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[提交译文] 20220914-Platforms that Help Deploy AI and ML Applications on the Cloud
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[#]: subject: "Platforms that Help Deploy AI and ML Applications on the Cloud"
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[#]: via: "https://www.opensourceforu.com/2022/09/platforms-that-help-deploy-ai-and-ml-applications-on-the-cloud/"
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[#]: author: "Dr Kumar Gaurav https://www.opensourceforu.com/author/dr-gaurav-kumar/"
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[#]: collector: "lkxed"
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[#]: translator: " "
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[#]: reviewer: " "
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[#]: publisher: " "
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[#]: url: " "
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Platforms that Help Deploy AI and ML Applications on the Cloud
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======
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*Artificial intelligence and machine learning are impacting nearly every industry today. This article underlines the various ways in which these are being used in our everyday lives and how some open source cloud platforms are enabling their deployment.*
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The goal of artificial intelligence (AI) is to construct machines and automated systems that are able to mimic human cognition. On a global scale, AI is transforming societies, politics, and economies in a variety of ways. Examples of the applications of AI include Google Help, Siri, Alexa, and self-driving cars like Tesla.
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Today, AI is being used to solve difficult problems in an effective manner in a wide range of industries. It is being used in the healthcare industry to make more accurate and faster diagnoses than humans. Doctors can use AI to diagnose a disease, and get an alert when a patient’s condition is deteriorating.
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Data security is critical for every business, and the number of cyberattacks is continually increasing. Using artificial intelligence, the security of data can be improved. An example of this is the integration of intelligent bots to identify software bugs and cyberattacks.
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Twitter, WhatsApp, Facebook and Snapchat are just a few of the social media platforms that store and manage billions of profiles by using AI algorithms. AI can arrange and sift through massive amounts of data to find the latest trends, hashtags, and needs of various people.
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![Figure 1: Key applications of machine learning][1]
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The tourism industry is becoming increasingly reliant on AI, as the latter can help with a variety of travel-related tasks including booking hotels, flights, and the best routes for consumers. For better and faster customer service, chatbots driven by artificial intelligence are being used in the travel industry.
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Table 1: Tools and frameworks for machine learning
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| Tool/Platform | URL |
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| :- | :- |
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| Streamlit | https://github.com/streamlit/streamlit |
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| TensorFlow | https://www.tensorflow.org/ |
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| PyTorch | https://pytorch.org/ |
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| scikit-learn | https://scikit-learn.org/ |
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| Apache Spark | https://spark.apache.org/ |
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| Torch | http://torch.ch/ |
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| Hugging Face | https://huggingface.co/ |
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| Keras | https://keras.io/ |
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| TensorFlowJS | https://www.tensorflow.org/js |
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| KNIME | https://www.knime.com/ |
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| Apache Mahout | https://mahout.apache.org/ |
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| Accord | http://accord-framework.net/ |
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| Shogun | http://shogun-toolbox.org/ |
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| RapidMiner | https://rapidminer.com/ |
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| Blocks | https://github.com/mila-iqia/blocks |
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| TuriCreate | https://github.com/apple/turicreate |
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| Dopamine | https://github.com/google/dopamine |
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| FlairNLP | https://github.com/flairNLP/flair |
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### Machine learning in different domains
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All techniques and tools that let software applications and gadgets respond and develop on their own are referred to as machine learning (ML). AI can learn without really being explicitly programmed to perform the required action, thanks to machine learning techniques. Rather than relying on predefined computer instructions, the ML algorithm learns a pattern from sample inputs, and then anticipates and executes tasks completely based on the learned pattern. If rigorous algorithms aren’t an option, machine learning can be a life-saver. It will pick up the new procedure by analysing prior ones and then putting it into action. ML has cleared the way for technical advancements and technologies that were previously unimaginable in a variety of industries. It is used in a variety of cutting-edge technologies today — from predictive algorithms to Internet TV live streaming.
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A notable ML and AI technique is image recognition, which is a method for categorising and detecting a feature or an item in a digital image. Classification and face recognition are done using this method.
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![Figure 2: Streamlit cloud for machine learning][2]
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The use of machine learning for recommender systems is among its most widely used and well-known applications. In today’s e-commerce world, product recommendation is a prominent tool that utilises powerful machine learning techniques. Websites use AI and ML to keep track of past purchases, search trends, and shopping cart history, and then generate product recommendations based on that data.
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There is a lot of interest in employing machine learning algorithms in the healthcare industry. Emergency room wait times can be predicted across multiple hospital departments by using an ML algorithm. Details of staff shifts, patient data, and recordings of department discussions and emergency room layouts are all used to help create the algorithm. Machine learning algorithms can be used for detecting a disease, planning treatments, and prognostication.
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**Key features of the cloud platforms used for machine learning**:
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* Algorithms or features extraction
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* Association rule mining
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* Big Data based predictive analytics
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* Classification, regression and clustering
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* Data loading and transformation
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* Data preparation, data preprocessing and visualisation
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* Dimensionality reduction
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* Distributed linear algebra
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* Hypothesis tests and kernel methods
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* Processing of image, audio, signal and vision data sets
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* Model selection and optimisation module
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* Preprocessing and dataflow programming
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* Recommender systems
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* Support for text mining and image mining through plugins
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* Visualisation and plotting
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### Cloud based deployment of AI and ML applications
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The applications of AI and ML can be deployed on cloud platforms. A number of cloud service providers nowadays enable programmers to build models for effective decision-making in their domain.
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These cloud based platforms are integrated with pre-trained machine learning and deep learning models on which the applications can be deployed without any coding or with minimum scripting.
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![Figure 3: Categories of ML deployments in Streamlit][3]
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**Streamlit:** Streamlit gives data scientists and ML experts access to assorted machine learning models. It is open source and compatible with cloud deployments. The ML models can be made ready to be used with data sets in a few moments.
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Streamlit provides a range of machine learning models and source code in multiple categories including natural language processing, geography, education, computer vision, etc.
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Streamlit provides a range of machine learning models and source code in multiple categories including natural language processing, geography, education, computer vision, etc.
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![Figure 4: Hugging Face for machine learning][4]
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**Hugging Face:** This is another platform with pre-trained models and architectures for ML and AI in a range of categories. Many corporate giants are using this platform including Facebook AI, Microsoft, Google AI, Amazon Web Services, and Grammarly.
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A number of pre-trained and deployment-ready models are available in Hugging Face for different applications including natural language processing and computer vision.
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The following tasks can be carried out by using the ML models in Hugging Face:
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* Audio-to-audio processing
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* Automatic speech recognition
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* Computer vision
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* Fill-mask
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* Image classification
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* Image segmentation
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* Object detection
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* Answering of questions
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* Sentence similarity
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* Summarisation
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* Text classification
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* Text generation
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* Text-to-speech translation
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* Token classification
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* Translation classification
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The problem solvers available in Hugging Face are optimised and effective, helping models to be deployed rapidly (Figure 5).
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![Figure 5: Problem solvers and models in Hugging Face][5]
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These cloud based platforms are useful for researchers, practitioners and data scientists in multiple domains, and simplify the development of real-world applications that perform well.
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--------------------------------------------------------------------------------
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via: https://www.opensourceforu.com/2022/09/platforms-that-help-deploy-ai-and-ml-applications-on-the-cloud/
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作者：[Dr Kumar Gaurav][a]
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选题：[lkxed][b]
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://www.opensourceforu.com/author/dr-gaurav-kumar/
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[b]: https://github.com/lkxed
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[1]: https://www.opensourceforu.com/wp-content/uploads/2022/08/Figure-1-Key-applications-of-machine-learning.jpg
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[2]: https://www.opensourceforu.com/wp-content/uploads/2022/08/Figure-2-Streamlit-cloud-for-machine-learning.png
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[3]: https://www.opensourceforu.com/wp-content/uploads/2022/08/Figure-3-Categories-of-ML-deployments-in-Streamlit.png
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[4]: https://www.opensourceforu.com/wp-content/uploads/2022/08/Figure-4-Hugging-Face-for-machine-learning.png
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[5]: https://www.opensourceforu.com/wp-content/uploads/2022/08/Figure-5-Problem-solvers-and-models-in-Hugging-Face.png
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[#]: subject: "Platforms that Help Deploy AI and ML Applications on the Cloud"
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[#]: via: "https://www.opensourceforu.com/2022/09/platforms-that-help-deploy-ai-and-ml-applications-on-the-cloud/"
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[#]: author: "Dr Kumar Gaurav https://www.opensourceforu.com/author/dr-gaurav-kumar/"
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[#]: collector: "lkxed"
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[#]: translator: "misitebao"
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[#]: reviewer: " "
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[#]: publisher: " "
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[#]: url: " "
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# 帮助在云端部署人工智能（AI）和机器学习（ML）应用程序的平台
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_人工智能和机器学习正在影响当今几乎每个行业。本文重点介绍了这些技术在我们日常生活中的各种使用方式，以及一些开源云平台如何实现其部署。_
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人工智能 (AI) 的目标是构建能够模仿人类认知的机器和自动化系统。在全球范围内，人工智能正在以多种方式改变着社会、政治和经济。人工智能应用的例子包括谷歌帮助 (Google Help)、Siri、Alexa 和 Tesla (特斯拉) 等自动驾驶汽车。
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如今，人工智能正被广泛使用，以有效的方式解决各行各业的难题。它被用于医疗保健行业，以做出比人类更准确、更快速的诊断。医生可以使用人工智能来诊断疾病，并在患者病情恶化时收到警报。
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数据安全对每个企业都至关重要，网络攻击的数量也在不断增加。使用人工智能，可以提高数据的安全性。这方面的一个例子是集成智能机器人来识别软件错误和网络攻击。
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推特 (Twitter)、WhatsApp、Facebook (脸书) 和 Snapchat 只是使用 AI 算法存储和管理数十亿个人资料的社交媒体平台中的一小部分。人工智能可以整理和筛选大量数据，以找到最新趋势、标签和各种各样人的需求。
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![Figure 1: Key applications of machine learning][1]
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旅游业越来越依赖人工智能，因为后者可以帮助完成各种与旅行相关的任务，包括为消费者预订酒店、航班和最佳路线。为了提供更好、更快的客户服务，由人工智能驱动的聊天机器人正被用于旅游业。
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表 1: 机器学习的工具和框架
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| 工具/平台 | 链接 |
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| :------------ | :------------------------------------- |
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| Streamlit | https://github.com/streamlit/streamlit |
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| TensorFlow | https://www.tensorflow.org/ |
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| PyTorch | https://pytorch.org/ |
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| scikit-learn | https://scikit-learn.org/ |
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| Apache Spark | https://spark.apache.org/ |
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| Torch | http://torch.ch/ |
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| Hugging Face | https://huggingface.co/ |
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| Keras | https://keras.io/ |
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| TensorFlowJS | https://www.tensorflow.org/js |
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| KNIME | https://www.knime.com/ |
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| Apache Mahout | https://mahout.apache.org/ |
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| Accord | http://accord-framework.net/ |
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| Shogun | http://shogun-toolbox.org/ |
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| RapidMiner | https://rapidminer.com/ |
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| Blocks | https://github.com/mila-iqia/blocks |
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| TuriCreate | https://github.com/apple/turicreate |
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| Dopamine | https://github.com/google/dopamine |
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| FlairNLP | https://github.com/flairNLP/flair |
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### 不同领域的机器学习
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让软件应用程序和小工具自行响应和开发的所有技术和工具都称为机器学习 (ML)。多亏了机器学习技术，人工智能可以在没有真正被明确编程来执行所需操作的情况下进行学习。ML 算法不依赖于预定义的计算机指令，而是从样本输入中学习一个模式，然后完全基于学习到的模式来预测和执行任务。如果不能选择严格的算法，机器学习可以成为救命稻草。它将通过分析以前的程序来选择新程序，然后将其付诸实施。ML 为技术进步和以前在各种行业中无法想象的技术扫清了道路。如今，它被用于各种尖端技术 — 从预测算法到互联网电视直播。
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一个值得注意的 ML 和 AI 技术是图像识别，它是一种对数字图像中的特征或项进行分类和检测的方法。分类和人脸识别是使用这种方法完成的。
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![Figure 2: Streamlit cloud for machine learning][2]
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在推荐系统中使用机器学习是其最广泛使用和知名的应用之一。在当今的电子商务世界中，产品推荐是一种利用强大的机器学习技术的突出工具。网站使用人工智能和机器学习来跟踪过去的购买、搜索趋势和购物车历史，然后根据这些数据生成产品推荐。
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在医疗保健行业中使用机器学习算法引起了很多兴趣。通过使用 ML 算法，可以跨多个医院部门预测急诊室等待时间。员工轮班的详细信息、患者数据以及科室讨论和急诊室布局的记录都用于帮助创建算法。机器学习算法可用于检测疾病、计划治疗和预测。
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**用于机器学习的云平台的主要特点**:
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- 算法或特征提取
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- 关联规则挖掘
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- 基于大数据的预测分析
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- 分类、回归和聚类
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- 数据加载和转换
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- 数据准备、数据预处理和可视化
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- 降维
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- 分布式线性代数
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- 假设检验和核方法
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- 处理图像、音频、信号和视觉数据集
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- 模型选择和优化模块
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- 预处理和数据流编程
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- 推荐系统
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- 通过插件支持文本挖掘和图像挖掘
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- 可视化和绘图
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### 基于云的 AI 和 ML 应用程序部署
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AI 和 ML 的应用可以部署在云平台上。如今，许多云服务提供商使程序员能够构建模型以在其领域内进行有效的决策。
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这些基于云的平台与预先训练的机器学习和深度学习模型集成在一起，无需任何编码或最少的脚本即可在这些模型上部署应用程序。
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![Figure 3: Categories of ML deployments in Streamlit][3]
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**Streamlit:** Streamlit 让数据科学家和机器学习专家能够访问各种机器学习模型。它是开源的并且与云部署兼容。ML 模型可以在几分钟内准备好与数据集一起使用
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Streamlit 提供一系列机器学习模型和多个类别的源代码，包括自然语言处理、地理、教育、计算机视觉等。
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![Figure 4: Hugging Face for machine learning][4]
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**Hugging Face:** 这是另一个平台，为各种类别的 ML 和 AI 提供预先训练的模型和架 构。许多企业巨头都在使用这个平台，包括 Facebook AI、微软、谷歌 AI、亚马逊网络服务和 Grammarly。
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Hugging Face 中提供了许多预训练和部署就绪的模型，用于不同的应用程序，包括自然语言处理和计算机视觉。
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使用 Hugging Face 中的 ML 模型可以执行以下任务:
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- 音频到音频处理
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- 自动语音识别
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- 计算机视觉
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- 填充蒙版
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- 图像分类
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- 图像分割
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- 物体检测
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- 问题应答
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- 句子相似度
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- 总结
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- 文本分类
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- 文本生成
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- 文本到语音翻译
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- 令牌分类
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- 翻译分类
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Hugging Face 中可用的问题解决器经过优化且有效，有助于快速部署模型（图 5）。
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![Figure 5: Problem solvers and models in Hugging Face][5]
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这些基于云的平台对多个领域的研究人员、从业者和数据科学家非常有用，并简化了性能良好的实际应用程序的开发。
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---
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via: https://www.opensourceforu.com/2022/09/platforms-that-help-deploy-ai-and-ml-applications-on-the-cloud/
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作者：[Dr Kumar Gaurav][a]
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选题：[lkxed][b]
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译者：[Misite Bao](https://github.com/misitebao)
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校对：[校对者 ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux 中国](https://linux.cn/) 荣誉推出
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[a]: https://www.opensourceforu.com/author/dr-gaurav-kumar/
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[b]: https://github.com/lkxed
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[1]: https://www.opensourceforu.com/wp-content/uploads/2022/08/Figure-1-Key-applications-of-machine-learning.jpg
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[2]: https://www.opensourceforu.com/wp-content/uploads/2022/08/Figure-2-Streamlit-cloud-for-machine-learning.png
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[3]: https://www.opensourceforu.com/wp-content/uploads/2022/08/Figure-3-Categories-of-ML-deployments-in-Streamlit.png
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[4]: https://www.opensourceforu.com/wp-content/uploads/2022/08/Figure-4-Hugging-Face-for-machine-learning.png
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[5]: https://www.opensourceforu.com/wp-content/uploads/2022/08/Figure-5-Problem-solvers-and-models-in-Hugging-Face.png
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