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  • Home
  • RAG / Vector DBs
  • No-Code AI Worflows
  • Model Context Protocol
  • Azure, AWS, GCP, GitHub
  • Transform Sub-quadractic
  • Agents
  • APIs / Tools
  • Machine Learning
  • GPU Training - TensorFlow
  • Graph Neural Networks
  • Glossary
  • Enterprise Architecture
  • More
    • Home
    • RAG / Vector DBs
    • No-Code AI Worflows
    • Model Context Protocol
    • Azure, AWS, GCP, GitHub
    • Transform Sub-quadractic
    • Agents
    • APIs / Tools
    • Machine Learning
    • GPU Training - TensorFlow
    • Graph Neural Networks
    • Glossary
    • Enterprise Architecture
  • Home
  • RAG / Vector DBs
  • No-Code AI Worflows
  • Model Context Protocol
  • Azure, AWS, GCP, GitHub
  • Transform Sub-quadractic
  • Agents
  • APIs / Tools
  • Machine Learning
  • GPU Training - TensorFlow
  • Graph Neural Networks
  • Glossary
  • Enterprise Architecture

Azure AI Agent Integration

Azure AI Agent Integration

AWS AI Solutions

Amazon Q

Amazon Transcribe

Amazon Bedrock

 AI Assistant - Amazon Q - AWS  


Amazon Q is a generative AI assistant that transforms how work gets done in your organization. With specialized capabilities for software developers, business intelligence analysts, contact center employees, supply chain analysts, and anyone building with AWS, Amazon Q helps every employee get insights on their data and accelerate their tasks. Leveraging Amazon Q's advanced agentic capabilities, companies can streamline processes, get to decisions faster, and help employees be more productive. .

Amazon Bedrock

Amazon Transcribe

Amazon Bedrock

 Build Generative AI Applications with Foundation Models – Amazon Bedrock – AWS  


 Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, DeepSeek, Luma, Meta, Mistral AI, poolside (coming soon), Stability AI, TwelveLabs (coming soon), Writer, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI. Using Amazon Bedrock, you can easily experiment with and evaluate top FMs for your use case, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that execute tasks using your enterprise systems and data sources. Since Amazon Bedrock is serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with. 

Amazon Transcribe

Amazon Transcribe

Amazon Transcribe

 Amazon Transcribe – Speech to Text - AWS 

 

Amazon Transcribe is a fully managed, automatic speech recognition (ASR) service that makes it easy for developers to add speech to text capabilities to their applications. It is powered by a next-generation, multi-billion parameter speech foundation model that delivers high accuracy transcriptions for streaming and recorded speech. Thousands of customers across industries use it to automate manual tasks, unlock rich insights, increase accessibility, and boost discoverability of audio and video content.

Realize the value of your speech data today with Amazon Transcribe.

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Amazon Poly

Amazon Rekognition

Amazon Transcribe

 AI Voice Generator and Text-to-Speech Tool - Amazon Polly - AWS 


Amazon Polly is a fully-managed service that generates voice on demand, converting any text to an audio stream. Using deep learning technologies to convert articles, web pages, PDF documents, and other text-to-speech (TTS). Polly provides dozens of lifelike voices across a broad set of languages for you to build speech-activated applications that engage and convert. Meet diverse linguistic, accessibility, and learning needs of users across geographies and markets. Powerful neural networks and generative voice engines work in the background, synthesizing speech for you. Integrate the Amazon Polly API into your existing applications to become voice-ready quickly.  

Amazon Textract

Amazon Rekognition

Amazon Rekognition

 Intelligently Extract Text & Data with OCR - Amazon Textract - Amazon Web Services  


Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, layout elements, and data from scanned documents. It goes beyond simple optical character recognition (OCR) to identify, understand, and extract specific data from documents. Today, many companies manually extract data from scanned documents, such as PDFs, images, tables, and forms, or through simple OCR software that requires manual configuration (which often must be updated when the form changes). To overcome these manual and expensive processes, Amazon Textract uses ML to read and process any type of document, accurately extracting text, handwriting, tables, and other data with no manual effort. You can use one of our pretrained or custom features to quickly automate document processing, whether you’re automating loans processing or extracting information from invoices and receipts. Amazon Textract provides you the ability to customize our pretrained features to meet the document processing needs specific to your business. Amazon Textract can extract the data in minutes instead of hours or days.

Amazon Rekognition

Amazon Rekognition

Amazon Rekognition

 Image recognition software, ML image analysis, and video analysis – Amazon Rekognition – AWS  


Quickly add pretrained or customizable computer vision APIs to your applications without building machine learning (ML) models and infrastructure from scratch. 


 Analyze millions of images, video streams, and stored videos within seconds, and augment human review tasks with AI. 


 Scale up and down based on your business needs with fully managed AI capabilities and pay only for the images and videos you analyze. 


 Detect real users and deter bad actors using spoofs in seconds during facial verification 


 Detect objects, scenes, activities, landmarks, dominant colors, and image quality. 


Google Cloud Platform (GCP)

Cloud Computing Services | Google Cloud 


  • Build AI agents and generative AI applications with our fully managed AI platform, Vertex AI—enhanced by Gemini, over 200 foundation models, and ecosystem of AI partners
  • Improve customer service with virtual agents and conversational AI products like Speech-to-Text
  • Get AI-powered code generation, recommendations, and completion from Gemini Code Assist
  • Explore our ecosystem of Gemini products for businesses and developers to help you get the most out of Google AI

GCP Solutions

AI & Machine Learning

AI & Machine Learning

AI & Machine Learning

 

  • Build AI agents and generative AI applications with our fully managed AI platform, Vertex AI—enhanced by Gemini, over 200 foundation models, and ecosystem of AI partners
  • Improve customer service with virtual agents and conversational AI products like Speech-to-Text
  • Get AI-powered code generation, recommendations, and completion from Gemini Code Assist
  • Explore our ecosystem of Gemini products for businesses and developers to help you get the most out of Google AIbb .

Compute

AI & Machine Learning

AI & Machine Learning

 

  • Create and run customizable virtual machines with Compute Engine. For general-purpose workloads, Axion C4A instances offer up to 65% better price-performance than current-generation x86 instances
  • Automatically deploy, scale, and manage containers with Google Kubernetes Engine or Cloud Run
  • Migrate your apps at your own pace by moving directly to virtual machines or automatically modernizing to containers—without rewriting code


Storage

AI & Machine Learning

Databases

Ar

  • Store any type of data, any amount of data, and retrieve it as often as you’d like with object storage
  • Transfer data with online and offline transfer solutions, including Storage Transfer Service and Transfer Appliance
  • Persistent Disk block storage is fully integrated with Google Cloud products like Compute Engine and GKE

Databases

Data Analytics

Databases

 

  • Reduce MySQL, PostgreSQL, and SQL Server costs with Cloud SQL, a fully-managed database service
  • Scale your enterprise workloads and build generative AI apps with AlloyDB for PostgreSQL
  • Get high performance, a 99.999% availability SLA, and virtually unlimited scale with SpannerRun

Data Analytics

Data Analytics

Data Analytics

Have 

 

  • Run analytics at scale with 26%–34% lower three-year TCO on BigQuery compared to cloud data warehouse alternatives
  • Ingest, process, and analyze event streams in real time to make data more useful
  • Reveal the true power of your data and bring clarity to every situation with Looker and Google Cloud.

Networking

Data Analytics

Data Analytics

Cu 

  • Help protect your applications and websites against denial of service and web attacks with Cloud Armor
  • Quickly and securely scale web and video content delivery with Cloud CDN
  • Explore hybrid connectivity options, including VPN, peering, and enterprise support

Developer Tools

Developer Tools

Developer Tools


  • Write, debug, and run cloud-native applications, locally or in the cloud—quickly and easily with Cloud Code
  • With Cloud Build, continuously build, test, and deploy software across all languages and in multiple environments
  • Deploy pre-built solution templates—with an active Google Cloud account—including dynamic websites, load balanced VMs, and three tier web apps.

Security

Developer Tools

Developer Tools



  • Understand threat actors and potential attack vectors with Google Threat Intelligence and Mandiant Consulting Services
  • Detect, investigate, and respond to threats faster with Google Security Operations
  • Protect your workloads and meet digital sovereignty requirements with our full suite of security products and services

GCP AI Products

GCP AI Products

Vertex AI Studio

Generative AI Documment Summarization

Vertex AI Agent Builder

 AI and Machine Learning Products and Services | Google Cloud 

  • Adapt models to your use case with prompt design
  • Easily tune foundation models with your own data
  • Rapidly prototype and test generative AI models
  • Vertex AI logoIntroduction to Vertex AI Studio
    27:50.

 

  • Prompt design and tuning with an easy-to-use interface 
  • Code completion and generation with Codey
  • Generating and customizing images with Imagen
  • Universal speech models

Vertex AI Agent Builder

Generative AI Documment Summarization

Vertex AI Agent Builder

 Vertex AI Agent Builder | Google Cloud 


  • Having  Vertex AI Agent Builder helps you turn your processes into multi-agent experiences by building on them, and not disrupting them–regardless of where you stand in your AI journey or which technology stack you've chosen. 

 

  • Building multimodal conversational AI agents
  • Building a Google-quality search experience on your own data
  • Enjoy powerful orchestration, grounding and customization tools

.

Generative AI Documment Summarization

Generative AI Documment Summarization

Generative AI Documment Summarization

 AI and Machine Learning Products and Services | Google Cloud 


  •  The one-click solution establishes a pipeline that extracts text from PDFs, creates a summary from the extracted text with Vertex AI Generative AI Studio, and stores the searchable summary in a BigQuery database. 

 

  • Process and summarize large documents using Vertex AI LLMs
  • Deploy an application that orchestrates the documentation summarization process
  • Trigger the pipeline with a PDF upload and view a generated summary

Vertex AI Notebooks

Natural Language Processing

Generative AI Documment Summarization

 AI and Machine Learning Products and Services | Google Cloud 


  •  Choose from Colab Enterprise or Vertex AI Workbench. Access every capability in Vertex AI Platform to work across the entire data science workflow—from data exploration to prototype to production 

 

  • Data scientist workflows
  • Rapid prototyping and model development
  • Developing and deploying AI solutions on Vertex AI with minimal transition

AutoML

Natural Language Processing

Natural Language Processing

AutoML Solutions - Train models without ML expertise | Google Cloud 


  •  Train high-quality custom machine learning models with minimal effort and machine learning expertise.  

 

  • Building custom machine learning models in minutes with minimal expertise
  • Training models specific to your business needs

Natural Language Processing

Natural Language Processing

Natural Language Processing

 Cloud Natural Language | Google Cloud 


  • Train high-quality custom machine learning models with minimal effort and machine learning expertise.  

 

  • Building custom machine learning models in minutes with minimal expertise
  • Training models specific to your business needs

Video

Python / GitHub

Say something interesting about your business here.

Generate excitement

What's something exciting your business offers? Say it here.

GitHub / n8n

Detail your services

Git and n8n#

n8n uses Git to provide source control. To use this feature, it helps to have some knowledge of basic Git concepts. n8n doesn't implement all Git functionality: you shouldn't view n8n's source control as full version control.


New to Git and source control?

If you're new to Git, don't panic. You don't need to learn Git to use n8n. This document explains the concepts you need. You do need some Git knowledge to set up the source control, as this involves work in your Git provider.


Familiar with Git and source control?

If you're familiar with Git, don't rely on behaviors matching exactly. In particular, be aware that source control in n8n doesn't support a pull request-style review and merge process, unless you do this outside n8n in your Git provider.

This page introduces the Git concepts and terminology used in n8n. It doesn't cover everything you need to set up and manage a repository. The person doing the Setup should have some familiarity with Git and with their Git hosting provider.


This is a brief introduction

Git is a complex topic. This section provides a brief introduction to the key terms you need when using environments in n8n. If you want to learn about Git in depth, refer to GitHub | Git and GitHub learning resources.


Git overview#

Git is a tool for managing, tracking, and collaborating on multiple versions of documents. It's the basis for widely used platforms such as GitHub and GitLab.


Branches: Multiple copies of a project#

Git uses branches to maintain multiple copies of a document alongside each other. Every branch has its own version. A common pattern is to have a main branch, and then everyone who wants to contribute to the project works on their own branch (copy). When they finish their work, their branch is merged back into the main branch.

Diagram

Local and remote: Moving work between your machine and a Git provider#

A common pattern when using Git is to install Git on your own computer, and use a Git provider such as GitHub to work with Git in the cloud. In effect, you have a Git repository (project) on GitHub, and work with copies of it on your local machine.

n8n uses this pattern for source control: you'll work with your workflows on your n8n instance, but send them to your Git provider to store them.


Push, pull, and commit#

n8n uses three key Git processes:

  • Push: send work from your instance to Git. This saves a copy of your workflows and tags, as well as credential and variable stubs, to Git. You can choose which workflows you want to save.
  • Pull: get the workflows, tags, and variables from Git and load it into n8n. You will need to populate any credentials or variable stubs included in the refreshed items.
    Pulling overwrites your work
    If you have made changes to a workflow in n8n, you must push the changes to Git before pulling. When you pull, it overwrites any changes you've made if they aren't stored in Git.
  • Commit: a commit in n8n is a single occurrence of pushing work to Git. In n8n, commit and push happen at the same time.

Refer to Push and pull for detailed information about how n8n interacts with Git.


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