Software Alternatives & Reviews

Quantiacs VS MCenter

Compare Quantiacs VS MCenter and see what are their differences

Quantiacs logo Quantiacs

Earn money by creating trading algorithms in your spare time

MCenter logo MCenter

Machine Learning Operationalization
  • Quantiacs Landing page
    Landing page //
    2023-06-25
  • MCenter Landing page
    Landing page //
    2021-08-03

Quantiacs videos

Quantitative Finance | Machine Learning in Trading | Quantiacs | Eric Hamer

More videos:

  • Review - Difference between Quantopian Quantiacs Quantconnect

MCenter videos

MCenter MIS Macedonia

Category Popularity

0-100% (relative to Quantiacs and MCenter)
Productivity
100 100%
0% 0
Data Science And Machine Learning
Tech
100 100%
0% 0
Data Science Notebooks
0 0%
100% 100

User comments

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Social recommendations and mentions

Based on our record, Quantiacs seems to be more popular. It has been mentiond 1 time 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.

Quantiacs mentions (1)

MCenter mentions (0)

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

What are some alternatives?

When comparing Quantiacs and MCenter, you can also consider the following products

Bytemine - A prediction and analysis platform for millennial investors.

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.

Algorithm Visualizer - Write down your algorithm to be visualized

Datatron - Datatron automates the deployment, monitoring, governance, and validation of your machine learning models in scikit-learn, TensorFlow, Keras, Pytorch, R, H20 and SAS

BestFit: Gym Workout & Fitness - The app provides individual training plans with an algorithm

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.