Alternative products to mlblocks

13 alternative and related products to mlblocks


A no-code Machine Learning solution. Made by teenagers.

As students in high school, we realised that even though it is amazing, machine learning is really difficult to implement into projects.

To remove the hassle we faced ourselves, we created MLBlocks.

-Train models with only 50 images per class

-Create them without expensive GPUs

-Deploy on any platform through our API


13 Alternatives to mlblocks

Deep Learning and AI accessible to everyone

Spell is The most flexible and powerful end-to-end platform for ML and deep learning engineering.

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In 2016, Serkan Piantino packed up his desk at Facebook with hopes to move on to something new. The former Director of Engineering for Faceboook AI Research had every intention to keep working on AI, but quickly realized a huge issue. Unless you're under the umbrella of one of these big tech ...
10 Alternatives to Spell

Train Neural Networks without a line of code

MakeML is an easy to use MacOS app for iOS devs, who want to try out machine learning in their apps. The app is made in a way that no Python development nor data scientist background are needed. There are 2 model types available for training: Object Detection and Style Transfer.

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Before we jump in, a few words about MakeML. The app runs on macOS 10.14+. It helps you to create object detection Core ML Models without writing a line of code. The app manages Python dependencies, data preparation, and visualizes the training process.
4 Alternatives to MakeML

A curated collection of machine learning projects

Every day, interesting machine learning projects get posted to GitHub, but they soon disappear into the abyss if you're not quick to bookmark them. I've created a small collection of ML projects that stood out to me from HN, reddit, and GitHub. Preference was given to open source projects witht an online demo. Let me know if I missed any!

11 Alternatives to ML Showcase

TensorFlow’s lightweight solution for mobile and embedded devices. TensorFlow has always run on many platforms but as the adoption of ML models has grown exponentially over the last few years, so has the need to deploy them on mobile and embedded devices. TensorFlow Lite enables low-latency inference of on-device machine learning models.

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Google Developers Blog
Posted by the TensorFlow team It is designed from scratch to be: Lightweight Enables inference of on-device machine learning models with a small binary size and fast initialization/startup Cross-platform A runtime designed to run on many different platforms, starting with Android and iOS Fast Optimized for mobile devices, including dramatically improved mode… See more
9 Alternatives to TensorFlow Lite

Build Machine Learning models without a single line of code

Mate Labs is trying to enable Machine Learning and Deep Learning to one and all. Irrespective of whether a user knows how to code or not.

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If you've ever wanted to train a machine learning model and integrate it with IFTTT, you now can with a new offering from MateLabs. MateVerse, a platform where novices can spin out machine learning models, now works with IFTTT so that you can automatically set up models to run based on condit...
6 Alternatives to Mateverse by Mate Labs

Powerful machine learning knowledge discovery platform

SIMON is an open-source, powerful and easy to use automated machine learning, knowledge discovery platform. Goal is to provide software that will empower anyone to extract meaningful information from data and enable them to rapidly prototype with ML algorithms

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We think that already by now you should be able to do machine learning by yourself, even if you don't have necessary resources to do so! We help you achieve that: Powerful, flexible, open-source and easy to use automated machine learning knowledge discovery platform. We are genular, an open source community behind SIMON.
4 Alternatives to SIMON

Create, train and use TensorFlow ML models on .NET

Gradient allows you to create, train, and use machine learning models with the full power of TensorFlow API on .NET

- Train and run models on any hardware platform

- Use distributed training features

- Track your progress with Tensorboard

- Use C#

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This is a series of articles about my ongoing journey into the dark forest of Kaggle competitions as a .NET developer. I will be focusing on (almost) pure neural networks in this and the following articles. It means, that most of the boring parts of the dataset preparation, like filling out missing values, feature selection, outliers analysis, etc.
9 Alternatives to Gradient
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