TensorFlow VS Sightify
Compare TensorFlow VS Sightify and see what are their differences
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TensorFlow
PyTorch
Scikit-learn
Keras
IBM Watson Studio
OpenCV
Azure Machine Learning Service
CUDA Toolkit
Sightify
MindsDB
H2O.ai
BaseTen
Relevance AI
Sightify | AI Agents is an LLM AI software application intended to automate SME workflows while ensuring data sovereignty.
Some features include:
Data-Sovereign Agents: Fine-tuned w/ RAG on open-source LLMs for specific business process optimization No AI Hallucinations: Source, page, and section citations for database-enforced tokens Multimodal: PDF, Excel, Word, TXT, PNG/JPEG, etc. CRM/ERP System Integration: API documentation, MCP compliant, R&D integration/support Updatable LLMs: Constant New Version Implementations (Qwen 70B, Gemma 27B)
Our current AI Agents are:
Knowledge Assistant: Generates RAG-powered responses referencing the ERP/CRM database for client relationship management, HR/company regulations search, marketing/email suggestions, etc. Contract Finalizer: Finalize legal contracts that are sent to or received from clients/partners by referencing past finalized contracts, government regulations/policies, and the ERP/CRM database. Report Generator: Instant generate monthly/annual sales/marketing/buget reports based on report templates and the ERP/CRM database Market Researcher: Analyze and compare competitor pricing, products, marketing, etc with Internet and ERP/CRM database reference Meeting Notetaker: Immediately generate meeting notes after recording/uploading meeting audio, use LLM reasoning to create action items, draft emails, etc.
Our AI software deployment is flexible:
On-Premise: Sightify has several OEM / SI partnerships across the world that help deploy Sightify | AI Agents on-premise globally. While Sightify stills provides L3 support, the OEM / SI combine to provide L1/L2 support.
Private Cloud: Sightify has multiple GPU compute provider partnerships across the world that help provide compliant infrastructure for deploying Sightify | AI Agents. Sightify provides L2/L3 support and the GPU compute provider provides L1/L2 support.
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Sightify's answer:
Data Sovereignty Many AI B2B SaaS today build their agents on ChatGPT or Claude models. Thus, each time enterprises use that SaaS AI, their data is exposed to these hyperscalers. AI Agents is built and fine-tuned on open-source LLMs. This means that enterprises using AI Agents are using their own proprietary model, preventing any other companies from accessing or using their data for training.
Easy-to-Use Sightify’s target client base are SMEs. These SMEs typically will not have an AI team, and so our platform is designed to be extremely easy-to-use, with no technical training required.
Switchable LLMs Since new and better open-source LLMs are being released every year, Sightify provides a platform function to switch base models for each specific Agent. That way, Agent performance is always optimized and equipped with the newest AI features.
Full, Flexible Deployment AI Agents can be deployed in any way -- according to client’s needs. Whether on-premise, on the private cloud (through 3rd-party infrastructure providers), or on the public cloud (Sightify’s own cloud infrastructure).
Sightify's answer:
Our AI Agents are fine-tuned on open-source LLMs, most recently Gemma 3. This guarantees that our Agents are enterprise-proprietarty and data-sovereign, giving our clients full control over their data.
Sightify's answer:
Small-to-Medium enterprises in data-sensitive industries: finance, telecom, legal, healthcare, laboratory sciences, etc.
Based on our record, TensorFlow seems to be more popular. It has been mentiond 7 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.
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 2 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 3 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: over 3 years ago
I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org. Source: over 3 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: over 3 years ago
PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...
MindsDB - We are an open-source project that enables you to do Machine Learning using SQL directly from the Database.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
H2O.ai - Democratizing Generative AI. Own your models: generative and predictive. We bring both super powers together with h2oGPT.
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
BaseTen - The fastest way to build ML-powered applications