TensorFlow
PyTorch
Keras
IBM Watson Studio
Scikit-learn
Azure Machine Learning Service
Pega Platform
Azure Machine Learning Studio
Emisar.dev
Emisar is the last MCP server youโll need to install: a Zero-Trust gateway connecting Claude, Cursor, ChatGPT, and any AI agent to your infrastructure. One server handles production access, debugging, alerts, and internal operations, with new capabilities added as packs. Agents can inspect real production state, debug what they shipped, and help resolve incidents. Safe reads run automatically; policy allows, blocks, or routes risky actions for approval. No SSH keys, VPNs, remote shells, or standing shell access โ and every call is recorded.
TensorFlow Emisar.devNo features have been listed yet.
No Emisar.dev videos yet. You could help us improve this page by suggesting one.
Emisar.dev's answer:
emisar is for SRE, DevOps, platform engineering, infrastructure, and security teams that want AI agents to inspect and operate production systems. It is especially relevant to teams managing multiple Linux hosts, clusters, databases, cloud services, or regulated environments where unrestricted shell access and incomplete audit records are unacceptable.
Emisar.dev's answer:
The hosted control plane and operator interface use Elixir, Phoenix, LiveView, PostgreSQL, and Tailwind CSS. The host runner and MCP bridge are written in Go. Action packs use YAML and JSON Schema, while production infrastructure is managed with Terraform on Google Cloud. The system communicates through MCP, OAuth 2.1, TLS, and WebSockets.
Emisar.dev's answer:
Emisar.dev's answer:
Founder Andrii Dryga spent a decade working as a CTO, full-stack engineer, SRE, and DevOps engineer. He experienced the cost of running the wrong command on the wrong cluster, while also seeing AI solve operational problems in seconds. emisar grew from the need to preserve both truths: AI agents are useful, and production access must remain bounded. Its answer is to give agents a reviewed catalog of operations instead of a blank terminal.
Emisar.dev's answer:
emisar lets AI agents work on real infrastructure without giving them a shell. Agents choose from a finite catalog of typed, versioned actions. Policy decides what runs, what requires approval, and what is denied, while an outbound-only runner verifies the action again on the host. New capabilities arrive as packs behind the same MCP integration, and every request is recorded in both a searchable audit trail and a tamper-evident host journal. [
Emisar.dev's answer:
Choose emisar when you want an agent to keep investigating and handling routine operations without handing it SSH credentials or supervising every call. Compared with raw shell access, copy-paste workflows, or one-off MCP servers, emisar provides reviewed action contracts, host-level enforcement, risk-based policy, scoped access, approvals, pack integrity checks, and a durable audit trail. It is built specifically for governed infrastructure access rather than generic automation.
Based on our record, TensorFlow seems to be more popular. It has been mentiond 8 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.
The open-source movement offers hope here. Projects like Hugging Face are democratizing access to state-of-the-art models, while initiatives like Google's TensorFlow provide powerful frameworks without licensing costs. But even open-source solutions require technical expertise that many lack. - Source: dev.to / 4 months 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 / over 3 years 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: about 4 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: about 4 years ago
I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org. Source: over 4 years ago
PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
IBM Watson Studio - Learn more about Watson Studio. Increase productivity by giving your team a single environment to work with the best of open source and IBM software, to build and deploy an AI solution.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Azure Machine Learning Service - Build and deploy machine learning models in a simplified way with Azure Machine Learning service. Make machine learning more accessible with automated capabilities.
Pega Platform - The best-in-class, rapid no-code Pega Platform is unified for building BPM, CRM, case management, and real-time decisioning apps.
