Software Alternatives, Accelerators & Startups

Numericcal VS Iterative.ai

Compare Numericcal VS Iterative.ai and see what are their differences

Numericcal logo Numericcal

Machine Learning Operationalization

Iterative.ai logo Iterative.ai

Iterative removes friction from managing datasets and ML models and introduces seamless data scientists collaboration.
  • Numericcal Landing page
    Landing page //
    2023-05-15
  • Iterative.ai Landing page
    Landing page //
    2023-08-18

Numericcal features and specs

  • Ease of Use
    Numericcal provides a user-friendly interface that simplifies complex calculations for users of various skill levels.
  • Comprehensive Tools
    The platform offers a wide range of calculation tools that cover diverse fields, making it versatile for different types of users.
  • Accessibility
    Being a web-based platform, Numericcal is accessible from anywhere with an internet connection, facilitating remote work and collaboration.
  • Regular Updates
    The platform receives frequent updates and improvements, ensuring that users have access to the latest features and security measures.

Possible disadvantages of Numericcal

  • Limited Offline Access
    As a web-based tool, Numericcal requires an internet connection, limiting access for users who need offline functionality.
  • Potential Learning Curve
    Although user-friendly, new users may still require time to familiarize themselves with the range of features available on the platform.
  • Subscription Costs
    Access to advanced features and tools may require a subscription, which could be a barrier for users or organizations with limited budgets.

Iterative.ai features and specs

  • Version Control with DVC
    Iterative.ai leverages Data Version Control (DVC) which allows for effective versioning of data and models, ensuring reproducibility and traceability in machine learning workflows.
  • Integration with Existing Tools
    It provides seamless integration with existing version control systems like Git, which allows data scientists to manage code, data, and models in a familiar environment.
  • Scalability
    The platform supports scalable machine learning operations by enabling users to manage datasets of any size and execute experiments efficiently.
  • Open Source
    As an open-source solution, Iterative.ai promotes transparency and community involvement, which can be beneficial for collaboration and gaining community-driven improvements.

Possible disadvantages of Iterative.ai

  • Learning Curve
    New users may face a learning curve when adapting to the unique features of Iterative.ai, especially if they are not familiar with version control systems.
  • Complexity for Small Projects
    For smaller projects, the features of Iterative.ai might be too robust, potentially complicating simple workflows with its advanced functionalities.
  • Resource Requirements
    Using Iterative.ai to scale operations can require significant computational resources, which might be a limitation for teams with constrained resources.
  • Limited Proprietary Support
    Although open source provides many advantages, organizations needing extensive proprietary support might find this limiting with Iterative.ai’s current service offerings.

Numericcal videos

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Iterative.ai videos

Reimagining DevOps for ML by Elle O'Brien, Iterative.ai

Category Popularity

0-100% (relative to Numericcal and Iterative.ai)
Data Science And Machine Learning
Data Science Notebooks
44 44%
56% 56
Machine Learning Tools
47 47%
53% 53
Machine Learning
30 30%
70% 70

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Numericcal and Iterative.ai

Numericcal Reviews

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Iterative.ai Reviews

  1. Ryan Raposo
    · Software Developer at Self-employed ·
    Rare

    The people at iterative.ai are special.

    Its hard to describe quickly, but if you're a potential client or employee--you could easily go your entire career unaware that groups like this exist.

    Their tools (like DVC) are exceptional, but I write this review because one need only interact with the people there to understand why they're execptional.

    The culture there is one that can only exist when the founding talent is top-tier. The experience you'll have, though, is so much more than that.

    Recommend whole-heatedly.

    👍 Pros:    Constantly improving|Quality|Community

Social recommendations and mentions

Based on our record, Iterative.ai seems to be more popular. It has been mentiond 6 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.

Numericcal mentions (0)

We have not tracked any mentions of Numericcal yet. Tracking of Numericcal recommendations started around Mar 2021.

Iterative.ai mentions (6)

  • Work with Google Drive files locally
    PyDrive2 is am open-source python package maintained by the awesome people at Iterative. And it is very easy to install:. - Source: dev.to / over 2 years ago
  • Any MLOps platform you use?
    These three are made by Iterative.ai, and seem like very clean implementations of MLOps tooling - especially if you aren't dealing with massive data. https://iterative.ai/. Source: over 2 years ago
  • How does your data science team collaborate?
    For what it's worth. (Full disclosure: I'm the community manager at Iterative (DVC,et.al.) Just wanted to make you aware of our online course (free) that we created specifically for Data Scientists (https://learn.iterative.ai). We know that bridging the gap between prototype to production/ jupyter notebook to reproducible/software engineering compatible, is a challenge. That's why we created the course. To also... Source: almost 3 years ago
  • Advice about Infra and IaC
    What do you think of iterative.ai tools like dvc or cml? I have no direct experience, but I am looking at setting up something similar to what you need for a personal project. Source: almost 3 years ago
  • TPI - Terraform provider for ML/AI & self-recovering spot-instances
    Hey all, we (at iterative.ai) are launching TPI - Terraform Provider Iterative https://github.com/iterative/terraform-provider-iterative. Source: about 3 years ago
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What are some alternatives?

When comparing Numericcal and Iterative.ai, you can also consider the following products

Algorithmia - Algorithmia makes applications smarter, by building a community around algorithm development, where state of the art algorithms are always live and accessible to anyone.

5Analytics - The 5Analytics AI platform enables you to use artificial intelligence to automate important commercial decisions and implement digital business models.

MCenter - Machine Learning Operationalization

neptune.ai - Neptune brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed and shared with others. Works with all common technologies and integrates with other tools.

Spell - Deep Learning and AI accessible to everyone

Managed MLflow - Managed MLflow is built on top of MLflow, an open source platform developed by Databricks to help manage the complete Machine Learning lifecycle with enterprise reliability, security, and scale.