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BigML VS Microsoft Academic Knowledge API

Compare BigML VS Microsoft Academic Knowledge API and see what are their differences

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BigML logo BigML

BigML's goal is to create a machine learning service extremely easy to use and seamless to integrate.

Microsoft Academic Knowledge API logo Microsoft Academic Knowledge API

Tap into the wealth of academic content in the Microsoft Academic Graph using the Academic Knowledge API:
  • BigML Landing page
    Landing page //
    2022-10-08
  • Microsoft Academic Knowledge API Landing page
    Landing page //
    2023-05-15

BigML features and specs

  • User-Friendly Interface
    BigML offers an intuitive web-based interface that makes it easy for users to build and deploy machine learning models without deep technical knowledge.
  • Wide Range of Algorithms
    It supports various machine learning algorithms, including regression, classification, clustering, and anomaly detection, catering to diverse use cases.
  • Ease of Integration
    BigML provides robust API support, allowing seamless integration with other applications and systems for streamlined workflows.
  • Visualization Tools
    The platform includes powerful visualization tools that help in understanding data, model performance, and results, aiding in better decision-making.
  • Scalability
    BigML's cloud-based infrastructure allows it to scale easily, handling large datasets and complex models efficiently.
  • Automated Workflows
    It offers automation features like WhizzML for creating automated workflows and advanced scripts, making repetitive tasks simpler.

Possible disadvantages of BigML

  • Cost
    The pricing structure can be a limiting factor for startups or individual users, especially when dealing with large amounts of data.
  • Limited Customization
    While the platform offers many pre-built algorithms, there is limited scope for customization compared to building models from scratch using open-source libraries.
  • Learning Curve
    Despite its user-friendly design, there can be a learning curve for those unfamiliar with machine learning concepts, particularly for advanced features.
  • Dependency on Internet
    As a cloud-based service, users need a reliable internet connection to access and utilize BigML's features, which can be a drawback in areas with poor connectivity.
  • Data Privacy Concerns
    Using a cloud-based service can raise data privacy and security concerns, particularly for sensitive or proprietary data.

Microsoft Academic Knowledge API features and specs

  • Rich Dataset
    The Microsoft Academic Knowledge API provides access to a vast amount of academic data, including publications, authors, journals, and conferences, which can enhance research and academic analysis.
  • Advanced Search Capabilities
    The API offers advanced search features that allow users to conduct complex queries, providing detailed information and insights for specific research needs.
  • Graph-based Data
    Utilizes a graph-based approach for representing relationships among academic entities, aiding in the exploration of connections within academic research.
  • Regular Updates
    The API data is regularly updated, ensuring that users have access to the latest research publications and academic information.

Possible disadvantages of Microsoft Academic Knowledge API

  • Discontinuation
    The Microsoft Academic services have been phased out by the end of 2021, which limits long-term availability and support for the API.
  • Access Restrictions
    Users may face limitations or require specific authorization to access certain datasets, which can hinder seamless integration and usage.
  • Learning Curve
    The API requires users to have a certain level of technical expertise to implement and use effectively, posing challenges for individuals unfamiliar with API integration.
  • Limited Scope
    While comprehensive, the API's dataset may not cover every niche or new academic field exhaustively, potentially missing out on emerging research areas.

Analysis of BigML

Overall verdict

  • BigML is a good choice for users seeking an accessible and efficient machine learning platform. Its combination of ease of use, flexibility, and robust features allows for effective data analysis and model deployment, making it suitable for many use cases.

Why this product is good

  • BigML is a popular machine learning platform known for its user-friendly interface and comprehensive suite of tools that cater to both beginners and experts. It offers a wide range of machine learning models and allows for seamless integration with other tools and workflows. Users appreciate its ease of use, scalability, and ability to handle various types of data. Additionally, BigML provides extensive documentation and support, making it an attractive option for those looking to implement machine learning solutions without extensive coding knowledge.

Recommended for

  • Data scientists and analysts looking for an intuitive platform to build and deploy models.
  • Businesses aiming to integrate machine learning into their operations without a steep learning curve.
  • Educators and students who wish to explore machine learning concepts hands-on.
  • Developers needing a scalable solution with ample API support for custom applications.
  • Organizations looking for a reliable and secure cloud-based machine learning solution.

BigML videos

+ Add

BigML is Machine Learning for Everyone

More videos:

  • Review - BigML Spring 2016 Webinar - WhizzML!

Microsoft Academic Knowledge API videos

No Microsoft Academic Knowledge API videos yet. You could help us improve this page by suggesting one.

Add video

Category Popularity

0-100% (relative to BigML and Microsoft Academic Knowledge API)

BigML

Microsoft Academic Knowledge API

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
NLP And Text Analytics
0 0%
NLP And Text Analytics
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Spreadsheets
0 0%
Spreadsheets
100% 100

User comments

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Social recommendations and mentions

Based on our record, BigML seems to be more popular. It has been mentiond 2 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

BigML mentions (2)

  • Free for dev - list of software (SaaS, PaaS, IaaS, etc.)
    Bigml.com — Hosted machine learning algorithms. Unlimited free tasks for development, limit of 16 MB data/task. - Source: dev.to / almost 4 years ago
  • Theory: The price action was intentionally manipulated to prevent any AI from being able to predict it. First time this model shows as flat. Forever.
    They know the website is bigml.com it's possible they have many magnitudes better algorithms to predict this shit. And it's also possible they paid some quants to come up with price action that just completely fucks with BigML's algorithm entirely to make it look flat. Source: about 4 years ago

Microsoft Academic Knowledge API mentions (0)

We have not tracked any mentions of Microsoft Academic Knowledge API yet. Tracking of Microsoft Academic Knowledge API recommendations started around Mar 2021.

What are some alternatives?

When comparing BigML and Microsoft Academic Knowledge API, you can also consider the following products

RapidMiner - RapidMiner is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment.

Microsoft Bing Autosuggest API - Show users intelligent search suggestions with the Bing Autosuggest API from Microsoft Azure. Test out the autocomplete API to see how it works.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Amazon Comprehend - Discover insights and relationships in text

Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.

FuzzyWuzzy - FuzzyWuzzy is a Fuzzy String Matching in Python that uses Levenshtein Distance to calculate the differences between sequences.

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