body or organisation conforms to:

  • Make the entirety of the program’s code open to society.
  • Allow anyone to transform, improve or re-engineer a program’s code.
  • Support the production of derivative works.
  • Enable the program to be used for any idea the user desires.

The open-source action is why technology has expanded at such a breakneck speed for the preceding several years.

If you are reading this blog, I am sure that we share similar interests and will be in similar industries. I have worked on various ML/AI/CV projects and tools and written blogs on multiple platforms to share my knowledge. Let’s connect on my social media platforms:

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

Mrinal Singh
Mrinal Singh

Data Scientist and a Technical Writer! I will give you the best of Open-Source and AI.

Talks about #chatgpt, #opensource, #contentcreation, #communitybuilding, and #artificialintelligence

Technical Writer | Data Science, ML, AI, Open-Source | Do More with Data - Litmus

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