refine tool names (#573)

README.md

@@ -132,8 +132,8 @@ Please review our [CONTRIBUTING.md](https://github.com/EthicalML/awesome-product
 
 
 ## Computation Load Distribution
+* [Apache Beam](https://github.com/apache/beam) ![](https://img.shields.io/github/stars/apache/beam.svg?style=social) Apache Beam is a unified programming model for Batch and Streaming.
 * [Bagua](https://github.com/BaguaSys/bagua) ![](https://img.shields.io/github/stars/BaguaSys/bagua.svg?style=social) - Bagua is a performant and flexible distributed training framework for PyTorch, providing a faster alternative to PyTorch DDP and Horovod. It supports advanced distributed training algorithms such as quantization and decentralization.
-* [Beam](https://github.com/apache/beam) ![](https://img.shields.io/github/stars/apache/beam.svg?style=social) Apache Beam is a unified programming model for Batch and Streaming.
 * [Colossal-AI](https://github.com/hpcaitech/ColossalAI) ![](https://img.shields.io/github/stars/hpcaitech/ColossalAI.svg?style=social) - A unified deep learning system for big model era, which helps users to efficiently and quickly deploy large AI model training and inference.
 * [Dask](https://github.com/dask/dask) ![](https://img.shields.io/github/stars/dask/dask.svg?style=social) - Distributed parallel processing framework for Pandas and NumPy computations - [(Video)](https://www.youtube.com/watch?v=RA_2qdipVng).
 * [DEAP](https://github.com/DEAP/deap) ![](https://img.shields.io/github/stars/DEAP/deap.svg?style=social) - A novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data structures transparent. It works in perfect harmony with parallelisation mechanisms such as multiprocessing and SCOOP.
@@ -182,6 +182,7 @@ Please review our [CONTRIBUTING.md](https://github.com/EthicalML/awesome-product
 ## Data Pipeline
 * [Apache Airflow](https://github.com/apache/airflow) ![](https://img.shields.io/github/stars/apache/airflow.svg?style=social) - Data Pipeline framework built in Python, including scheduler, DAG definition and a UI for visualisation.
 * [Apache Nifi](https://github.com/apache/nifi) ![](https://img.shields.io/github/stars/apache/nifi.svg?style=social) - Apache NiFi was made for dataflow. It supports highly configurable directed graphs of data routing, transformation, and system mediation logic.
+* [Apache Oozie](https://github.com/apache/oozie) ![](https://img.shields.io/github/stars/apache/oozie.svg?style=social) - Workflow scheduler for Hadoop jobs.
 * [Argo Workflows](https://github.com/argoproj/argo-workflows) ![](https://img.shields.io/github/stars/argoproj/argo-workflows.svg?style=social) - Argo Workflows is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Argo Workflows is implemented as a Kubernetes CRD (Custom Resource Definition).
 * [Azkaban](https://github.com/azkaban/azkaban) ![](https://img.shields.io/github/stars/azkaban/azkaban.svg?style=social) - Azkaban is a batch workflow job scheduler created at LinkedIn to run Hadoop jobs. Azkaban resolves the ordering through job dependencies and provides an easy to use web user interface to maintain and track your workflows.
 * [BatchFlow](https://github.com/analysiscenter/batchflow) ![](https://img.shields.io/github/stars/analysiscenter/batchflow.svg?style=social) - BatchFlow helps data scientists conveniently work with random or sequential batches of your data and define data processing and machine learning workflows for large datasets.
@@ -204,7 +205,6 @@ Please review our [CONTRIBUTING.md](https://github.com/EthicalML/awesome-product
 * [Luigi](https://github.com/spotify/luigi) ![](https://img.shields.io/github/stars/spotify/luigi.svg?style=social) - Luigi is a Python module that helps you build complex pipelines of batch jobs, handling dependency resolution, workflow management, visualisation, etc..
 * [Metaflow](https://github.com/Netflix/metaflow) ![](https://img.shields.io/github/stars/Netflix/metaflow.svg?style=social) - A framework for data scientists to easily build and manage real-life data science projects.
 * [Neuraxle](https://github.com/Neuraxio/Neuraxle) ![](https://img.shields.io/github/stars/Neuraxio/Neuraxle.svg?style=social) - A framework for building neat pipelines, providing the right abstractions to chain your data transformation and prediction steps with data streaming, as well as doing hyperparameter searches (AutoML).
-* [Oozie](https://github.com/apache/oozie) ![](https://img.shields.io/github/stars/apache/oozie.svg?style=social) - Workflow scheduler for Hadoop jobs.
 * [Pachyderm](https://github.com/pachyderm/pachyderm) ![](https://img.shields.io/github/stars/pachyderm/pachyderm.svg?style=social) - Open source distributed processing framework build on Kubernetes focused mainly on dynamic building of production machine learning pipelines - [(Video)](https://www.youtube.com/watch?v=LamKVhe2RSM).
 * [PipelineX](https://github.com/Minyus/pipelinex) ![](https://img.shields.io/github/stars/Minyus/pipelinex.svg?style=social) - Based on Kedro and MLflow. Full comparison is found [here](https://github.com/Minyus/Python_Packages_for_Pipeline_Workflow).
 * [Ploomber](https://github.com/ploomber/ploomber) ![](https://img.shields.io/github/stars/ploomber/ploomber.svg?style=social) - The fastest way to build data pipelines. Develop iteratively, deploy anywhere.
@@ -463,6 +463,7 @@ Please review our [CONTRIBUTING.md](https://github.com/EthicalML/awesome-product
 
 ## Industry Strength Visualisation
 * [Apache ECharts](https://github.com/apache/echarts) ![](https://img.shields.io/github/stars/apache/echarts.svg?style=social) - Apache ECharts is a powerful, interactive charting and data visualization library for browser.
+* [Apache Superset](https://github.com/apache/superset) ![](https://img.shields.io/github/stars/apache/superset.svg?style=social) - A modern, enterprise-ready business intelligence web application.
 * [Bokeh](https://github.com/bokeh/bokeh) ![](https://img.shields.io/github/stars/bokeh/bokeh.svg?style=social) - Bokeh is an interactive visualization library for Python that enables beautiful and meaningful visual presentation of data in modern web browsers.
 * [Geoplotlib](https://github.com/andrea-cuttone/geoplotlib) ![](https://img.shields.io/github/stars/andrea-cuttone/geoplotlib.svg?style=social) - geoplotlib is a python toolbox for visualizing geographical data and making maps.
 * [ggplot2](https://github.com/tidyverse/ggplot2) ![](https://img.shields.io/github/stars/tidyverse/ggplot2.svg?style=social) - An implementation of the grammar of graphics for R.
@@ -480,7 +481,6 @@ Please review our [CONTRIBUTING.md](https://github.com/EthicalML/awesome-product
 * [seaborn](https://github.com/mwaskom/seaborn) ![](https://img.shields.io/github/stars/mwaskom/seaborn.svg?style=social) - Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.
 * [Spotlight](https://github.com/Renumics/spotlight) ![](https://img.shields.io/github/stars/Renumics/spotlight.svg?style=social) - Spotlight helps you to identify critical data segments and model failure modes. It enables you to build and maintain reliable machine learning models by curating high-quality datasets.
 * [Streamlit](https://github.com/streamlit/streamlit) ![](https://img.shields.io/github/stars/streamlit/streamlit.svg?style=social) - Streamlit lets you create apps for your machine learning projects with deceptively simple Python scripts. It supports hot-reloading, so your app updates live as you edit and save your file.
-* [Superset](https://github.com/apache/superset) ![](https://img.shields.io/github/stars/apache/superset.svg?style=social) - A modern, enterprise-ready business intelligence web application.
 * [tensorboardX](https://github.com/lanpa/tensorboardX) ![](https://img.shields.io/github/stars/lanpa/tensorboardX.svg?style=social) - Write TensorBoard events with simple function call.
 * [TensorBoard](https://github.com/tensorflow/tensorboard) ![](https://img.shields.io/github/stars/tensorflow/tensorboard.svg?style=social) - TensorBoard is a visualization toolkit for machine learning experimentation that makes it easy to host, track, and share ML experiments.
 * [Transformer Explainer](https://github.com/poloclub/transformer-explainer) ![](https://img.shields.io/github/stars/poloclub/transformer-explainer.svg?style=social) - Transformer Explainer is an interactive visualization tool designed to help anyone learn how Transformer-based models like GPT work.
@@ -525,6 +525,7 @@ Please review our [CONTRIBUTING.md](https://github.com/EthicalML/awesome-product
 
 
 ## Model Serving and Monitoring
+* [Apache PredictionIO](https://github.com/apache/predictionio) ![](https://img.shields.io/github/stars/apache/predictionio.svg?style=social) - An open source Machine Learning Server built on top of a state-of-the-art open source stack for developers and data scientists to create predictive engines for any machine learning task.
 * [Backprop](https://github.com/backprop-ai/backprop) ![](https://img.shields.io/github/stars/backprop-ai/backprop.svg?style=social) - Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.
 * [BentoML](https://github.com/bentoml/BentoML) ![](https://img.shields.io/github/stars/bentoml/BentoML.svg?style=social) - BentoML is an open source framework for high performance ML model serving.
 * [Cortex](https://github.com/cortexlabs/cortex) ![](https://img.shields.io/github/stars/cortexlabs/cortex.svg?style=social) - Cortex is an open source platform for deploying machine learning models—trained with any framework—as production web services. No DevOps required.
@@ -558,7 +559,6 @@ Please review our [CONTRIBUTING.md](https://github.com/EthicalML/awesome-product
 * [OpenVINO](https://github.com/openvinotoolkit/openvino) ![](https://img.shields.io/github/stars/openvinotoolkit/openvino.svg?style=social) - OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
 * [Phoenix](https://github.com/Arize-ai/phoenix) ![](https://img.shields.io/github/stars/arize-ai/phoenix.svg?style=social) - Phoenix is an open source ML observability in a notebook to validate, monitor, and fine-tune your generative LLM, CV, and tabular models.
 * [PowerInfer](https://github.com/SJTU-IPADS/PowerInfer) ![](https://img.shields.io/github/stars/SJTU-IPADS/PowerInfer.svg?style=social) - PowerInfer is a CPU/GPU LLM inference engine leveraging activation locality for your device.
-* [PredictionIO](https://github.com/apache/predictionio) ![](https://img.shields.io/github/stars/apache/predictionio.svg?style=social) - An open source Machine Learning Server built on top of a state-of-the-art open source stack for developers and data scientists to create predictive engines for any machine learning task.
 * [Prompt2Model](https://github.com/neulab/prompt2model) ![](https://img.shields.io/github/stars/neulab/prompt2model.svg?style=social) - Prompt2Model is a system that takes a natural language task description (like the prompts used for LLMs such as ChatGPT) to train a small special-purpose model that is conducive for deployment.
 * [Redis-AI](https://github.com/RedisAI/RedisAI) ![](https://img.shields.io/github/stars/RedisAI/RedisAI.svg?style=social) - A Redis module for serving tensors and executing deep learning models. Expect changes in the API and internals.
 * [Seldon Core](https://github.com/SeldonIO/seldon-core) ![](https://img.shields.io/github/stars/SeldonIO/seldon-core.svg?style=social) - Open source platform for deploying and monitoring machine learning models in Kubernetes - [(Video)](https://www.youtube.com/watch?v=pDlapGtecbY).