neptune.ai
Comet.ml
Weights & Biases
Algorithmia
Managed MLflow
Spell
MCenter
5Analytics
Amazon Textract
DocParser
ABBYY FineReader
Nanonets OCR
Pen to Print
FlexiCapture
Pen2txt
IBM Datacap
Track and version your notebooks Log all your notebooks directly from Jupyter or Jupyter Lab. All you need is to install a Jupyter extension.
Manage your experimentation process Neptune tracks your work with virtually no interference to the way you like to do it. Decide what is relevant to your project and start tracking: - Metrics - Hyperparameters - Data versions - Model files - Images - Source code
Integrate with your workflow easily Neptune is a lightweight extension to your current workflow. Works with all common technologies in data science domain and integrates with other tools. It will take you 5 minutes to get started.
neptune.ai Amazon TextractOnly negative is I didn't see it integrated with Azure, does with Google, AWS and one more. Looks real nice, and pretty powerful and plenty useful features for a data science group
Based on our record, Amazon Textract should be more popular than neptune.ai. It has been mentiond 34 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.
Neptune.ai - Log, store, display, organize, compare, and query all your MLOps metadata. Free for individuals: 1 member, 100 GB of metadata storage, 200h of monitoring/month. - Source: dev.to / 4 months ago
Hi I am Jakub. I run marketing at a dev tool startup https://neptune.ai/ and I share learnings on dev tool marketing on my blog https://www.developermarkepear.com/. Whenever I'd start a new marketing project I found myself going over a list of 20+ companies I knew could have done something well to “copy-paste” their approach as a baseline (think Tailscale, DigitalOCean, Vercel, Algolia, CircleCi, Supabase,... - Source: Hacker News / 8 months ago
There are a lot of tools out there for experiment tracking (eg neptune.ai), but I'm really not sure whether that sort of thing is over the top for what I need to do. Source: 9 months ago
Welcome to another episode of The Developer-led Podcast, where we dive into the strategies modern companies use to build and grow their developer tools. In this exciting episode, we're joined by Jakub Czakon, the CMO at Neptune.ai, a startup that assists developers in efficiently managing their machine-learning model data. Jakub is renowned not only for his role at Neptune.ai but also for his developer marketing... - Source: dev.to / 9 months ago
Tbh I have done a pretty good search on this topic, I couldn't find any. I thought maybe community could help me find one, if people like you (who works at neptune.ai) have the same opinion then it is what it is :). Anyway thank you for the suggestions that you gave, probably gonna use that. Source: 11 months ago
Amazon Textract has an Analyze Lending API for evaluating and categorizing the documents contained in mortgage loan application packages, as well as extracting the data they contain. The new API can assist in processing applications quicker and with minimal errors, therefore improving the end-customer experience and lowering operational costs. - Source: dev.to / 4 months ago
You could try something like https://aws.amazon.com/textract/ or https://cloud.google.com/vision/docs/handwriting. Both have support for modern handwriting. I don't know if it will work with a script written a century ago though. - Source: Hacker News / 5 months ago
Create a main.js file inside the look-for-github-profile-step project folder. Implement the code that parses the resume and plucks the GitHub profile URL. This step function is responsible for using Textract (an AI service from AWS) and passing state back to the state machine. - Source: dev.to / 8 months ago
The primary challenge in processing invoices is extracting the relevant data. This is where Amazon Textract can help. It is a service provided by Amazon Web Services (AWS) that uses advanced Machine Learning (ML) algorithms to automatically extract structured and unstructured data from scanned documents, images, and PDF files. It can detect typed and handwritten text in different types of documents including... - Source: dev.to / 9 months ago
First, we’ve decided to leave open-source solutions behind. We’ve used AWS Textract to parse PDF files. This way we don’t rely on the internal structure of the PDF to get text from it (or to get nothing - like in the case of the Uber example). Textract uses OCR and machine learning to get not only text but also spatial information from the document. - Source: dev.to / 10 months ago
Comet.ml - Comet lets you track code, experiments, and results on ML projects. It’s fast, simple, and free for open source projects.
DocParser - Extract data from PDF files & automate your workflow with our reliable document parsing software. Convert PDF files to Excel, JSON or update apps with webhooks.
Weights & Biases - Developer tools for deep learning research
ABBYY FineReader - ABBYY's latest PDF editor software, FineReader 16 you can easily convert files like PDF to Excel, PDF to Word, edit, share, collaborate & more with this PDF editor!
Algorithmia - Algorithmia makes applications smarter, by building a community around algorithm development, where state of the art algorithms are always live and accessible to anyone.
Nanonets OCR - Intelligent text extraction using OCR and deep learning