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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

Real-time Challenges of Machine Learning Projects

Tech
Tech Last Updated : 14 Nov, 2022
4 min read

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

Introduction

Machine learning projects can be extremely challenging in the IT industry. Several factors can make them difficult, including the volume of data that needs to be processed, the complexity of the algorithms involved, and the need to ensure that the systems are accurate and reliable. In addition, machine learning projects can be time-consuming and expensive to develop and deploy. The challenges of machine learning projects in the IT industry can be daunting but also very rewarding.

Machine Learning

Source: unsplash.com

This blog will be divided into two sections; the first part, i.e., Challenges or pain points, and the second part, which is a Solution to overcome the desired set of challenges. It will give a super lens that could be applied to your current project and organization.

Disclaimer: The below-discussed points mainly focus on consultant organizations, not in-house ML companies.

Challenges of Machine learning Projects in the IT Industry

1. Data – Though we are in the era of data evolution. However, this is still one of the biggest pain- points for many ML and DS people in many organizations. The first challenge is often in finding the right data to train the machine learning algorithm. The data needs to represent the problem you are trying to solve, and it can be difficult to find enough data to cover all the possibilities. How many of you can relate to a situation where you have been asked to develop something without giving you any data? All the business stakeholders have fancy ideas and thoughts about machine learning and artificial intelligence. They ask ML developers to come up with some implementation without any clarity about the data or data elements. This is one of the real challenges, especially in new growing industries or startups.

2. Connecting bridge –  This is frequently another puzzle component that is overlooked. While ML experts have their own preferred method of working on a given use case, business folks have their notions about certain use cases and their outcomes. And frequently, it is rather challenging for people to communicate their views to one another. Additionally, the technical portion is tiresome and uninteresting to comprehend for businesspeople. How can businesses overcome the challenge of a lack of machine learning knowledge in their decision-makers? The challenge for businesses is the lack of knowledge in machine learning among business people. Without this knowledge, businesses will not be able to take advantage of the opportunities machine learning presents.

Machine Learning

Source: unsplash.com

3. Production -As per the Gartner report, 87% of ML projects like prediction modeling, forecasting, and recommendation system never goes into production, and there are different reasons and factors. Machine learning projects can be challenging to manage in a production environment. Several factors must be considered when deploying a machine learning model into a live system. One of the biggest challenges is ensuring that the model is accurate. The model may be used in a production environment to make critical decisions, so it must produce accurate results. Another challenge is ensuring that the model is stable. The model may be used frequently and under various conditions in a production environment. The model must perform consistently under these conditions. A third challenge is ensuring that the model is efficient. The model may need to handle a large volume of data in a production environment. The model must be able to process this data quickly and efficiently. Finally, it is important to ensure that the model is secure. The paucity of resources for these ML operations and the lack of a standardized process for ML model deployment are making the issue worse.

Solutions for the Machine Learning Application

  1. Keep asking the relevant questions in client calls for the data required to build the particular defined use case.
  2. Make the problem statement or use case very clearly and dig every outcome.
  3. Practice narrating stories using simpler language. The company stakeholder’s interest will be maintained in this way. Always remember the fundamental rule that money comes from profits, not company ideas. Businesspeople are, therefore, always highly valued in any organization.
  4. Always make your story around the points that business people are interested in. This will give weightage to your ML project. Even though it is difficult for them to absorb, they will be keenly interested in the results.
  5. The right education and training for business people from time to time will help in overcoming the lack of ML knowledge in business people or decision-makers
  6. Make a proper plan for deployment and add the resources needed to make the project live.
  7. There is a wide variety of ML ops tools in the market. They provide long-lasting benefits like model versioning, lineage, and packaging, to name a few advantages. Make maximize of them. My favorite is ML flow.

Conclusion

In the rising field of AI, where Technologies and platforms are constantly evolving, it can be difficult to keep up with the latest trends. It is still challenging to make ML projects and deploy them into production. Constant efforts and setting up of organization in 360view by involving technical and business people as decision-makers could change the situation in the coming years. Educating and training about ML areas with tools like no code or low code platform for businesspeople can improve things a lot easier and better. There has been a lot of buzz lately about the benefits of advanced machine learning (ML) algorithms in the IT industry. By utilizing these algorithms, businesses can improve their operations and decision-making processes. Some of the benefits of advanced ML algorithms include the following:

  1. Increased accuracy and efficiency in data analysis
  2. Improved customer service through the use of predictive analytics
  3. More effective fraud detection and prevention
  4. Greater insight into market trends and customer behavior
  5. Enhanced automation of business processes

Lastly, finding real-time use cases appealing to business and storytelling are the two important factors that would play a key role in finding a better solution in this industry.

I hope you enjoyed reading it. Thank you.

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

Tech
Tech

Dr. Monica Mundada is a highly skilled and experienced data scientist with a passion for solving complex business problems using data-driven insights. Monica has a background in Ph.D., where she developed a strong foundation in statistics, mathematics, and computer science.

As a data scientist, Monica has a proven track record of success in collecting, cleaning, and analyzing large datasets to identify patterns and trends that inform business decisions. She is skilled in using a variety of tools, including Python, R, SQL, and machine learning algorithms, to perform their work. She is also adapted to data visualization, using tools like Tableau, PowerBI, or Matplotlib, to present their findings in a clear and concise manner.

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