DevOps Market to reach $15 billion by 2026

The global DevOps market size is projected to reach $14,969.6 million by 2026, a compound annual growth rate of 19.1%, according to a Fortune Business Insights report. The report highlighted the significance of this increase, noting nearly +404% in eight years, as that market was only worth $3,708.1 million in 2018. Containerization, PaaS (Platform as a Service) and hybrid cloud are three major enablers in DevOps growth.

For more information:

• Read this DevOps market TechRepublic article
• Read another market study on DevOps from Grand View Research expecting $12.85 billion by 2025 with a close CAGR of +18.6%, or this related Medium article

Agile Testing + DevOps = DevTestOps

"DevOps is now really DevTestOps and for teams to be truly agile, test management is the vital link in the success of DevOps. You require TestOps to match the pace of DevOps and testing early and often — breaking the silos."

In fact, the World Quality Report 2019-2020 led by Capgemini shows that there is increased investment in the QA and Test function reported by 90% of US and 69% percent of Canadian survey participants in the past four years.

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The Global Embedded Systems Market Expected to Grow +5% CAGR by 2024

An embedded system is a combination of software and hardware which together facilitate the accurate functioning of a target device. Embedded system market is expected to mark significant growth over 2019 to 2024 owing to increasing consumers spending on smart phones, providing high application- specified integrated circuit and high speed operating systems applications and technological advancement. 

A recent Advance Market Analytics market study is being classified by Type (Normal Phase HPLC and Reverse Phase HPLC), by Application (Automotive, Telecommunication, Healthcare, Industrial, Consumer Electronics and Military & Aerospace) and major geographies with country level break-up. According to this study, the Global Embedded Systems market is expected to see growth rate of 5.28% and may see market size of USD536.2 Million by 2024.

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Distinct AI Techniques Bring Different Business Values

Machine learning and deep learning are often conflated by business decision makers. Machine Learning can involve a wide variety of techniques for building analytics models or decision engines that don't involve neural networks, the mechanism for deep learning. And there is a whole range of AI techniques outside of machine learning as well that can be applied to solve business problems.

Do you leverage these techniques or do you prefer computer vision and natural language processing applications to solve your business problems?

Read George Lawton article in TechTarget

TESTAR test results extracted while executing MyThaiStar as web system under test

Authors: Fernando Pastor Ricos and Tanja E. Vos from Universitat Politècnica de València

TESTAR test results datasets extracted with TESTAR tool using MyThaiStar web application as System Under Test (SUT). These datasets have been generated to be used as an example to be automatically generated and introduced locally in DECODER PKM, from H2020 DECODER Project.

TESTAR tool is an open source tool for automated testing through graphical user interface (GUI) currently  being  developed  by  the Universitat Politecnica de Valencia and the Open University of the Netherlands.

MyThaiStar is the reference application that Capgemini uses internally to promote best programming practices and the correct use of last technologies. It’s is developed with Devon Framework, the standard tool for development at the company. More...

An MLOps approach to bring models to production

Machine Learning Open Studio and Model as a Service (MaaS) from Activeeon helps data scientists and IT operations work together in an MLOps approach allowing to bring ML models to production. Machine Learning Open Studio includes automatic data drift detection mechanisms and allows traceability and audit over model performance to retrain it when necessary.

Only a small percentage of ML projects make it to production because of deployment complexity, lack of governance tools and many other reasons. Once in production, ML models often fail to adapt to the changes in the environment and its dynamic data which results in performance degradation.

To maintain the prediction accuracy of ML models in production, an active monitoring of model performance is mandatory. This allows to know when to retrain it using the most recent data and the newest implementation techniques, then redeploy in production. More...

Algorithm and Data Structure Visualization

Visualizations can help us understand how data structures and algorithms work. 

The visualgo.net website provides great visualization and animations on advanced algorithms. Most of them are discussed in 'Competitive Programming', co-authored by two brothers Dr Steven Halim and Dr Felix Halim. Today, some of these advanced algorithms visualization/animation can only be found in VisuAlgo. 

An online quiz system has been added that allows students to test their knowledge of basic data structures and algorithms. It generates questions and check the student answers automatically.

Covid-19 infection in Italy: when AI provides vital insights

Thanks to mathematical models and predictions, Gianluca Malato - a Data Scientist, fiction author and software developer - compared logistic and exponential models applied to Covid-19 virus infection in Italy. Both models help to better understand the evolution of the infection. The data preparation and python coding are detailed in an article posted in Towards Data Science on 8 March 2020. At that time, the main projections - now checked regularly by this Covid-19 Italian infection collaborative research - were:

• The expected number of infected people at infection end is 15968 +/- 4174.
• The infection peak is expected around 9 March 2020.
• The expected infection should end on 15 April 2020.
• Read the Covid-19 infection in Italy article in Towards Data Science

Clear Linux OS automates the creation of RPM packaging

Designed by Intel and open source contributors, the Clear Linux OS delivers a secure, hardware optimized OS. Its updates ensure that software dependencies remain mutually compatible. 

The autospec tool is used to assist with the automated creation and maintenance of RPM packaging in Clear Linux OS. Where a standard RPM build process using rpmbuild requires a tarball and .spec file to start, autospec requires only a tarball and package name to start.

Recent reviews confirm the performance an stability improvements of Clear Linux OS. However, software that are packaged in other formats for other Linux distributions are not guaranteed to work on Clear Linux OS and may be impacted by Clear Linux OS updates. 

• The Clear Linux OS Project
• The Clear Linux AppStore

The Twelve-Factor App, a Methodology for Building Web Apps

Suggested by the designers of the Heroku PaaS platform, the twelve-factor methodology can be applied to apps written in any programming language, and which use any combination of backing services (database, queue, memory cache, etc). It is aimed at building Software-as-a-Service apps that:

1. Use declarative formats for setup automation, to minimize time and cost for new developers joining the project;
2. Have a clean contract with the underlying operating system, offering maximum portability between execution environments;
3. Are suitable for deployment on modern cloud platforms, obviating the need for servers and systems administration;
4. Minimize divergence between development and production, enabling continuous deployment for maximum agility;
5. And can scale up without significant changes to tooling, architecture, or development practices.

More about the Twelve-Factor App