AWS SageMaker Ground Truth
Computer Vision Annotation Tool (CVAT)
Labelbox
Supervisely
Qubole
V7
Universal Data Tool
Google CLOUD AUTOML
NumPy
Pandas
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
WEKA
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Based on our record, NumPy seems to be a lot more popular than AWS SageMaker Ground Truth. While we know about 107 links to NumPy, we've tracked only 3 mentions of AWS SageMaker Ground Truth. 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.
Perhaps https://aws.amazon.com/sagemaker/data-labeling/ ? Source: almost 2 years ago
In this session you will discover how to use Amazon SageMaker to prepare data for machine learning in minutes. SageMaker provides data preparation tools that make it easier to label, prepare, and analyse your data. Walk through a complete data-preparation workflow, including how to use SageMaker Ground Truth to label training datasets, as well as how to extract data from numerous data sources, convert it using... - Source: dev.to / over 2 years ago
As for who run MLD I guess It’s Amazon itself, have a look at this https://aws.amazon.com/sagemaker/groundtruth/. I speculate that multiple companies use this resource and they are the one responsible to upload the correct instructions, Amazon just redirect the labeling job for us using and requester account in mTurk, that explains why the communication is unacceptable with this requester. Source: over 2 years ago
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication. - Source: dev.to / 2 months ago
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:. - Source: dev.to / 2 months ago
Numpy: A library for scientific computing in Python. - Source: dev.to / 5 months ago
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy. - Source: dev.to / 6 months ago
A majority of software in the modern world is built upon various third party packages. These packages help offload work that would otherwise be rather tedious. This includes interacting with cloud APIs, developing scientific applications, or even creating web applications. As you gain experience in python you'll be using more and more of these packages developed by others to power your own code. In this example... - Source: dev.to / 7 months ago
Computer Vision Annotation Tool (CVAT) - Powerful and efficient Computer Vision Annotation Tool (CVAT) - opencv/cvat
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Labelbox - Build computer vision products for the real world
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Supervisely - Supervisely helps people with and without machine learning expertise to create state-of-the-art...
OpenCV - OpenCV is the world's biggest computer vision library
