Algorithmia
Managed MLflow
neptune.ai
5Analytics
MCenter
Numericcal
Datatron
Iterative.ai
TensorFlow
PyTorch
Scikit-learn
Keras
IBM Watson Studio
OpenCV
Amazon SageMaker
Pega Platform
Algorithmia TensorFlowTensorFlow might be a bit more popular than Algorithmia. We know about 7 links to it since March 2021 and only 5 links to Algorithmia. 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.
To push a model into production, there are additional concerns which the tools in the versioning, deployment and release space aim to solve. This includes obtaining adequate infrastructure to run the model reliably and facilitating easy model release or rollback. Solutions in the MLOps space includes Kubeflow, Pachyderm and Algorithmia. - Source: dev.to / over 2 years ago
And for enterprises that want to do the same with ML you can use algorithmia.com. Source: over 2 years ago
Algorithmia advertises themselves as an MLops platform for data scientists, and they provide an easy way to host models on a scalable REST API. Source: over 2 years ago
Seems similar to https://algorithmia.com. Source: over 2 years ago
Algorithmia.com — Host algorithms for free. Includes free monthly allowance for running algorithms. Now with CLI support. - Source: dev.to / almost 3 years ago
Converting the images to a tensor: Deep learning models work with tensors, so the images should be converted to tensors. This can be done using the to_tensor function from the PyTorch library or convert_to_tensor from the Tensorflow library. - Source: dev.to / about 1 year ago
So I went to tensorflow.org to find some function that can generate a CSR representation of a matrix, and I found this function https://www.tensorflow.org/api_docs/python/tf/raw_ops/DenseToCSRSparseMatrix. Source: almost 2 years ago
Can anyone offer up an explanation for why there is a performance difference, and if possible, what could be done to fix it. I'm using the installation guidelines found on tensorflow.org and installing tf2.7 through pip using an anaconda3 env. Source: almost 2 years ago
I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org. Source: about 2 years ago
I have looked at this TensorFlow website and TensorFlow.org and some of the examples are written by others, and it seems that I am stuck in RNNs. What is the best way to install TensorFlow, to follow the documentation and learn the methods in RNNs in Python? Is there a good tutorial/resource? Source: about 2 years ago
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.
PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...
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.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
5Analytics - The 5Analytics AI platform enables you to use artificial intelligence to automate important commercial decisions and implement digital business models.
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.