flink-kubernetes-operator
fiftyone
flink-kubernetes-operator | fiftyone | |
---|---|---|
8 | 21 | |
725 | 6,756 | |
4.3% | 2.7% | |
9.2 | 10.0 | |
8 days ago | 7 days ago | |
Java | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
flink-kubernetes-operator
-
Top 10 Common Data Engineers and Scientists Pain Points in 2024
Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example, implementing a real-time anomaly detection model in Kafka Streams would require translating Python code into Java, slowing down pipeline performance, and requiring a complex initial setup.
-
Choosing Between a Streaming Database and a Stream Processing Framework in Python
Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling.
- FLaNK Stack Weekly 22 January 2024
-
Go concurrency simplified. Part 4: Post office as a data pipeline
also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc.
-
Five Apache projects you probably didn't know about
Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features.
-
Getting Started with Flink SQL, Apache Iceberg and DynamoDB Catalog
Due to the technology transformation we want to do recently, we started to investigate Apache Iceberg. In addition, the data processing engine we use in house is Apache Flink, so it's only fair to look for an experimental environment that integrates Flink and Iceberg.
- FLaNK Stack Weekly for 07August2023
fiftyone
-
Anomaly Detection with FiftyOne and Anomalib
pip install -U git+https://github.com/voxel51/fiftyone.git
-
May 8, 2024 AI, Machine Learning and Computer Vision Meetup
In this brief walkthrough, I will illustrate how to leverage open-source FiftyOne and Anomalib to build deployment-ready anomaly detection models. First, we will load and visualize the MVTec AD dataset in the FiftyOne App. Next, we will use Albumentations to test out augmentation techniques. We will then train an anomaly detection model with Anomalib and evaluate the model with FiftyOne.
-
Voxel51 Is Hiring AI Researchers and Scientists — What the New Open Science Positions Mean
My experience has been much like this. For twenty years, I’ve emphasized scientific and engineering discovery in my work as an academic researcher, publishing these findings at the top conferences in computer vision, AI, and related fields. Yet, at my company, we focus on infrastructure that enables others to unlock scientific discovery. We have built a software framework that enables its users to do better work when training models and curating datasets with large unstructured, visual data — it’s kind of like a PyTorch++ or a Snowflake for unstructured data. This software stack, called FiftyOne in its single-user open source incarnation and FiftyOne Teams in its collaborative enterprise version, has garnered millions of installations and a vibrant user community.
-
How to Estimate Depth from a Single Image
We will use the Hugging Face transformers and diffusers libraries for inference, FiftyOne for data management and visualization, and scikit-image for evaluation metrics.
-
How to Cluster Images
With all that background out of the way, let’s turn theory into practice and learn how to use clustering to structure our unstructured data. We’ll be leveraging two open-source machine learning libraries: scikit-learn, which comes pre-packaged with implementations of most common clustering algorithms, and fiftyone, which streamlines the management and visualization of unstructured data:
-
Efficiently Managing and Querying Visual Data With MongoDB Atlas Vector Search and FiftyOne
FiftyOne is the leading open-source toolkit for the curation and visualization of unstructured data, built on top of MongoDB. It leverages the non-relational nature of MongoDB to provide an intuitive interface for working with datasets consisting of images, videos, point clouds, PDFs, and more.
-
FiftyOne Computer Vision Tips and Tricks - March 15, 2024
Welcome to our weekly FiftyOne tips and tricks blog where we recap interesting questions and answers that have recently popped up on Slack, GitHub, Stack Overflow, and Reddit.
- FLaNK AI for 11 March 2024
-
How to Build a Semantic Search Engine for Emojis
If you want to perform emoji searches locally with the same visual interface, you can do so with the Emoji Search plugin for FiftyOne.
- FLaNK Stack Weekly for 07August2023
What are some alternatives?
hugging-chat-api - HuggingChat Python API🤗
caer - High-performance Vision library in Python. Scale your research, not boilerplate.
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
ToolBench - [ICLR'24 spotlight] An open platform for training, serving, and evaluating large language model for tool learning.
ZnTrack - Create, visualize, run & benchmark DVC pipelines in Python & Jupyter notebooks.
CallCMLModel - An example on calling models deployed in CML
Serpent.AI - Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own!
Qwen-7B - The official repo of Qwen (通义千问) chat & pretrained large language model proposed by Alibaba Cloud. [Moved to: https://github.com/QwenLM/Qwen]
streamlit - Streamlit — A faster way to build and share data apps.
cdf-workshop