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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

Meta’s HawkEye: Transforming ML Debugging for Enhanced Efficiency

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 03 Jan, 2024
3 min read

In a groundbreaking move, Meta has introduced HawkEye, a revolutionary toolkit aimed at transforming the landscape of machine learning (ML) debugging. Addressing the challenges of debugging at scale, HawkEye streamlines monitoring, observability, and debuggability for Meta’s ML-based products. This article delves into the key features and benefits of HawkEye, outlining its impact on debugging efficiency and the broader field of ML development.

Also Read: Apple Secretly Launches Its First Open-Source LLM, Ferret

The Challenge of ML Debugging at Meta

Meta’s ML-based products face intricate challenges, including diverse data distributions, multiple models, and ongoing A/B experiments. The need to efficiently identify and resolve production issues is crucial for ensuring robust predictions and enhancing user experiences and monetization strategies.

HawkEye efficiently debugs Meta's ML products

Historically, debugging machine learning models at Meta required specialized knowledge and extensive coordination across different departments. Engineers relied on shared notebooks and code for root cause analyses, consuming significant time and effort. This is where HawkEye comes to the rescue.

HawkEye’s Revolutionary Approach

HawkEye emerges as a transformative solution, introducing a decision tree-based approach that dramatically reduces the time spent on debugging complex production issues. Unlike traditional methods, HawkEye empowers both ML experts and non-specialists to triage issues with minimal coordination, marking a paradigm shift in ML debugging.

HawkEye's ML debugging workflows
Source: marktechpost

HawkEye’s operational debugging workflows offer a systematic approach to identifying and addressing anomalies in top-line metrics. The toolkit isolates prediction anomalies to specific features, leveraging advanced model explainability and feature importance algorithms. Real-time analysis of model inputs and outputs enhances the efficiency of the triage process.

Also Read: Microsoft’s WaveCoder and CodeOcean Revolutionize Instruction Tuning

Key Features of HawkEye

HawkEye boasts several key features that set it apart in the ML debugging landscape. Its user-friendly interface ensures accessibility for both ML experts and professionals with less specialized knowledge. The toolkit’s advanced diagnostics, real-time monitoring, and automated issue-detection capabilities contribute to its efficiency.

HawkEye supports collaborative debugging efforts, offering shared workspaces and integrated communication tools. Customizable workflows, scalability, and seamless integration with existing systems make HawkEye a versatile and essential tool in ML development.

Also Read: Monster API: Bridging the Gap Between Open Source Model Tuning and Deployment

Impact Across Industries

The launch of HawkEye not only transforms Meta’s ML debugging capabilities but also has far-reaching implications across industries. In healthcare, finance, retail, and e-commerce, enhanced ML capabilities promise advancements in diagnostics, personalized medicine, fraud detection, risk assessment, and customer experience.

Our Say

HawkEye’s introduction by Meta sets a new standard for ML development tools, fostering innovation, and growth in the field. It stands as a game-changer in Meta’s commitment to enhancing the quality of ML-based products. Its streamlined decision tree-based approach simplifies operational workflows, democratizes ML development, and promises continuous improvement through community collaboration.

HawkEye’s transformative capabilities position Meta at the forefront of machine learning innovation. It makes Meta a driving force towards efficiency, innovation, and growth in this dynamic field. As Meta embraces HawkEye, it signals a new era in ML debugging, promising a future of enhanced user engagement and effective monetization strategies.

DebuggingefficiencyFeaturesHawkEyemachine learningMetaMLML debuggingML DevelopmentML Modelsnews
K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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