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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 vs. ModelOps: What’s the Difference?

Emily
Emily Last Updated : 22 Feb, 2023
6 min read

Introduction

Machine learning operations (MLOps) and model operations for artificial intelligence (ModelOps) have become increasingly important as more companies and organizations explore how they could use machine learning. Enterprises should ideally invest in both MLOps and ModelOps to make the most of their machine learning utilization and to steer clear of costly pitfalls.

modelops

MLOps vs. ModelOps discussions often finds people using these terms interchangeably.

However, ModelOps and MLOps have substantial differences and overarching goals. They still both have places in any organization using machine learning, though. Here’s a closer look at some of the key differences between MLOps vs. ModelOps.

Learning Objectives:

In this article, you will understand the following:

  1. The importance of MLOps and how they help the data scientists.
  2. What are modelOps, and how it supports enterprise operations.
  3. The automated solutions for both models.
  4. The value of both models.

Table of Contents

MLOps Helps Data Scientists Deploy Models Faster

MLOps primarily concerns tools, processes, and practices that make deploying machine learning models at scale easier. Data scientists find it helps them get models ready for market faster. Many of these models also help them learn critical things within their professions, making them more of an asset to their workplaces.

Machine learning models can be highly experimental and have complex components. Those are some of the realities that have created the need for MLOps. It provides practices that improve communication and collaboration between data scientists and operations team members.

Deploying a machine learning model is more than pushing updated code to an application. Developers can handle the coding aspect, but the ever-changing nature of the data associated with the algorithm can cause issues. Company representatives may experience problems reproducing their models later or find deploying them takes longer than expected.

MLOps can reduce many of these issues. It can also help model builders spot and fix problems before they negatively affect the project.

MLOps

ModelOps Supports Enterprise Operations and Governance

ModelOps goes beyond model deployment and concerns how people govern and maintain all machine learning and decision models throughout their life cycles. MLOps gets machine-learning models ready to use and increases the chances of their successful deployments.

A real-life case study of ModelOps in action comes from IBM, which has a commercial solution called Cloud Pak for Data. A client used the service to create an artificial intelligence-powered bank loan solution.

Cloud Pak for Data helped the company collect and examine the data needed for the associated machine-learning models. It then allowed people to build and use them and enabled ongoing monitoring. Those steps are critical because they let leaders verify that a model works as expected and gets the desired results. If it doesn’t, the monitoring aspect of a ModelOps solution helps them spot and remedy problems faster.

ModelOps also prioritizes accountability. That links to the governance theme, helping organizations appropriately use algorithms and their data. Keeping people and departments accountable reduces the chances of fines or other unwanted attention from regulators. Including more accountability in using machine learning models is critical for getting more people to trust their results.

Fidelity Investments created a scalable ModelOps framework to easily deploy hundreds of models within the organization’s production environment. They go through a model governance process encompassing appropriate controls throughout the life cycle. This focus on ModelOps caused an 80% decrease in the time required to find and fix models in production. Moreover, it doubled production speed.

Automated Solutions Available for MLOps and ModelOps

A common misconception in MLOps vs. ModelOps discussions is that they compete. In reality, they’re complementary and necessary for successfully deploying and using machine learning models.

However, MLOps and ModelOps solutions include automated capabilities to make them more user-friendly. Qwak is an MLOps startup aiming to standardize machine learning project structures. Users also can use Qwak’s tools to get support throughout the deployment process and use an optional data lake to make information management easier.

SAS is one of the many well-known companies offering automated ModelOps solutions. ModelOps automation features are comparably more robust because of the need to support machine learning models throughout their life cycles.

Even though people can buy automated tools for MLOps and ModelOps, they must stay involved in all parts of the workflow. The more aware they are, the easier it will be to build and use models that work as intended and help organizations become more resilient.

It’s also important for decision-makers to set budgets and timelines associated with how they’ll use any automated solutions. These products are not cure-alls for major process flaws, but they can help people achieve better visibility into their machine-learning projects.

MLOps

ModelOps Supports Future-Proofing Efforts

ModelOps concerns the governance and full life cycle management of all artificial intelligence and decision-making models. Thus, it includes machine learning but extends to knowledge graphs and natural language processing.

A good way to think about ModelOps is that it keeps machine learning models current and easily updated, making them relevant in the fast-changing landscape. Otherwise, data drift can occur when a company’s operational information doesn’t match what was used to train a machine learning algorithm. Things like changing consumer preferences or product launches in new markets can cause it. Model drift manifests when its predictive power decreases due to real-world fluctuations.

Adaptability is one of the vital components of any artificial intelligence deployment. Algorithms must react to conditions in the moment. MLOps and ModelOps make that possible, but ModelOps lets company representatives tweak existing models faster or test that they still work as intended.

Experts also point out that ModelOps can add preventive measures that keep machine learning projects from going off track later. People who bring standardization to their processes develop models that are more likely to be scalable and reusable, providing a higher return on investment.

Process improvement is significant when using machine learning to make life-changing decisions. For example, using ModelOps in health care is becoming increasingly common. It can assist with diagnostics and create personalized plans with the support of human expertise. However, many people have concerns about machine learning’s unexplainable nature.

People can’t always pinpoint how machine learning reaches decisions. However, showing the process used to build, maintain and oversee the algorithms could help improve public trust.

MLOps Can Boost Productivity

The focus of MLOps is to deploy machine learning models, so it can help organizations use algorithms more effectively. It reduces overall expenditures by making it unnecessary to develop multiple models for one purpose.

Instead, people could build and tweak one to meet an organization’s needs. This is one example of how MLOps and ModelOps often overlap. It’s not as easy to update existing models if the company does not also utilize ModelOps. MLOps makes deployment easier, but ModelOps streamlines the parts of the life cycle beyond and including deployment.

Experts often discuss how MLOps can increase model velocity, minimizing the time required to deploy a machine-learning solution. It may only take weeks or days when a company uses MLOps. Otherwise, the deployment time could span months, and the models may not come to fruition after all that effort.

A 2022 study from Accenture showed that only 12% of companies using artificial intelligence were doing so at a high maturity level. However, the enterprises in that small group used the technology to outperform competitors. They also had 50% higher revenue growth than peers at lower AI maturity levels.

MLOps does not guarantee the success of machine learning projects. However, it can improve the likelihood of desirable outcomes by removing or reducing many aspects contributing to failure.

MLOps and ModelOps: Both Have Value

Companies and individuals working with machine learning models often use MLOps and ModelOps. Knowing how they differ is the first step in applying them successfully.

However, this overview clarifies it’s not always productive for people to compare MLOps vs. ModelOps by focusing too much on their differences. It’s often more advantageous for everyone involved to see how they’re both critical for successfully using machine learning.

Think of MLOps as supporting the backend infrastructure necessary for using automation. It relates to the operational practices vital for successfully using machine learning.

Most people and organizations find both valuable throughout their machine-learning journeys. Individuals should explore how and when to use them together rather than trying to choose one over the other.

Conclusion

It’s increasingly common for today’s companies to explore how they might use MLOps and ModelOps to enhance their machine learning workflows. Being familiar with both options and understanding how they apply in organizations is the first step when considering implementing them.

Key takeaways:

  • MLOps support better productivity during model-building and deployment, streamlining data scientists; work.
  • ModelOps makes machine learning models more scalable, easy to update, and future-proof.
  • Automated solutions can help people use ModelOps and MLOps cost-effectively.
  • It’s typically best for companies to investigate using MLOps and ModelOPs together rather than focusing on their differences.

Emily Newton is the Editor-in-Chief of Revolutionized, an online magazine exploring science and technology innovations. She loves seeing the impact technology can have on every industry.

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

Emily
Emily

Emily Newton is the Editor-in-Chief of Revolutionized, an online magazine exploring science and technology innovations. She loves seeing the impact technology can have on every industry. You can also find more of her published work on IoT For All and IT Chronicles.

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