Manage your Complete Machine Learning Workflow
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What is

Build & deploy ML models faster on unstructured data. No specialized skills required. Easy-to-use & scalable SaaS platform. is a tool in the Machine Learning Tools category of a tech stack.'s Features

  • Collect, label, and visualize unstructured data
  • Guided modules to upload, clean, label, and visualize unstructured data
  • Create & train models automatically
  • Train models without coding using our ready-made, fine tuned, state-of-the-art neural network architecture
  • Monitor model performance and iterate in minutes
  • Monitor data collection, labeling, training, and performance of deployed models in real-time Alternatives & Comparisons

What are some alternatives to
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano.
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
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