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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

12 Best AI Tools for Data Science Workflow

Yana Khare
Yana Khare Last Updated : 26 Jul, 2024
8 min read

Introduction

Today’s world is focused on data; businesses must utilize advanced AI technology to stay ahead and improve efficiency. Some instruments assist data scientists, analysts, and developers in efficiently creating, deploying, and overseeing machine learning models. This article explores some of the leading AI tools and platforms in the data science workflow.

Table of contents

Cloud Platforms

Amazon SageMaker & Bedrock

Amazon SageMaker is a completely managed service that enables developers and data scientists to create, train, and release machine learning models efficiently. Another is Amazon Bedrock, which can be used in data science workflows. It is a service managed to develop and expand generative artificial intelligence applications using base models.

Key Features:

  • Integrated development environment for ML workflows.
  • Automated machine learning (AutoML) that automatically builds and trains models.
  • Central repository to store, update, retrieve, and share features.
  • CI/CD service for end-to-end machine learning workflows.
  • Tools for model debugging, monitoring, and profiling.
  • Data labeling service to create high-quality training datasets.
  • Provides access to foundation models like Jurassic-2, GPT, and more for generative AI tasks.

Pricing: Pricing for Amazon SageMaker varies based on usage, including computing, storage, and instance hours. Different pricing tiers depend on the services used (e.g., training, inference, SageMaker Studio). Amazon Bedrock’s pricing depends on the specific foundation models used and the compute resources required for inference and training.

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Google Cloud Vertex AI

Google Cloud Vertex AI offers a centralized platform for creating, implementing, and expanding machine learning models. It streamlines the complete ML process, including data intake and preparation, model training, assessment, and deployment.

Key Features:

  • Train high-quality models with minimal effort using automated machine learning.
  • Jupyter-based environment for data scientists to build and experiment with models.
  • Continuous monitoring and retraining of deployed models.
  • Manage and serve ML features for training and serving.
  • Tools to create, manage, and monitor ML pipelines.
  • Seamless data integration with Google’s data warehouse service.
  • Tools for interpreting and understanding model predictions.

Pricing: Vertex AI pricing uses many components, such as AI Platform Training, AI Platform Prediction, and AutoML. Costs vary according to what a user might choose.

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Microsoft Azure Machine Learning Studio

The Microsoft Azure Machine Learning Studio is a cloud-based IDE designed for creating, teaching, and launching machine learning models. This AI tool for data science workflow offers a shared, minimal-code platform for data scientists and developers.

Key Features:

  • Simplifies the process of model creation with a visual interface.
  • Automatically selects the best algorithms and hyperparameters.
  • Effortlessly blends with Azure services such as Azure Data Lake, Azure Databricks, and Azure SQL Database.
  • Collaborative development can be done using Jupyter notebooks.
  • Integrated tools for controlling, deploying, and overseeing models.
  • Capable of working with TensorFlow, PyTorch, Scikit-learn, and more.
  • Utilizes Azure’s cloud infrastructure to enable scalable computing.

Pricing:  Azure Machine Learning Studio structures payments so that users pay only for the resources they use, such as virtual machines, storage, and compute hours. Microsoft provides various pricing levels and discounts for customers who commit to longer terms or use high volumes of their services.

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Machine Learning and Deep Learning Libraries and Platforms

TensorFlow

Google developed TensorFlow, an open-source machine learning framework. It is commonly utilized for constructing, teaching, and implementing machine learning models, specifically deep learning models. TensorFlow can handle various tasks, from research to deployment in production.

Key Features:

  • Incorporates TensorFlow Core, TensorFlow Lite for mobile and embedded gadgets, TensorFlow Extended (TFX) for complete ML workflows, and TensorFlow.js for ML in JavaScript.
  • Suitable for beginners and advanced users, it accommodates both eager execution and graph mode.
  • Provides advanced interfaces such as Keras for rapid prototyping and alternative interfaces for greater control and customization.
  • Instruments for implementing models on different platforms, such as cloud, mobile, web, and IoT devices.
  • Extensive documentation, tutorials, and a vibrant community contribute to its ecosystem.
  • Visualization tool for model training and performance metrics.

Pricing:  TensorFlow is available at no charge and is open-source. Expenses are linked to the computing resources (such as GPUs and TPUs) utilized for training and deploying models, which can be controlled via cloud services such as Google Cloud Platform (GCP).

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Hugging Face

Hugging Face focuses on NLP and transformer models. It offers a well-liked open-source library named Transformers, containing pre-trained models for different NLP tasks and a platform for distributing and collaborating on models.

Key Features:

  • Access to state-of-the-art pre-trained models for tasks like text classification, translation, summarization, etc.
  • A platform to discover, share, and deploy pre-trained models.
  • A collection of datasets for training and evaluating models.
  • Easy-to-use API for deploying models to production.
  • Simplified training and fine-tuning of models.
  • Efficient tokenization tools for preprocessing text data.

Pricing: Hugging Face provides both free and paid plans. The free tier allows users to access basic features, while the paid plans, starting at $9 per month, include additional capabilities such as private model hosting, accelerated inference, and premium support. Enterprise pricing is available for larger organizations with custom requirements.

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PyTorch

Facebook’s AI Research lab produced the open-source machine learning package PyTorch. Due to its adaptability and ease of use, this AI tool for data science workflow is frequently used in deep learning tasks, particularly in academic research and industrial environments.

Key Features:

  • Makes model construction more straightforward to understand and more adaptable.
  • For computer vision and natural language processing, they include libraries like TorchVision and TorchText.
  • Seamless interaction with NumPy and SciPy, two Python libraries.
  • Makes use of GPUs to accelerate computing.
  • Strong community support accompanied by a wealth of lessons and material.
  • Facilitates exporting models for compatibility with other frameworks in the Open Neural Network Exchange (ONNX) standard.

Pricing: PyTorch is free and open-source under the BSD license. Using computing resources (e.g., GPU/TPU instances) to train and deploy models typically incurs costs through cloud providers or on-premises infrastructure.

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Scikit-learn

Scikit-learn is a popular Python machine-learning library frequently used as an open source. This AI tool for data science workflow includes a variety of classification, regression, and clustering algorithms and is developed using NumPy, SciPy, and Matplotlib as its foundation.

Key Features:

  • For data mining and data analysis.
  • Easy to learn and use for various machine-learning tasks.
  • Extensive user guides and API references.
  • Includes algorithms for classification, regression, clustering, and dimensionality reduction.
  • Tools for cross-validation, grid search, and other evaluation metrics.
  • Works seamlessly with other Python libraries like Pandas and Matplotlib.

Pricing: Scikit-learn is free and open-source under the BSD license. As with PyTorch, costs are associated with the computational resources required to run the library, which vary based on the user’s environment.

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Polars

Polars is a fast, multi-threaded DataFrame library for Rust and Python. It is designed to handle large datasets efficiently and aims to be a faster alternative to Pandas.

Key Features:

  • Optimized for speed with multi-threaded execution.
  • Designed to handle large datasets with minimal memory overhead.
  • Uses lazy computation for performance optimization.
  • Offers a Pandas-like API for ease of use.

Pricing: Polars is free and open-source under the MIT license. Users only need to consider the costs of the computing resources used to process data with Polars.

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AI Tools for Dashboarding and Reports

Tableau

Tableau is a top tool for data visualization and business intelligence. It aids users in visualizing and comprehending their data. This AI tool for data science workflow enables the development of interactive and easily shareable dashboards, streamlining the process of analyzing data and uncovering valuable insights.

Key Features:

  • Create interactive and visually appealing dashboards.
  • Connects to various data sources, including databases, spreadsheets, cloud services, and big data platforms.
  • Tools for data cleaning, blending, and transformation.
  • Built-in analytics functions, including trend lines, forecasting, and statistical summaries.
  • Share dashboards and collaborate with others through Tableau Server or Tableau Online.
  • Access and interact with dashboards on mobile devices.
  • Integrates with other tools and platforms, including R and Python for advanced analytics.

Pricing: Tableau offers several pricing options:

  • Tableau Public: Free version for creating and sharing public dashboards.
  • Tableau Desktop: $70 per user per month
  • Tableau Server: $35 per user per month
  • Tableau Online: $42 per user per month
  • Tableau Creator, Explorer, and Viewer Plans: Tailored to different user needs, ranging from $12 to $70 per user per month.

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Power BI

Microsoft’s Power BI is a business analytics service. It offers interactive visualizations and business intelligence features with a user-friendly interface for building reports and dashboards.

Key Features:

  • Make interactive dashboards and reports, then distribute them.
  • Establishes connections with several data sources, such as cloud-based data services, Excel, and SQL databases.
  • Enhanced data modeling capabilities with Power Query and DAX (Data Analysis Expressions).
  • Incorporates machine learning and AI capabilities for forecasts and insights.
  • Team members may work together in real time by sharing dashboards and reports.
  • Access and interact with Power BI reports on mobile devices.

Pricing: Power BI offers several pricing options:

  • Power BI Desktop: Free for individual use.
  • Power BI Pro: $9.99 per user per month.
  • Power BI Premium:  $20 per user per month or $4,995 per monthly capacity.

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AI Tools to Increase Productivity

ChatGPT

ChatGPT is an AI language model by OpenAI that has been revolutionary since its launch. This AI tool for data science workflow is commonly utilized for conversational AI, content generation, and other purposes.

Key Features:

  • Can understand and generate text across a wide range of topics.
  • Assists in generating articles, summaries, and other written content.
  • Helps with writing and debugging code.
  • Fine-tuning is available for specific applications and industries.

Pricing: It has free and pro versions ($20 per month).

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Perplexity AI

Perplexity AI is an AI chatbot. It was created to respond to queries and offer details in a human-like approach. It uses sophisticated NLP to comprehend and answer user inquiries.

Key Features:

  • Provides accurate and relevant answers to user queries.
  • Engages users in interactive and natural-sounding conversations.
  • It can be integrated into websites, applications, and other platforms.
  • Utilizes a wide range of data sources to provide comprehensive answers.
  • This can be customized to suit specific business needs and industries.

Pricing: Perplexity AI typically offers custom pricing based on the client’s needs and usage requirements. Pricing details are often provided upon request and may vary depending on the scope and scale of implementation.

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Conclusion

As data science advances, practitioners now have access to stronger and more flexible tools and platforms. The AI tools for data science workflow offer complete solutions for different data science activities, including model creation, deployment, data visualization, and productivity improvement. Organizations can greatly improve their data science workflows by choosing the appropriate blend of tools, resulting in improved insights, streamlined processes, and increased success in their data-driven projects.

Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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