Amazon Rekognition
Kairos
Google Vision AI
Clarifai
OpenCV
Microsoft Computer Vision API
Trueface Visionbox
Social Mapper
Saturn Cloud
Deepnote
Amazon SageMaker
Apache Zeppelin
Databricks Unified Analytics Platform
Azure Synapse Analytics
Google BigQuery
GeoSpock
Saturn Cloud is an award-winning ML platform with 75,000+ users, including NVIDIA, CFA Institute, Snowflake, Flatiron School, Nestle, and more. It is an all-in-one solution for data science & ML development, deployment, and data pipelines in the cloud. Users can spin up a notebook with 4TB of RAM, add a GPU, connect to a distributed cluster of workers, build large language models, and more in a completely hosted environment.
Data scientists and analysts work best using the tools they want to use. You can use your preferred languages, IDEs, and machine-learning libraries in Saturn Cloud. We offer full Git integration, shared custom images, and secure credential storage, making scaling and building your team in the cloud easy. We support the entire machine learning lifecycle from experimentation to production with features like jobs and deployments. These features and built-in tools are easily shareable within teams, so time is saved and work is reproducible.
Amazon RekognitionSmooth and bug free experience. There are ready data science images with pre loaded packages for most common scenarios, making you focus on the project/problem and leave the infrastructure part to Saturn Cloud.
True story, way better than just sweating Colab. The best and cheapest compute services there is.
I have started using this to run the computations which generally require like 64+GB of RAM, and the procedure to setup the enviroment is also nice. Got all the R packages running smoothly.
Based on our record, Amazon Rekognition should be more popular than Saturn Cloud. It has been mentiond 33 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.
AWS Rekognition is a great choice for many types of real-world projects or just for testing an idea on your images. The issue eventually comes with its cost, unfortunately, which we will see later in a specific example. Don’t get me wrong, Rekognition is a great service and I love to use it for its simplicity and reliable performance on quite a few projects. - Source: dev.to / 3 months ago
I don’t really want to spend so much time manually adjusting labels. For most machine learning, the next step would be to fine tune your model. You can essentially fine tune Amazon Rekognition by using Custom Labels. You can do this to make it better at detecting specific objects (like bears) or train it to detect new objects like your product or logo. It really depends on your application needs. - Source: dev.to / 11 months ago
For instance, are you a company with lots of security cameras? Hire me to write a program that pipes your data into AWS rekognition and then shows you a dashboard of what happened on your cams today. Got a ton of products with no meta-description? Hire me to write a program that pipes your data into OpenAI, and then saves the generated description to your custom CMS. Source: 11 months ago
Amazon Rekognition: Used to index, detect faces in the picture, and compare faces when users try voting, it was the heart of the facial voting feature. - Source: dev.to / about 1 year ago
Sure. But if you think generating thumbnails and detecting intros/credits takes a long time, wait until your computer is running machine learning/computer vision over your entire library. They also have to build and train that model which is no trivial task. And I know what you're thinking, why don't they just use Amazon's Rekognition service that does celebrity identification? Well, it's $0.10 per minute of... Source: about 1 year ago
Not 100% sure of your intention, but if you work with python, and you're familiar with (or can spend the time learning) dask, and willing to pay, you can consider coiled.io or saturncloud.io that offer managed dask that you can scale and use GPUs etc (again, not sure if applicable to your use case). Source: over 1 year ago
SaturnCloud - Data science cloud environment, that allows to run Jupyter notebooks and Dask clusters. 30 hours free computation and 3 hours of Dask per month. - Source: dev.to / over 1 year ago
I think your site looks good and I have used the type of service you offer, but there are 2 potential problems. As SheepherderPatient51 said,Google already offers all of this for free (and so does https://kaggle.com and https://www.paperspace.com ). There are also other sites just like yours such as https://deepnote.com,https://saturncloud.io, and https://lambdalabs.com . Source: over 1 year ago
* How does it differ from other GPU cloud providers that offer ready to use Jupyter notebooks? (E.g. https://support.genesiscloud.com/support/solutions/articles/47001170102-running-jupyter-notebook-or-jupyterlab-on-your-instance or https://saturncloud.io/). - Source: Hacker News / about 2 years ago
At the moment I am going to go to https://saturncloud.io/ or https://www.cloudeo.group/. Source: over 2 years ago
Kairos - Facial recognition & mood detection API
Deepnote - A collaboration platform for data scientists
Google Vision AI - Cloud Vision API provides a comprehensive set of capabilities including object detection, ocr, explicit content, face, logo, and landmark detection.
Amazon SageMaker - Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.
Clarifai - The World's AI
Apache Zeppelin - A web-based notebook that enables interactive data analytics.