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Data Science Platform Market Size 2025-2029

The data science platform market size is forecast to increase by USD 763.9 million at a CAGR of 40.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the integration of artificial intelligence (AI) and machine learning (ML). This enhancement enables more advanced data analysis and prediction capabilities, making data science platforms an essential tool for businesses seeking to gain insights from their data. Another trend shaping the market is the emergence of containerization and microservices in platforms. This development offers increased flexibility and scalability, allowing organizations to efficiently manage their projects. 
However, the use of platforms also presents challenges, particularly In the area of data privacy and security. Ensuring the protection of sensitive data is crucial for businesses, and platforms must provide strong security measures to mitigate risks. In summary, the market is witnessing substantial growth due to the integration of AI and ML technologies, containerization, and microservices, while data privacy and security remain key challenges.

What will be the Size of the Data Science Platform Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing demand for advanced data analysis capabilities in various industries. Cloud-based solutions are gaining popularity as they offer scalability, flexibility, and cost savings. The market encompasses the entire project life cycle, from data acquisition and preparation to model development, training, and distribution. Big data, IoT, multimedia, machine data, consumer data, and business data are prime sources fueling this market's expansion. Unstructured data, previously challenging to process, is now being effectively managed through tools and software. Relational databases and machine learning models are integral components of platforms, enabling data exploration, preprocessing, and visualization.
Moreover, Artificial intelligence (AI) and machine learning (ML) technologies are essential for handling complex workflows, including data cleaning, model development, and model distribution. Data scientists benefit from these platforms by streamlining their tasks, improving productivity, and ensuring accurate and efficient model training. The market is expected to continue its growth trajectory as businesses increasingly recognize the value of data-driven insights.

How is this Data Science Platform Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  On-premises
  Cloud


Component

  Platform
  Services


End-user

  BFSI
  Retail and e-commerce
  Manufacturing
  Media and entertainment
  Others


Sector

  Large enterprises
  SMEs


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France


  APAC

    China
    India
    Japan


  South America

    Brazil


  Middle East and Africa

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

On-premises deployment is a traditional method for implementing technology solutions within an organization. This approach involves purchasing software with a one-time license fee and a service contract. On-premises solutions offer enhanced security, as they keep user credentials and data within the company's premises. They can be customized to meet specific business requirements, allowing for quick adaptation. On-premises deployment eliminates the need for third-party providers to manage and secure data, ensuring data privacy and confidentiality. Additionally, it enables rapid and easy data access, and keeps IP addresses and data confidential. This deployment model is particularly beneficial for businesses dealing with sensitive data, such as those in manufacturing and large enterprises. While cloud-based solutions offer flexibility and cost savings, on-premises deployment remains a popular choice for organizations prioritizing data security and control.

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The on-premises segment was valued at USD 38.70 million in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 48% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request F

Improving the accuracy of prediction on future values based on the past and current observations has been pursued by enhancing the prediction's methods, combining those methods or performing data pre-processing. In this paper, another approach is taken, namely by increasing the number of input in the dataset. This approach would be useful especially for a shorter time series data. By filling the in-between values in the time series, the number of training set can be increased, thus increasing the generalization capability of the predictor. The algorithm used to make prediction is Neural Network as it is widely used in literature for time series tasks. For comparison, Support Vector Regression is also employed. The dataset used in the experiment is the frequency of USPTO's patents and PubMed's scientific publications on the field of health, namely on Apnea, Arrhythmia, and Sleep Stages. Another time series data designated for NN3 Competition in the field of transportation is also used for benchmarking. The experimental result shows that the prediction performance can be significantly increased by filling in-between data in the time series. Furthermore, the use of detrend and deseasonalization which separates the data into trend, seasonal and stationary time series also improve the prediction performance both on original and filled dataset. The optimal number of increase on the dataset in this experiment is about five times of the length of original dataset.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Probabilistic AI: The Next Generation of Artificial Intelligence

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Extreme weather events, including fires, heatwaves, and droughts, have significant impacts on earth, environmental, and energy systems. Mechanistic and predictive understanding, as well as probabilistic risk assessment of these extreme weather events, are crucial for detecting, planning for, and responding to these extremes. Records of extreme weather events provide an important data source for understanding present and future extremes, but the existing data needs preprocessing before it can be used for analysis. Moreover, there are many nonstandard metrics defining the levels of severity or impacts of extremes. In this study, we compile a comprehensive benchmark data inventory of extreme weather events, including fires, heatwaves, and droughts. The dataset covers the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 0.5°×0.5° (~55km×55km) over the continental United States (CONUS), and a spatial resolution of 1km × 1km over the Pacific Northwest (PNW) region, together with the co-located and relevant meteorological variables. By exploring and summarizing the spatial and temporal patterns of these extremes in various forms of marginal, conditional, and joint probability distributions, we gain a better understanding of the characteristics of climate extremes. The resulting AI/ML-ready data products can be readily applied to ML-based research, fostering and encouraging AI/ML research in the field of extreme weather. This study can contribute significantly to the advancement of extreme weather research, aiding researchers, policymakers, and practitioners in developing improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of extreme weather events. Usage Notes We presented a long term (2001-2020) and comprehensive data inventory of historical extreme events with daily temporal resolution covering the separate spatial extents of CONUS (0.5°×0.5°) and PNW(1km×1km) for various applications and studies. The dataset with 0.5°×0.5° resolution for CONUS can be used to help build more accurate climate models for the entire CONUS, which can help in understanding long-term climate trends, including changes in the frequency and intensity of extreme events, predicting future extreme events as well as understanding the implications of extreme events on society and the environment. The data can also be applied for risk accessment of the extremes. For example, ML/AI models can be developed to predict wildfire risk or forecast HWs by analyzing historical weather data, and past fires or heateave , allowing for early warnings and risk mitigation strategies. Using this dataset, AI-driven risk assessment models can also be built to identify vulnerable energy and utilities infrastructure, imrpove grid resilience and suggest adaptations to withstand extreme weather events. The high-resolution 1km×1km dataset ove PNW are advantageous for real-time, localized and detailed applications. It can enhance the accuracy of early warning systems for extreme weather events, helping authorities and communities prepare for and respond to disasters more effectively. For example, ML models can be developed to provide localized HW predictions for specific neighborhoods or cities, enabling residents and local emergency services to take targeted actions; the assessment of drought severity in specific communities or watersheds within the PNW can help local authorities manage water resources more effectively.

Question Paper Solutions of chapter Data pre-processing and clean-up of Data Mining, 6th Semester , B.Tech in Computer Science & Engineering (Artificial Intelligence and Machine Learning)

Global AI Software Platforms is segmented by Application (Developing AI applications, Building machine learning models, Training AI models, Deploying AI solutions, Data analysis and preprocessing), Type (Machine learning platforms, Deep learning platforms, Natural language processing (NLP) platforms, Computer vision platforms, AI development tools) and Geography(North America, LATAM, West Europe, Central & Eastern Europe, Northern Europe, Southern Europe, East Asia, Southeast Asia, South Asia, Central Asia, Oceania, MEA)

Monitoring land cover changes is essential for understanding the environmental impact of natural processes and human activities, such as deforestation, urbanization, and agriculture. We present a comprehensive dataset covering the entire United States, spanning from 2016 to 2024. This dataset integrates multi-band imagery from the Sentinel-2 satellite with pixel-level land cover annotations from the Dynamic World dataset, providing a valuable resource for land cover change detection research. The dataset is publicly available and includes six spectral bands (Red, Green, Blue, NIR, SWIR1, SWIR2) as well as labels indicating land cover types. To address the steep learning curve associated with remote sensing, we also provide accompanying code to facilitate data loading and perform basic analyses, enabling researchers to focus on their core work without being hindered by data preprocessing challenges. This dataset facilitates research in fields such as environmental monitoring, urban planning, and climate change, offering an accessible tool for understanding landscape dynamics over time.

Please cite the following paper when using this dataset:Vanessa Su and Nirmalya Thakur, “COVID-19 on YouTube: A Data-Driven Analysis of Sentiment, Toxicity, and Content Recommendations”, Proceedings of the IEEE 15th Annual Computing and Communication Workshop and Conference 2025, Las Vegas, USA, Jan 06-08, 2025 (Paper accepted for publication, Preprint: https://arxiv.org/abs/2412.17180).Abstract:This dataset comprises metadata and analytical attributes for 9,325 publicly available YouTube videos related to COVID-19, published between January 1, 2023, and October 25, 2024. The dataset was created using the YouTube API and refined through rigorous data cleaning and preprocessing. Key Attributes of the Dataset:Video URL: The full URL linking to each video.Video ID: A unique identifier for each video.Title: The title of the video.Description: A detailed textual description provided by the video uploader.Publish Date: The date the video was published, ranging from January 1, 2023, to October 25, 2024.View Count: The total number of views per video, ranging from 0 to 30,107,100 (mean: ~59,803).Like Count: The number of likes per video, ranging from 0 to 607,138 (mean: ~1,413).Comment Count: The number of comments, varying from 1 to 25,000 (mean: ~147).Duration: Video length in seconds, ranging from 0 to 42,900 seconds (median: 137 seconds).Categories: Categorization of videos into 15 unique categories, with "News & Politics" being the most common (4,035 videos).Tags: Tags associated with each video.Language: The language of the video, predominantly English ("en").

This dataset is an RO-Crate that bundles artifacts of an AI-based computational pipeline execution. It is an example of application of the CPM RO-Crate profile, which integrates the Common Provenance Model (CPM), and the Process Run Crate profile.

As the CPM is a groundwork for the ISO 23494 Biotechnology — Provenance information model for biological material and data provenance standards series development, the resulting profile and the example is intended to be presented at one of the ISO TC275 WG5 regular meetings, and will become an input for the ISO 23494-5 Biotechnology — Provenance information model for biological material and data — Part 5: Provenance of Data Processing standard development.

Description of the AI pipeline

The goal of the AI pipeline whose execution is described in the dataset is to train an AI model to detect the presence of carcinoma cells in high resolution human prostate images. The pipeline is implemented as a set of python scripts that work over a filesystem, where the datasets, intermediate results, configurations, logs, and other artifacts are stored. In particular, the AI pipeline consists of the following three general parts:

Image data preprocessing. Goal of this step is to prepare the input dataset – whole slide images (WSIs) and their annotations – for the AI model. As the model is not able to process the entire high resolution images, the preprocessing step of the pipeline splits the WSIs into groups (training and testing). Furthermore, each WSI is broken down into smaller overlapping parts called patches. The background patches are filtered out and the remaining tissue patches are labeled according to the provided pathologists’ annotations.

AI model training. Goal of this step is to train the AI model using the training dataset generated in the previous step of the pipeline. Result of this step is a trained AI model.

AI model evaluation. Goal of this step is to evaluate the trained model performance on a dataset which was not provided to the model during the training. Results of this step are statistics describing the AI model performance.

In addition to the above, execution of the steps results in generation of log files. The log files contain detailed traces of the AI pipeline execution, such as file paths, model weight parameters, timestamps, etc. As suggested by the CPM, the logfiles and additional metadata present on the filesystem are then used by a provenance generation step that transforms available information into the CPM compliant data structures, and serializes them into files.

Finally, all these artifacts are packed together in an RO-Crate.

For the purpose of the example, we have included only a small fragment of the input image dataset in the resulting crate, as this has no effect on how the Process Run Crate and CPM RO-Crate profiles are applied to the use case. In real world execution, the input dataset would consist of terabytes of data. In this example, we have selected a representative image for each of the input dataset parts. As a result, the only difference between the real world application and this example would be that the resulting real world crate would contain more input files.

Description of the RO-Crate

Process Run Crate related aspects

The Process Run Crate profile can be used to pack artifacts of a computational workflow of which individual steps are not controlled centrally. Since the pipeline presented in this example consists of steps that are executed individually, and that the pipeline execution is not managed centrally by a workflow engine, the process run crate can be applied.

Each of the computational steps is expressed within the crate’s ro-crate-metadata.json file as a pair of elements: 1) SW used to create files; 2) specific execution of that SW. In particular, we use the SoftwareSourceCode type to indicate the executed python scripts and the CreateAction type to indicate actual executions.

As a result, the crate consists the seven following “executables”:

Three python scripts, each corresponding to a part of the pipeline: preprocessing, training, and evaluation.

Four provenance generation scripts, three of which implement the transformation of the proprietary log files generated by the AI pipeline scripts into CPM compliant provenance files. The fourth one is a meta provenance generation script.

For each of the executables, their execution is expressed in the resulting ro-crate-metadata.json using the CreateAction type. As a result, seven create-actions are present in the resulting crate.

Input dataset, intermediate results, configuration files and resulting provenance files are expressed according to the underlying RO Crate specification.

CPM RO-Crate related aspects

The main purpose of the CPM RO-Crate profile is to enable identification of the CPM compliant provenance files within a crate. To achieve this, the CPM RO-Crate profile specification prescribes specific file types for such files: CPMProvenanceFile, and CPMMetaProvenanceFile.

In this case, the RO Crate contains three CPM Compliant files, each documenting a step of the pipeline, and a single meta-provenance file. These files are generated as a result of the three provenance generation scripts that use available log files and additional information to generate the CPM compliant files. In terms of the CPM, the provenance generation scripts are implementing the concept of provenance finalization event. The three provenance generation scripts are assigned SoftwareSourceCode type, and have corresponding executions expressed in the crate using the CreateAction type.

Remarks

The resulting RO Crate packs artifacts of an execution of the AI pipeline. The scripts that implement individual steps of the pipeline and provenance generation are not included in the crate directly. The implementation scripts are hosted on github and just referenced from the crate’s ro-crate-metadata.json file to their remote location.

The input image files included in this RO-Crate are coming from the Camelyon16 dataset.

LScD (Leicester Scientific Dictionary)April 2020 by Neslihan Suzen, PhD student at the University of Leicester (ns433@leicester.ac.uk/suzenneslihan@hotmail.com)Supervised by Prof Alexander Gorban and Dr Evgeny Mirkes[Version 3] The third version of LScD (Leicester Scientific Dictionary) is created from the updated LSC (Leicester Scientific Corpus) - Version 2*. All pre-processing steps applied to build the new version of the dictionary are the same as in Version 2** and can be found in description of Version 2 below. We did not repeat the explanation. After pre-processing steps, the total number of unique words in the new version of the dictionary is 972,060. The files provided with this description are also same as described as for LScD Version 2 below.* Suzen, Neslihan (2019): LSC (Leicester Scientific Corpus). figshare. Dataset. https://doi.org/10.25392/leicester.data.9449639.v2** Suzen, Neslihan (2019): LScD (Leicester Scientific Dictionary). figshare. Dataset. https://doi.org/10.25392/leicester.data.9746900.v2[Version 2] Getting StartedThis document provides the pre-processing steps for creating an ordered list of words from the LSC (Leicester Scientific Corpus) [1] and the description of LScD (Leicester Scientific Dictionary). This dictionary is created to be used in future work on the quantification of the meaning of research texts. R code for producing the dictionary from LSC and instructions for usage of the code are available in [2]. The code can be also used for list of texts from other sources, amendments to the code may be required.LSC is a collection of abstracts of articles and proceeding papers published in 2014 and indexed by the Web of Science (WoS) database [3]. Each document contains title, list of authors, list of categories, list of research areas, and times cited. The corpus contains only documents in English. The corpus was collected in July 2018 and contains the number of citations from publication date to July 2018. The total number of documents in LSC is 1,673,824.LScD is an ordered list of words from texts of abstracts in LSC.The dictionary stores 974,238 unique words, is sorted by the number of documents containing the word in descending order. All words in the LScD are in stemmed form of words. The LScD contains the following information:1.Unique words in abstracts2.Number of documents containing each word3.Number of appearance of a word in the entire corpusProcessing the LSCStep 1.Downloading the LSC Online: Use of the LSC is subject to acceptance of request of the link by email. To access the LSC for research purposes, please email to ns433@le.ac.uk. The data are extracted from Web of Science [3]. You may not copy or distribute these data in whole or in part without the written consent of Clarivate Analytics.Step 2.Importing the Corpus to R: The full R code for processing the corpus can be found in the GitHub [2].All following steps can be applied for arbitrary list of texts from any source with changes of parameter. The structure of the corpus such as file format and names (also the position) of fields should be taken into account to apply our code. The organisation of CSV files of LSC is described in README file for LSC [1].Step 3.Extracting Abstracts and Saving Metadata: Metadata that include all fields in a document excluding abstracts and the field of abstracts are separated. Metadata are then saved as MetaData.R. Fields of metadata are: List_of_Authors, Title, Categories, Research_Areas, Total_Times_Cited and Times_cited_in_Core_Collection.Step 4.Text Pre-processing Steps on the Collection of Abstracts: In this section, we presented our approaches to pre-process abstracts of the LSC.1.Removing punctuations and special characters: This is the process of substitution of all non-alphanumeric characters by space. We did not substitute the character “-” in this step, because we need to keep words like “z-score”, “non-payment” and “pre-processing” in order not to lose the actual meaning of such words. A processing of uniting prefixes with words are performed in later steps of pre-processing.2.Lowercasing the text data: Lowercasing is performed to avoid considering same words like “Corpus”, “corpus” and “CORPUS” differently. Entire collection of texts are converted to lowercase.3.Uniting prefixes of words: Words containing prefixes joined with character “-” are united as a word. The list of prefixes united for this research are listed in the file “list_of_prefixes.csv”. The most of prefixes are extracted from [4]. We also added commonly used prefixes: ‘e’, ‘extra’, ‘per’, ‘self’ and ‘ultra’.4.Substitution of words: Some of words joined with “-” in the abstracts of the LSC require an additional process of substitution to avoid losing the meaning of the word before removing the character “-”. Some examples of such words are “z-test”, “well-known” and “chi-square”. These words have been substituted to “ztest”, “wellknown” and “chisquare”. Identification of such words is done by sampling of abstracts form LSC. The full list of such words and decision taken for substitution are presented in the file “list_of_substitution.csv”.5.Removing the character “-”: All remaining character “-” are replaced by space.6.Removing numbers: All digits which are not included in a word are replaced by space. All words that contain digits and letters are kept because alphanumeric characters such as chemical formula might be important for our analysis. Some examples are “co2”, “h2o” and “21st”.7.Stemming: Stemming is the process of converting inflected words into their word stem. This step results in uniting several forms of words with similar meaning into one form and also saving memory space and time [5]. All words in the LScD are stemmed to their word stem.8.Stop words removal: Stop words are words that are extreme common but provide little value in a language. Some common stop words in English are ‘I’, ‘the’, ‘a’ etc. We used ‘tm’ package in R to remove stop words [6]. There are 174 English stop words listed in the package.Step 5.Writing the LScD into CSV Format: There are 1,673,824 plain processed texts for further analysis. All unique words in the corpus are extracted and written in the file “LScD.csv”.The Organisation of the LScDThe total number of words in the file “LScD.csv” is 974,238. Each field is described below:Word: It contains unique words from the corpus. All words are in lowercase and their stem forms. The field is sorted by the number of documents that contain words in descending order.Number of Documents Containing the Word: In this content, binary calculation is used: if a word exists in an abstract then there is a count of 1. If the word exits more than once in a document, the count is still 1. Total number of document containing the word is counted as the sum of 1s in the entire corpus.Number of Appearance in Corpus: It contains how many times a word occurs in the corpus when the corpus is considered as one large document.Instructions for R CodeLScD_Creation.R is an R script for processing the LSC to create an ordered list of words from the corpus [2]. Outputs of the code are saved as RData file and in CSV format. Outputs of the code are:Metadata File: It includes all fields in a document excluding abstracts. Fields are List_of_Authors, Title, Categories, Research_Areas, Total_Times_Cited and Times_cited_in_Core_Collection.File of Abstracts: It contains all abstracts after pre-processing steps defined in the step 4.DTM: It is the Document Term Matrix constructed from the LSC[6]. Each entry of the matrix is the number of times the word occurs in the corresponding document.LScD: An ordered list of words from LSC as defined in the previous section.The code can be used by:1.Download the folder ‘LSC’, ‘list_of_prefixes.csv’ and ‘list_of_substitution.csv’2.Open LScD_Creation.R script3.Change parameters in the script: replace with the full path of the directory with source files and the full path of the directory to write output files4.Run the full code.References[1]N. Suzen. (2019). LSC (Leicester Scientific Corpus) [Dataset]. Available: https://doi.org/10.25392/leicester.data.9449639.v1[2]N. Suzen. (2019). LScD-LEICESTER SCIENTIFIC DICTIONARY CREATION. Available: https://github.com/neslihansuzen/LScD-LEICESTER-SCIENTIFIC-DICTIONARY-CREATION[3]Web of Science. (15 July). Available: https://apps.webofknowledge.com/[4]A. Thomas, "Common Prefixes, Suffixes and Roots," Center for Development and Learning, 2013.[5]C. Ramasubramanian and R. Ramya, "Effective pre-processing activities in text mining using improved porter’s stemming algorithm," International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, no. 12, pp. 4536-4538, 2013.[6]I. Feinerer, "Introduction to the tm Package Text Mining in R," Accessible en ligne: https://cran.r-project.org/web/packages/tm/vignettes/tm.pdf, 2013.

Global AI-Enabled Testing Tools Market was valued at $437.56 Million in 2023, and is projected to reach $USD 1693.95 Million by 2032, at a CAGR of 16.23%.

This repository contains part 7/7 of the full dataset used for the models of the preprint "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy".

This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2018-2019. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.

To use the data, clone the corresponding repository, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.

According to Cognitive Market Research, the global Automated Machine Learning (AutoML) market size is USD 989.95 million in 2024 and will expand at a compound annual growth rate (CAGR) of 28.1% from 2024 to 2031. Market Dynamics of Automated Machine Learning (AutoML) Market

Key Drivers for Automated Machine Learning (AutoML) Market

Democratization of Machine Learning to Increase the Demand Globally - One key driver in the Automated Machine Learning (AutoML) market is the democratization of ML. Automated Machine Learning (AutoML) enables non-experts to leverage machine learning techniques without requiring extensive technical expertise. By automating the process of model selection, hyperparameter tuning, and feature engineering, AutoML platforms empower a broader range of users, including business analysts and domain experts, to build and deploy machine learning models effectively. Scalability and Efficiency- AutoML streamlines and accelerates the machine learning workflow, reducing the time and resources needed to develop and deploy models. This scalability and efficiency drive adoption across industries, allowing organizations to rapidly innovate, iterate, and scale their machine-learning initiatives to address diverse business challenges.

Key Restraints for Automated Machine Learning (AutoML) Market

Complexity- The complexity of implementing and integrating AutoML solutions into existing workflows can limit market growth, as it requires significant expertise and resources to deploy and manage these technologies effectively. Data Quality and Availability- The availability of high-quality training data and the need for large, diverse datasets pose challenges for AutoML adoption, as inadequate or biased data can lead to suboptimal model performance and hinder market expansion. Introduction of the Automated Machine Learning (AutoML) Market

The automated machine learning (AutoML) market is a rapidly evolving sector at the intersection of artificial intelligence (AI) and data science, revolutionizing the way organizations develop machine learning models. AutoML solutions streamline the machine learning pipeline, automating tasks such as data preprocessing, feature engineering, model selection, hyperparameter tuning, and model deployment. This democratization of machine learning enables users with varying levels of expertise to harness the power of AI without extensive programming or data science knowledge. The market offers a diverse range of AutoML platforms, tools, and services tailored to different use cases and industries, catering to the growing demand for scalable, efficient, and accessible AI solutions. With the exponential growth of data and the increasing importance of AI in business operations, the AutoML market is poised for substantial growth, empowering organizations to unlock valuable insights, optimize processes, and drive innovation through automated machine learning technologies.

Analysis of ‘Motorcycle Gears Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/shivamb/motorcycle-gears-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Motorcycle Gears Dataset

RevZilla.com, also known as RevZilla, is an online motorcycle-gear retailer based in Philadelphia, Pennsylvania. The company sells motorcycle gear, parts and accessories, and was founded in 2007. This dataset contains the details of about 1000 motorcycle gears and accessories along with meta information such as - price, category, color, brand etc. This dataset can be used for retail-related use cases such as content-based product recommendations, or inventory optimization.

Interesting Tasks Ideas

• Identify the most expensive brands (and least expensive)
• Create a recommender system to suggest relevant gears and accessories
• Create a network of gears similarity

Acknowledgements

The dataset was scraped from Revzilla.com by crawlfeed team. I later performed data cleaning and data preprocessing to make it in usable form.

--- Original source retains full ownership of the source dataset ---

Problem Statement

👉 Download the case studies here

Traditional education systems often fail to address the diverse learning needs of students. A leading EdTech company faced challenges in providing tailored educational experiences, leading to decreased student engagement and inconsistent learning outcomes. The company sought an innovative solution to create adaptive learning platforms that cater to individual learning styles and pace.

Challenge

Creating a personalized education platform involved overcoming the following challenges:

Analyzing diverse datasets, including student performance, engagement metrics, and learning preferences.

Designing adaptive content delivery that adjusts to each student’s progress in real-time.

Maintaining a balance between personalized learning and curriculum standards.

Solution Provided

An adaptive learning system was developed using machine learning algorithms and AI-driven educational software. The solution was designed to:

Analyze student data to identify strengths, weaknesses, and preferred learning styles.

Provide personalized learning paths, including targeted content, quizzes, and feedback.

Continuously adapt content delivery based on real-time performance and engagement metrics.

Development Steps

Data Collection

Aggregated student data, including assessment scores, engagement patterns, and interaction histories, from existing learning management systems.

Preprocessing

Cleaned and structured data to identify trends and learning gaps, ensuring accurate input for machine learning models.

Model Training

Built recommendation algorithms to suggest tailored learning materials based on student progress. Developed predictive models to identify students at risk of falling behind and provide timely interventions.

Validation

Tested the system with diverse student groups to ensure its adaptability and effectiveness in various educational contexts.

Deployment

Integrated the adaptive learning platform with the company’s existing educational software, ensuring seamless operation across devices.

Monitoring & Improvement

Established a feedback loop to refine algorithms and enhance personalization based on new data and evolving student needs.

Results

Enhanced Student Engagement

The platform increased student participation by providing interactive and tailored learning experiences.

Improved Learning Outcomes

Personalized learning paths helped students grasp concepts more effectively, resulting in better performance across assessments.

Tailored Educational Experiences

The adaptive system offered individualized support, catering to students with diverse needs and learning styles.

Proactive Support

Predictive insights enabled educators to identify struggling students early and provide necessary interventions.

Scalable Solution

The platform scaled efficiently to accommodate thousands of students, ensuring consistent quality and personalization.

THE MNIST DATABASE of handwritten digits

mnist 手寫辨識資料 http://yann.lecun.com/exdb/mnist/

mnist in csv 格式，出自於kaggle https://www.kaggle.com/oddrationale/mnist-in-csv/data

• Yann LeCun, Courant Institute, NYU
• Corinna Cortes, Google Labs, New York
• Christopher J.C. Burges, Microsoft Research, Redmond

The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.

It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.

Abstract

Single-cell research faces challenges in accurately annotating cell types at high resolution, especially when dealing with large-scale datasets and rare cell populations. To address this, foundation models like scGPT offer flexible, scalable solutions by leveraging transformer-based architectures. This protocol provides a comprehensive guide to fine-tuning scGPT for cell-type classification in single-cell RNA sequencing (scRNA-seq) data. We demonstrate how to fine-tune scGPT on a custom retina dataset, highlighting the model’s efficiency in handling complex data and improving annotation accuracy achieving 99.5% F1-score. This protocol automates key steps, including data preprocessing, model fine-tuning, and evaluation. This protocol enables researchers to efficiently deploy scGPT for their own datasets. The provided tools, including a command-line script and Jupyter Notebook, simplify the customization and exploration of the model, proposing an accessible workflow for users with minimal Python and Linux knowledge. The protocol offers an off-the-shell solution of high-precision cell-type annotation using scGPT for researchers with intermediate bioinformatics.

Problem Statement

👉 Download the case studies here

A large enterprise faced challenges in managing the administrative workload for its employees. Tasks such as scheduling meetings, organizing emails, and retrieving documents consumed significant time, reducing productivity and detracting from core responsibilities. The organization sought a solution to automate these tasks, allowing employees to focus on higher-value activities.

Challenge

Implementing AI-driven virtual assistants for enterprise use required addressing the following challenges:

Enabling the virtual assistant to understand and process diverse user requests in natural language.

Integrating the solution with enterprise tools such as calendars, email clients, and document management systems.

Ensuring data security and privacy while handling sensitive corporate information.

Solution Provided

An AI-driven virtual personal assistant was developed using Natural Language Processing (NLP) and machine learning. The solution was designed to:

Understand and process employee requests through natural language interfaces.

Automate tasks such as meeting scheduling, email management, and document retrieval.

Seamlessly integrate with enterprise systems and provide secure access to data and resources.

Development Steps

Data Collection

Gathered diverse datasets, including corporate email structures, calendar patterns, and task management workflows, to train the AI models.

Preprocessing

Cleaned and structured data to enable accurate intent recognition and task execution, while ensuring compliance with privacy standards.

Model Development

Built NLP models to understand and process employee commands in natural language. Developed machine learning algorithms for task prioritization and resource optimization.

Testing and Validation

Tested the assistant in real-world scenarios to ensure high accuracy in understanding requests and completing tasks.

Deployment

Rolled out the solution across the enterprise, enabling employees to access the assistant via desktop, mobile, and voice interfaces.

Continuous Monitoring & Improvement

Established a feedback loop to refine NLP capabilities and improve task accuracy based on user interactions.

Results

Increased Employee Productivity

Automating repetitive tasks allowed employees to focus on core responsibilities, enhancing overall productivity.

Reduced Administrative Workload

The virtual assistant handled tasks such as scheduling, email sorting, and document retrieval, significantly reducing administrative burdens.

Enhanced Workplace Efficiency

Faster task execution and streamlined workflows improved operational efficiency across teams.

Improved User Experience

Employees reported a smoother and more intuitive experience with the virtual assistant, increasing engagement and satisfaction.

Scalable and Secure Solution

The system scaled effortlessly to accommodate new users and integrated with enterprise security protocols to safeguard sensitive data.

Description:

👉 Download the dataset here

This dataset consists of 20,000 image-text pairs designed to aid in training machine learning models capable of extracting text from scanned Telugu documents. The images in this collection resemble “scans” of documents or book pages, paired with their corresponding text sequences. This dataset aims to reduce the necessity for complex pre-processing steps, such as bounding-box creation and manual text labeling, allowing models to directly map from image inputs to textual sequences.

The main objective is to train models to handle real-world scans, particularly those from aged or damaged documents, without needing to design elaborate computer vision algorithms. The dataset focuses on minimizing the manual overhead involved in traditional document processing methods, making it a valuable resource for tasks like optical character recognition (OCR) in low-resource languages like Telugu.

Download Dataset

Key Features:

Wide Variety of Realistic Scans: The dataset includes images mimicking realistic variations, such as aging effects, smudges, and incomplete characters, commonly found in physical book scans or older documents.

Image-Text Pairing: Each image is linked with its corresponding pure text sequence, allowing models to learn efficient text extraction without additional manual preprocessing steps.

Customizable Data Generation: The dataset is built using open-source generator code, which provides flexibility for users to adjust hundreds of parameters. It supports custom corpora, so users can replace the probabilistically generated “gibberish” text with actual texts relevant to their use cases.

Scalable and Efficient: Thanks to parallelized processing, larger datasets can be generated rapidly. Users with powerful computational resources can expand the dataset size to hundreds of thousands or even millions of pairs, making it an adaptable resource for large-scale AI training.

Multi-Script Support: The generator code can easily be extended to other scripts, including different Indic languages or even non-Indic languages, by modifying the Unicode character set and adjusting parameters such as sentence structure and paragraph lengths.

Dataset Applications:

This dataset is especially useful for developing OCR systems that handle Telugu language documents. However, the data generation process is flexible enough to extend to other Indic languages and non-Indic scripts, making it a versatile resource for cross-lingual and multi-modal research in text extraction, document understanding, and AI-driven translation.

This dataset is sourced from Kaggle.

Walnut and Heart CT Data corresponding to Noisier2Inverse consist of high-resolution computed tomography (CT) scans used for evaluating deep learning-based image reconstruction under severe noise conditions. The dataset includes walnut CT scans from controlled experimental settings and clinical cardiac CT images. The Walnut data stems from this source: https://paperswithcode.com/dataset/cbct-walnut, and the Heart CT data is processed in python before, and is provided in .pt format. The origintal image stem from https://www.kaggle.com/datasets/abbymorgan/heart-ct. The exact preprocessing and also how the noise is put on the data can be found on our Github https://github.com/Nadja1611/Noisier2Inverse-Joint-Denoising-and-Reconstruction-of-correlated-noise