DOCSLIB.ORG

Amazon Kinesis Video Streams Guida Per Gli Sviluppatori Amazon Kinesis Video Streams Guida Per Gli Sviluppatori

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Amazon Kinesis Video Streams Guida Per Gli Sviluppatori Amazon Kinesis Video Streams Guida Per Gli Sviluppatori Amazon Kinesis Video Streams Guida per gli sviluppatori Amazon Kinesis Video Streams Guida per gli sviluppatori Amazon Kinesis Video Streams: Guida per gli sviluppatori Copyright © Amazon Web Services, Inc. and/or its affiliates. All rights reserved. Amazon's trademarks and trade dress may not be used in connection with any product or service that is not Amazon's, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be affiliated with, connected to, or sponsored by Amazon. Amazon Kinesis Video Streams Guida per gli sviluppatori Table of Contents Che cos'è Amazon Kinesis Video Streams? ........................................................................................... 1 Primo utilizzo di Kinesis Video Streams? ........................................................................................ 2 Requisiti di sistema ............................................................................................................................. 3 Requisiti della videocamera .......................................................................................................... 3 Sistemi operativi testati ................................................................................................................ 3 Requisiti di storage per l'SDK ....................................................................................................... 4 Come funziona ................................................................................................................................... 5 Producer Library e API ................................................................................................................ 6 API Kinesis Video Streams .................................................................................................. 6 Producer Library ................................................................................................................. 8 Video Playback (Riproduzione video) ............................................................................................. 9 Riproduzione di video con HLS ............................................................................................. 9 Riproduzione video con MPEG-DASH .................................................................................. 13 Utilizzo dei metadati di streaming ................................................................................................ 15 Aggiunta di metadati a Kinesis Video Stream ........................................................................ 16 Consumo dei metadati incorporati in Kinesis Video Stream ...................................................... 17 Limitazioni dello streaming di metadati ................................................................................. 18 Modello di dati .......................................................................................................................... 18 Elementi di intestazione del flusso ....................................................................................... 19 Streaming dei dati della traccia ........................................................................................... 22 Elementi di intestazione del frame ....................................................................................... 23 Dati del frame MKV .......................................................................................................... 23 Nozioni di base ................................................................................................................................ 24 Fase 1: impostazione di un account ............................................................................................ 24 Registrazione ad AWS ....................................................................................................... 24 Creazione di un utente IAM amministratore ........................................................................... 25 Per creare una chiave account AWS ................................................................................... 25 Fase successiva ............................................................................................................... 26 Fase 2: creazione di un flusso video Kinesis ................................................................................. 26 Creazione di un flusso video tramite console ......................................................................... 26 Creazione di un flusso video tramite AWS CLI ...................................................................... 26 Fase successiva ............................................................................................................... 26 Fase 3: invio dei dati al flusso video Kinesis ................................................................................. 27 Fase successiva ............................................................................................................... 27 Fase 4: consumo di dati multimediali ........................................................................................... 27 Visualizzazione dei dati multimediali nella console ................................................................. 27 Consumo dei dati dei media tramite HLS .............................................................................. 27 Fase successiva ............................................................................................................... 27 Fasi successive ........................................................................................................................ 28 Sicurezza ......................................................................................................................................... 29 Protezione dei dati .................................................................................................................... 29 Cos'è la crittografia lato server per Kinesis Video Streams? ..................................................... 30 Considerazioni su costi, regioni e prestazioni ........................................................................ 30 Cosa devo fare per iniziare a utilizzare la crittografia lato server? ............................................. 31 Creazione e utilizzo di chiavi master AWS KMS generate dall'utente ......................................... 31 Autorizzazioni per l'uso di chiavi master AWS KMS generate dall'utente ..................................... 32 Controllo dell'accesso alle risorse Kinesis Video Streams che utilizzano IAM ...................................... 33 Sintassi delle policy ........................................................................................................... 33 Operazioni per Kinesis Video Streams ................................................................................. 34 Amazon Resource Name (ARN) per Kinesis Video Streams .................................................... 34 Concessione dell'accesso ad altri account IAM a un flusso di video Kinesis ................................ 35 Policy di esempio .............................................................................................................. 35 Controllo dell'accesso alle risorse Kinesis Video Streams utilizzando AWS IoT .................................... 37 IoT ThingName come nome del flusso ................................................................................. 37 iii Amazon Kinesis Video Streams Guida per gli sviluppatori IoT CertificateID come nome del flusso ................................................................................ 42 Monitoraggio ............................................................................................................................ 43 Convalida della conformità ......................................................................................................... 43 Flessibilità ................................................................................................................................ 44 Sicurezza dell'infrastruttura ......................................................................................................... 44 Best practice di sicurezza .......................................................................................................... 44 Implementazione dell'accesso ai privilegi minimi .................................................................... 44 Uso di ruoli IAM ............................................................................................................... 45 Uso di CloudTrail per il monitoraggio delle chiamate API ......................................................... 45 Producer Library ............................................................................................................................... 46 Client Kinesis Video Streams Producer ........................................................................................ 46 Kinesis Video Streams Producer Library ....................................................................................... 47 Argomenti correlati .................................................................................................................... 47 Java Producer Library ............................................................................................................... 47 Procedura:
Load more

Recommended publications
  • Building Machine Learning Inference Pipelines at Scale
    Building Machine Learning inference pipelines at scale Julien Simon Global Evangelist, AI & Machine Learning @julsimon © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Problem statement • Real-life Machine Learning applications require more than a single model. • Data may need pre-processing: normalization, feature engineering, dimensionality reduction, etc. • Predictions may need post-processing: filtering, sorting, combining, etc. Our goal: build scalable ML pipelines with open source (Spark, Scikit-learn, XGBoost) and managed services (Amazon EMR, AWS Glue, Amazon SageMaker) © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Apache Spark https://spark.apache.org/ • Open-source, distributed processing system • In-memory caching and optimized execution for fast performance (typically 100x faster than Hadoop) • Batch processing, streaming analytics, machine learning, graph databases and ad hoc queries • API for Java, Scala, Python, R, and SQL • Available in Amazon EMR and AWS Glue © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. MLlib – Machine learning library https://spark.apache.org/docs/latest/ml-guide.html • Algorithms: classification, regression, clustering, collaborative filtering. • Featurization: feature extraction, transformation, dimensionality reduction. • Tools for constructing, evaluating and tuning pipelines • Transformer – a transform function that maps a DataFrame into a new
    [Show full text]
  • Open Source in the Enterprise
    Open Source in the Enterprise Andy Oram and Zaheda Bhorat Beijing Boston Farnham Sebastopol Tokyo Open Source in the Enterprise by Andy Oram and Zaheda Bhorat Copyright © 2018 O’Reilly Media. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online edi‐ tions are also available for most titles (http://oreilly.com/safari). For more information, contact our corporate/institutional sales department: 800-998-9938 or [email protected]. Editor: Michele Cronin Interior Designer: David Futato Production Editor: Kristen Brown Cover Designer: Karen Montgomery Copyeditor: Octal Publishing Services, Inc. July 2018: First Edition Revision History for the First Edition 2018-06-18: First Release The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Open Source in the Enterprise, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the authors, and do not represent the publisher’s views. While the publisher and the authors have used good faith efforts to ensure that the informa‐ tion and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.
    [Show full text]
  • Serverless Predictions at Scale
    Serverless Predictions at Scale Thomas Reske Global Solutions Architect, Amazon Web Services © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Serverless computing allows you to build and run applications and services without thinking about servers © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. What are the benefits of serverless computing? No server management Flexible scaling $ Automated high availability No excess capacity © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Machine Learning Process Fetch data Operations, Clean monitoring, and and optimization format data Integration Prepare and and deployment transform data Evaluation and Model Training verificaton © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Machine Learning Process Fetch data Operations, Clean How can I use pre-trained monitoring, and and models for serving optimization format data predictions? How to scale effectively? Integration Prepare and and deployment transform data Evaluation and Model Training verificaton © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. What exactly are we deploying? © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. A Pre-trained Model... • ... is simply a model which has been trained previously • Model artifacts (structure, helper files, parameters, weights, ...) are files, e.g. in JSON or binary format • Can be serialized (saved) and de-serialized (loaded) by common deep learning frameworks • Allows for fine-tuning of models („head-start“ or „freezing“), but also to apply them to similar problems and different data („transfer learning“) • Models can be made publicly available, e.g. in the MXNet Model Zoo © 2018, Amazon Web Services, Inc.
    [Show full text]
  • Analytics Lens AWS Well-Architected Framework Analytics Lens AWS Well-Architected Framework
    Analytics Lens AWS Well-Architected Framework Analytics Lens AWS Well-Architected Framework Analytics Lens: AWS Well-Architected Framework Copyright © Amazon Web Services, Inc. and/or its affiliates. All rights reserved. Amazon's trademarks and trade dress may not be used in connection with any product or service that is not Amazon's, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be affiliated with, connected to, or sponsored by Amazon. Analytics Lens AWS Well-Architected Framework Table of Contents Abstract ............................................................................................................................................ 1 Abstract .................................................................................................................................... 1 Introduction ...................................................................................................................................... 2 Definitions ................................................................................................................................. 2 Data Ingestion Layer ........................................................................................................... 2 Data Access and Security Layer ............................................................................................ 3 Catalog and Search Layer ...................................................................................................
    [Show full text]
  • Ezgi Korkmaz Outline
    KTH ROYAL INSTITUTE OF TECHNOLOGY BigDL: A Distributed Deep Learning Framework for Big Data ACM Symposium on Cloud Computing 2019 [Acceptance Rate: 24%] Jason Jinquan Dai, Yiheng Wang, Xin Qiu, Ding Ding, Yao Zhang, Yanzhang Wang, Xianyan Jia, Cherry Li Zhang, Yan Wan, Zhichao Li, Jiao Wang, Sheng- sheng Huang, Zhongyuan Wu, Yang Wang, Yuhao Yang, Bowen She, Dongjie Shi, Qi Lu, Kai Huang, Guoqiong Song. [Intel Corporation] Presenter: Ezgi Korkmaz Outline I Deep learning frameworks I BigDL applications I Motivation for end-to-end framework I Drawbacks of prior approaches I BigDL framework I Experimental Setup and Results of BigDL framework I Critique of the paper 2/16 Deep Learning Frameworks I Big demand from organizations to apply deep learning to big data I Deep learning frameworks: I Torch [2002 Collobert et al.] [ C, Lua] I Caffe [2014 Berkeley BAIR] [C++] I TensorFlow [2015 Google Brain] [C++, Python, CUDA] I Apache MXNet [2015 Apache Software Foundation] [C++] I Chainer [2015 Preferred Networks] [ Python] I Keras [2016 Francois Chollet] [Python] I PyTorch [2016 Facebook] [Python, C++, CUDA] I Apache Spark is an open-source distributed general-purpose cluster-computing framework. I Provides interface for programming clusters with data parallelism 3/16 BigDL I A library on top of Apache Spark I Provides integrated data-analytics within a unifed data analysis pipeline I Allows users to write their own deep learning applications I Running directly on big data clusters I Supports similar API to Torch and Keras I Supports both large scale distributed training and inference I Able to run across hundreds or thousands servers efficiently by uses underlying Spark framework 4/16 BigDL I Developed as an open source project I Used by I Mastercard I WorldBank I Cray I Talroo I UCSF I JD I UnionPay I GigaSpaces I Wide range of applications: transfer learning based image classification, object detection, feature extraction, sequence-to-sequence prediction, neural collaborative filtering for reccomendation etc.
    [Show full text]
  • Apache Mxnet on AWS Developer Guide Apache Mxnet on AWS Developer Guide
    Apache MXNet on AWS Developer Guide Apache MXNet on AWS Developer Guide Apache MXNet on AWS: Developer Guide Copyright © Amazon Web Services, Inc. and/or its affiliates. All rights reserved. Amazon's trademarks and trade dress may not be used in connection with any product or service that is not Amazon's, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be affiliated with, connected to, or sponsored by Amazon. Apache MXNet on AWS Developer Guide Table of Contents What Is Apache MXNet? ...................................................................................................................... 1 iii Apache MXNet on AWS Developer Guide What Is Apache MXNet? Apache MXNet (MXNet) is an open source deep learning framework that allows you to define, train, and deploy deep neural networks on a wide array of platforms, from cloud infrastructure to mobile devices. It is highly scalable, which allows for fast model training, and it supports a flexible programming model and multiple languages. The MXNet library is portable and lightweight. It scales seamlessly on multiple GPUs on multiple machines. MXNet supports programming in various languages including Python, R, Scala, Julia, and Perl. This user guide has been deprecated and is no longer available. For more information on MXNet and related material, see the topics below. MXNet MXNet is an Apache open source project. For more information about MXNet, see the following open source documentation: • Getting Started – Provides details for setting up MXNet on various platforms, such as macOS, Windows, and Linux.
    [Show full text]
  • Open Source in the Enterprise
    opensource.amazon.com 企业中的开放源代码 Andy Oram 和 Zaheda Bhorat 企业中的开放源代码 作者：Andy Oram 和 Zaheda Bhorat 版权 © 2018 O'Reilly Media 所有。保留所有权利。 本书采用知识共享组织署名-非商业性使用 4.0 国际许可协议进行授权。要查看该许可， 请访问 http://creativecommons.org/licenses/by-nc/4.0/ 或致函以下地址：Creative Commons, PO Box 1866, Mountain View, CA 94042, USA。 本书中表达的观点是作者的观点，并不代表出版商的看法。虽然出版商和作者都真诚地付出 努力来确保本书中所含的信息和说明准确无误，但双方对错误或遗漏之处（包括但不限于因 使用或依据本书而导致的任何损失）概不负责。使用本书中所含的信息和说明带来的风险将 由您自行承担。如果本书中包含或介绍的任何代码示例或其他技术需遵循开源许可证或涉及 他人知识产权，您应负责确保对相应代码示例和技术的使用遵循此类许可证和/或知识产权 规定。 本书由 O'Reilly 与 Amazon 协作完成。 目录 鸣谢 ................................................................................................................................... vii 企业中的开放源代码 ..................................................................................................... 1 为什么公司和政府均改为使用开放源代码？ ..................................................................2 不仅仅需要许可证或代码 .....................................................................................................4 了解开放源代码的准备工作 ................................................................................................5 采用开放源代码 ......................................................................................................................7 参与项目社区 ........................................................................................................................ 13 为开源项目做贡献 ............................................................................................................... 17 启动开源项目 .......................................................................................................................
    [Show full text]
  • Anima Anandkumar
    ANIMA ANANDKUMAR DISTRIBUTED DEEP LEARNING PRACTICAL CONSIDERATIONS FOR MACHINE LEARNING SOFTWARE PACKAGES TITLE OF SLIDE UTILITY DIVERSE & COMPUTING LARGE DATASETS CHALLENGES IN DEPLOYING LARGE-SCALE LEARNING TITLE OF SLIDE CHALLENGES IN DEPLOYING LARGE-SCALE LEARNING • Complex deep network TITLE OF SLIDE • Coding from scratch is impossible • A single image requires billions floating-point operations • Intel i7 ~500 GFLOPS • Nvidia Titan X: ~5 TFLOPS • Memory consumption is linear with number of layers DESIRABLE ATTRIBUTES IN A ML SOFTWARE PACKAGE {…} TITLE OF SLIDE PROGRAMMABILITY PORTABILITY EFFICIENCY Simplifying network Efficient use In training definitions of memory and inference TensorFlow TITLE OF SLIDE Torch CNTK MXNET IS AWS’S DEEP LEARNING TITLE OF SLIDE FRAMEWORK OF CHOICE MOST OPEN BEST ON AWS Apache (Integration with AWS) {…} {…} PROGRAMMABILITY frontend backend TITLE OF SLIDE single implementation of performance guarantee backend system and regardless which front- common operators end language is used IMPERATIVE PROGRAMMING PROS • Straightforward and flexible. import numpy as np a = np.ones(10) • Take advantage of language b = np.ones(10) * 2 native features (loop, c = b * a condition, debugger) TITLE OF SLIDE cd = c + 1 • E.g. Numpy, Matlab, Torch, … Easy to tweak CONS with python codes • Hard to optimize DECLARATIVE PROGRAMMING A = Variable('A') PROS B = Variable('B') • A B More chances for optimization C = B * A • Cross different languages D = C + 1 X 1 f = compile(D) • E.g. TensorFlow, Theano, + TITLE OF SLIDE d = f(A=np.ones(10),
    [Show full text]
  • Arxiv:1907.04433V2 [Cs.LG] 13 Feb 2020 by Gluoncv and Gluonnlp to Allow for Software Distribution, Modification, and Usage
    Journal of Machine Learning Research 21 (2020) 1-7 Submitted 5/19; Revised 1/20; Published 2/20 GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing Jian Guo [email protected] University of Michigan MI, USA He He [email protected] Tong He [email protected] Leonard Lausen [email protected] ∗Mu Li [email protected] Haibin Lin [email protected] Xingjian Shi [email protected] Chenguang Wang [email protected] Junyuan Xie [email protected] Sheng Zha [email protected] Aston Zhang [email protected] Hang Zhang [email protected] Zhi Zhang [email protected] Zhongyue Zhang [email protected] Shuai Zheng [email protected] Yi Zhu [email protected] Amazon Web Services, CA, USA Editor: Antti Honkela Abstract We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-the-art pre-trained models, training scripts, and training logs, to facilitate rapid prototyping and promote reproducible research. We also provide modular APIs with flex- ible building blocks to enable efficient customization. Leveraging the MXNet ecosystem, the deep learning models in GluonCV and GluonNLP can be deployed onto a variety of platforms with different programming languages. The Apache 2.0 license has been adopted arXiv:1907.04433v2 [cs.LG] 13 Feb 2020 by GluonCV and GluonNLP to allow for software distribution, modification, and usage. Keywords: Machine Learning, Deep Learning, Apache MXNet, Computer Vision, Nat- ural Language Processing 1. Introduction Deep learning, a sub-field of machine learning research, has driven the rapid progress in artificial intelligence research, leading to astonishing breakthroughs on long-standing prob- lems in a plethora of fields such as computer vision and natural language processing.
    [Show full text]
  • Big Data Management of Hospital Data Using Deep Learning and Block-Chain Technology: a Systematic Review
    EAI Endorsed Transactions on Scalable Information Systems Research Article Big Data Management of Hospital Data using Deep Learning and Block-chain Technology: A Systematic Review Nawaz Ejaz1, Raza Ramzan1, Tooba Maryam1,*, Shazia Saqib1 1 Department of Computer Science, Lahore Garrison University, Lahore, Pakistan Abstract The main recompenses of remote and healthcare are sensor-based medical information gathering and remote access to medical data for real-time advice. The large volume of data coming from sensors requires to be handled by implementing deep learning and machine learning algorithms to improve an intelligent knowledge base for providing suitable solutions as and when needed. Electronic medical records (EMR) are mostly stored in a client-server database and are supported by enabling technologies like Internet of Things (IoT), Sensors, cloud, big data, Deep Learning, etc. It is accessed by several users involved like doctors, hospitals, labs, insurance providers, patients, etc. Therefore, data security from illegal access is crucial especially to manage the integrity of data . In this paper, we describe all the basic concepts involved in management and security of such data and proposed a novel system to securely manage the hospital’s big data using Deep Learning and Block-Chain technology. Keywords: Electronic medical records, big data, Security, Block-chain, Deep learning. Received on 23 December 2020, accepted on 16 March 2021, published on 23 March 2021 Copyright © 2021 Nawaz Ejaz et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
    [Show full text]
  • Deep Learning on AWS
    Deep Learning on AWS AWS Classroom Training Course description In this course, you’ll learn about AWS’s deep learning solutions, including scenarios where deep learning makes sense and how deep learning works. You’ll learn how to run deep learning models on the cloud using Amazon SageMaker and the MXNet framework. You’ll also learn to deploy your deep learning models using services like AWS Lambda while designing intelligent systems on AWS. Course level: Intermediate Duration: 1 day Activities This course includes presentations, group exercises, and hands-on labs. Course objectives In this course, you will: Learn how to define machine learning (ML) and deep learning Learn how to identify the concepts in a deep learning ecosystem Use Amazon SageMaker and the MXNet programming framework for deep learning workloads Fit AWS solutions for deep learning deployments Intended audience This course is intended for: Developers who are responsible for developing deep learning applications Developers who want to understand the concepts behind deep learning and how to implement a deep learning solution on AWS Cloud Prerequisites We recommend that attendees of this course have: A basic understanding of ML processes Knowledge of AWS core services like Amazon EC2 and AWS SDK Knowledge of a scripting language like Python Enroll today Visit aws.training to find a class today. © 2021, Amazon Web Services, Inc. or its affiliates. All rights reserved. Template version 12/2020. Deep Learning on AWS AWS Classroom Training Course outline Module 1: Machine
    [Show full text]
  • Nexedi Stack: Less Is More AWS Vs. Rapid.Space
    Most cloud services could be built by combining an open source / Free Software (eg. MariaDB) together with a service lifecycle automation (SLM) platform (eg. SlapOS). With one notable exception, this goal has not yet been reached by the Open Source / Free Software community because most projects are still focusing on some kind of virtualisation (eg. virtual machines, containers) or orchestration (eg. Kuberneres) which only represent 10% to 20% of what is necessary to implement a public cloud service. This may leave SlapOS as the only open source / Free Software project that could possibly match leading public cloud services (AWS, Azure, Alicloud), as the following comparison table highlights: SlapOS OpenStack Kubernetes Jupyter NixOS AWS IaaS ✔ ✔ ✔ PaaS ✔ ✔ ✔ ✔ ✔ Service App Store ✔ ✔ ✔ Orchestration ✔ ✔ ✔ ✔ Virtualisation ✔ ✔ ✔ Network management ✔ ✔ ✔ Resilient networking ✔ ✔ Bare metal encapsulation ✔ ✔ ✔ ✔ Portability ✔ limited ? Multi-tenant services (eg. CDN) ✔ ✔ Edge computing ✔ ✔ Continuous integration ✔ ✔ Self-monitoring ✔ ✔ Autonomous convergence ✔ ? Automated DR ✔ ✔ Accounting & Billing ✔ ✔ Nexedi stack: less is more Nexedi develops and operates complex, scalable applications with less than 15 software: the Nexedi Freee Software stack. Out of those 15 software, developers actually focus on four of them: ERP5 for enterprise records management (including CRM, MRP, MES) OfficeJS for Progressive Web Application (PWA) development; SlapOS for IT infrastructure automation (on the cloud, at the edge or in the workshop); Wendelin for data collection and processing (including AI and conversion). Since both SlapOS, Wendelin and OfficeJS are just variations of ERP5, Nexedi developers actually only need to learn a single framework (ERP5) and a single language (python). By relying on less tools, Nexedi developers have more time to learn ERP5 in depth.
    [Show full text]