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BigML VS Databricks Unified Analytics Platform

Compare BigML VS Databricks Unified Analytics Platform 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.

Databricks Unified Analytics Platform logo Databricks Unified Analytics Platform

One platform for accelerating data-driven innovation across data engineering, data science & business analytics
  • BigML Landing page
    Landing page //
    2022-10-08
  • Databricks Unified Analytics Platform Landing page
    Landing page //
    2023-07-11

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.

Databricks Unified Analytics Platform features and specs

  • Scalability
    Databricks is built on Apache Spark, which allows for easy scaling of data processing and analytics operations across large datasets.
  • Integrated Environment
    Provides a unified analytics platform that combines data engineering, data science, and data warehouse capabilities, simplifying workflows.
  • Collaborative Workspace
    Enables collaboration between data engineers, data scientists, and analysts with its interactive notebooks and real-time collaboration features.
  • Lakehouse Architecture
    Combines the best features of data lakes and data warehouses, providing structured transactional data access over unstructured data.
  • Support for Multiple Languages
    Offers support for multiple programming languages such as Python, R, SQL, and Scala, making it versatile for different users.

Possible disadvantages of Databricks Unified Analytics Platform

  • Complexity
    Despite its powerful features, the platform can be complex to set up and manage, particularly for teams unfamiliar with similar environments.
  • Cost
    The platform can become expensive, especially when scaling operations and running large workloads continuously.
  • Learning Curve
    New users might face a steep learning curve, requiring training and practice to use the platform effectively.
  • Vendor Lock-In
    Using proprietary tools and integrations could lead to dependency on Databricks, making it harder to switch to other solutions in the future.
  • Limited Offline Features
    As a cloud-native platform, Databricks relies heavily on internet connectivity, lacking robust offline features for some use cases.

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!

Databricks Unified Analytics Platform videos

No Databricks Unified Analytics Platform videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to BigML and Databricks Unified Analytics Platform)

BigML

Databricks Unified Analytics Platform

Data Science And Machine Learning
66 66%
Data Science And Machine Learning
34% 34
Office & Productivity
0 0%
Office & Productivity
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Development
0 0%
Development
100% 100

User comments

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Social recommendations and mentions

Based on our record, BigML should be more popular than Databricks Unified Analytics Platform. 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

Databricks Unified Analytics Platform mentions (1)

  • Should I replicate all our transactional DB to Redshift?
    See more here: https://databricks.com/product/data-lakehouse. Source: about 3 years ago

What are some alternatives?

When comparing BigML and Databricks Unified Analytics Platform, 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.

Saturn Cloud - ML in the cloud. Loved by Data Scientists, Control for IT. Advance your business's ML capabilities through the entire experiment tracking lifecycle. Available on multiple clouds: AWS, Azure, GCP, and OCI.

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

Amazon SageMaker - Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.

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

Azure Synapse Analytics - Get started with Azure SQL Data Warehouse for an enterprise-class SQL Server experience. Cloud data warehouses offer flexibility, scalability, and big data insights.

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