IBM Corp. said today it’s hoping to provide a standardized solution for developers to create and deploy machine learning models in production and make them portable to any cloud platform.

To do so, it said it’s open-sourcing the Kubeflow machine learning platform on Tekton, a continuous integration/continuous development platform developed by Google LLC. It’s popular with developers who use Kubernetes to manage containerized applications, which can run unchanged across many computing environments.

IBM said it created Kubeflow Pipelines on Tekton in response to the need for a more reliable solution for deploying, monitoring and governing machine learning models in production on any cloud platform. That’s important, IBM says, because hybrid cloud models are rapidly becoming the norm for many enterprises that want to take advantage of the benefits of running their most critical business applications across distributed computing environments.

One of the main complaints of data science and application development and delivery teams is the difficulty of creating portable machine learning models that can run on any cloud, Animesh Singh, IBM’s chief architect of data and AI open source, and Trent Gray-Donald, a distinguished engineer in AI, said in a blog post. They cite problems such as manual handoffs, frantic monitoring and loose governance, which can cause big delays in the deployment of new models.

IBM’s response to this has been to double down on Machine Learning Operations, or MLOps, which refers to a practice for collaboration and communication between data scientists and operations professionals to help manage production ML lifecycles.

IBM has since adopted Kubeflow Pipelines as the basis of its MLOps. Kubeflow is a machine learning platform that’s focused on distributed training, hyperparameter optimization, production model serving and management. Singh and Gray-Donald said Kubeflow was chosen primarily for its ability to tap into the Kubernetes ecosystem, which makes it compatible with containers and gives it greater scalability.

Kubeflow Pipelines also uses the Python interconnected that’s familiar to many developers for defining and deploying Pipelines. That enables metadata collection and lineage tracking of ML models.

Now, IBM has decided to open-source a revamped version of Kubeflow Pipelines especially for Tekton, which is an open-source project that provides a framework to create cloud-native continuous integration/continuous deployment pipelines quickly. With Tekton, developers can build, test, and deploy CI/CD pipelines across multiple cloud providers or on-premises systems by abstracting away the underlying implementation details.

“The decision to adopt Kubeflow Pipelines on our side came with an internal requirement to redesign Kubeflow Pipelines to run on top of Tekton,” Singh and Gray-Donald said.

Available today, the new Kubeflow Pipelines on Tekton provides Kubernetes-style resources for developers to declare CI/CD pipelines. It also introduces several new Custom Resource Definitions such as Task, Pipeline, TaskRun and PipelineRun.

In addition, IBM has created OpenShift Pipelines based on Tekton for its Red Hat OpenShift platform. That’s used to automate the building, testing and deployment of containerized applications for on-premises and public cloud platforms.

“Within IBM, we have standardized on Tekton as a cloud CI/CD engine, and OpenShift Pipelines is based on Tekton,” Singh and Gray-Donald said.

IBM is also planning to add to its stack with the coming launch of Watson AI Platform Pipelines. That will provide features that make it even easier for developers to build ML pipelines with a drag-and-drop canvas, pipeline components registry and production-grade logging. It will also provide better governance capabilities via integrations with IBM Watson Studio, Watson AutoAI, Watson Knowledge Catalog and others.

Continuous Integration Tools Market Research Report covers the present scenario and the growth prospects of the Keyword Industry for 2020-2026. The report covers the market landscape and its growth prospects over the coming years and discussion of the Leading Companies effective in this market. Continuous Integration Tools Market has been prepared based on an in-depth market analysis with inputs from industry experts. To calculate the market size, the report considers the revenue generated from the sales of Keyword globally.

The Continuous Integration Tools market research study considers the present scenario of the Continuous Integration Tools industry and its market dynamics for the period 2020−2026. The report covers both the demand and supply aspects of the market. Continuous Integration Tools market research report provides market sizing, share, forecast – estimation & approach, Covid19 aftermath – Analyst view, strategic analysis, revenue opportunities, industry trends, competition outlook, insights and growth – relevancy mapping, growth drivers, and vendor analysis.

Continuous Integration Tools Market reports under the Continuous Integration Tools industry are supported by various macro and microeconomic factors impacting the industry. We browse through historical data and provide an overview of the emerging markets and the next big opportunities for investors within the niche market. After COVID pandemic, there are increasing demand from emerging countries provides a good business opportunity for companies to invest in coming years. Our reports are updated with changing industry regulatory policies and offer insight depending on client’s requirement.

The global Continuous Integration Tools market has been subjected to several regulatory compliances and crucial coding terminology over the years. Adherence to regulatory standards remains crucial for vendors.

The study profiles and examines leading companies and other prominent companies operating in the Continuous Integration Tools industry.

List of key players profiled in the report:

IBM
Atlassian
Red Hat
CA Technologies
Puppet
Cloudbees
AWS
Microsoft
Oracle
Micro Focus
Circleci
Jetbrains
Shippable
Electric Cloud
Smartbear
Vsoft Technologies
Autorabit
Appveyor
Drone.Io
Rendered Text
Bitrise
Nevercode
Continuous Integration Tools Market segmentation as per below:

Based on Product Types:

On Premises
Cloud
Applications covered in this report:

Banking, Financial Services, and Insurance
Retail and Ecommerce
Telecommunication
Education
Media and Entertainment
Healthcare
Manufacturing
Others
COVID-19 Impact on Continuous Integration Tools Industry

The outbreak of COVID-19 has bought along a global recession, which has impacted several industries. Along with this impact COVID Pandemic has also generated few new business opportunities for Continuous Integration Tools market. Overall competitive landscape and market dynamics of Continuous Integration Tools has been disrupted due to this pandemic. All these disruptions and impacts has been analysed quantifiably in this report, which is backed by market trends, events and revenue shift analysis. COVID impact analysis also covers strategic adjustments for Tier 1, 2 and 3 players of Continuous Integration Tools market.

The competitive environment in the Continuous Integration Tools market is intensifying. The market currently witnesses the presence of several major as well as other prominent vendors, contributing toward the market growth. However, the market is observing an influx of local vendors entering the market.

Vendors can consider targeting key regions such as APAC, North America, and Europe to gather maximum customer attention. Countries in the APAC region such as China, India, and Japan among others are expected to display significant growth prospects in the future due to high economic growth forecasts along with huge population statistics leading to high consumption of goods and products.

Regional Overview & Analysis of Continuous Integration Tools Market:

North America (US and Canada)
Europe (UK, Germany, France and Rest of Europe)
Asia Pacific (China, Japan, India and Rest of Asia Pacific)
Latin America (Brazil, Mexico and Rest of Latin America)
Middle East & Africa (GCC and Rest of Middle East & Africa)
The changing regulatory compliance scenario and the growing purchasing power among consumers are likely to promise well for the North America market. New product development and technological advancements remain key for competitors to capitalize upon in the Continuous Integration Tools industry across the globe.

JavaScript is the most popular programming language among developers followed by Python and then Java, according to analyst firm SlashData’s latest survey of developers. However, in terms of adding new developers within the last one year Python leads with 2.2 million developers. Python can be classed as the fastest-growing programming language in 2020.

The company’s State of the Developer Nation Q3 2020 survey includes responses from more than 17,000 developers in 159 countries, covering the most popular programming languages, developer attitudes to open-source software, adoption of DevOps practices, and usage of popular cloud technologies, such as containers and orchestration tools like Kubernetes.

According to SlashData, JavaScript, which includes CoffeeScript and Microsoft’s TypeScript in the survey, is by far the most popular language, with 12.4 million developers reportedly using it worldwide.

It estimates five million developers have joined the JavaScript community since 2017, consolidating its position as the world’s most popular programming language.

“Even in software sectors where JavaScript is least popular, like data science or AR/VR, over a fifth of developers use it in their projects,” SlashData notes.

Despite weaknesses in web, mobile and desktop app development, Python has gained 2.2 million net new developers over the past year thanks to machine learning, data science and IoT app – or anything without a user interface.

Peter Wang, CEO of Anaconda, the maker of the Anaconda Python distribution for data science, told ZDNet in a recent interview it was “kind of a miracle that Python is even on the radar, much less ranked in the top three languages”, given its low adoption for mobile and application development.

“On the web, the frontend is always JavaScript or a derivative. And on mobile, Python is barely used at all,” he noted.

Nonetheless, Python’s developer population now stands at an estimated nine million users – or roughly 800,000 more than the estimated 8.2 million Java developers.

Python’s popularity overtook Java at the beginning of 2020, according to SlashData. This observation is consistent with several programming language popularity rankings.

Developer analyst RedMonk’s observed the switch in its June 2020 ranking, while Java hit an “all-time low” in Tiobe’s October 2020 popularity rankings and looks set to be overtaken by Python for second place. JavaScript and Python were also more popular than Java in Stack Overflow’s 2020 developer survey.

But Oracle argues 25-year-old Java is still hugely popular, used by over 69% of full-time developers worldwide with 51 billion active Java Virtual Machines (JVMs) deployed globally. And SlashData notes that the Java community gained 1.6 million developers since mid-2017, despite not growing in the past six months.

Languages with developers numbering between six million and 6.3 million include C and C++, PHP, and C#. In the next group of languages with between 2.3 million and 2.8 million developers are Google-endorsed Android app-development language Kotlin and Apple’s Swift. Swift overtook Kotlin in early 2020, according to SlashData.

According to SlashData, 60% of backend developers use containers, making it the most popular cloud technology, ahead of the 45% using database-as-a-service from the likes of AWS, Oracle, IBM, Google, and MongoDB.

Around 57% of backend developers who are using a containerized application orchestration tool choose Kubernetes.

Interestingly for DevOps adoption, SlashData found that 35% of developers don’t consider themselves to be engaged in the area, yet 40% of respondents say they practice continuous integration (CI) and 37% say they use continuous delivery or deployment (CD) – both being fundamental aspects of DevOps.

However, SlashData reckons the “full automation of the software release process – and therefore true commitment to the DevOps culture – is far from a reality”.

“While more than half (52%) of developers use CI or CD to streamline parts of their workflow, only 25% use both practices to automate all steps between integrating code changes into a central repository through to production deployment,” it notes. “As it turns out, developers are still skeptical about fully automated CI/CD pipelines. This is evident by the fact that nearly 40% of them manually give the green light for code deployments to be promoted to production.”

The four integrations add native support for Microsoft Azure Pipelines, a Jenkins Plugin, GitHub Actions and GitLab pipelines. A continuous integration/continuous delivery (CI/CD) Spoke capability on the ServiceNow platform makes it possible for developers to build automated pipelines using the Flow Designer tool.

Marcus Torres, vice president of product platform management at ServiceNow, said these latest integrations build on a DevOps integration initiative that ServiceNow launched last year. Rather than building its own DevOps platform, the company has opted to focus on integrating ticket-based ITSM processes with DevOps processes as the management of IT continues to evolve and mature.

In some cases, best DevOps practices for rapidly deploying applications is an absolute requirement, he noted. However, there are plenty of IT processes that are still best served by a ticket-based approach based on best practices defined within the widely adopted ITIL framework.

Since the initiative was launched, the number of organizations that are employing the low-code ServiceNow App Engine platform to build custom workflows that integrate DevOps platforms is also steadily increasing, said Torres. ServiceNow will continue to add other integrations with DevOps platforms based on customer demand with each successive update to its platform.

Torres said early tensions between ITIL and DevOps proponents are subsiding in favor of a more practical approach, which seeks to leverage the best of both approaches. The goal is to enable developer agility in a way that internal IT teams can support more reliably. The goal is to reduce to mean time to resolution whenever an incident arises from a recent change to the IT environment in real-time, noted Torres.

Overall, the rate at which IT management platforms have shifted to the cloud has accelerated in the wake of the COVID-19 pandemic. With more IT professionals working from home, remotely accessing management platforms via virtual private networks (VPNs) has shown itself to be cumbersome at best.

Longer-term, that shift to cloud will also enable IT teams to leverage artificial intelligence (AI) and robotic process automation tools to automate most low-level IT processes. The challenge IT organizations face today is on-premises IT management platforms are not able to aggregate enough data to automate IT processes reliably. However, AI capabilities will not replace IT administrators, but rather free them to address a wide range of more complex challenges, said Torres.

Arguably, more IT organizations are re-evaluating their existing IT processes now than at any other point in IT history. The shift to supporting employees who will be working from home much more frequently is seminal moment. Each IT organization will need to determine its own best path forward, but the one thing that is for certain now is, like it or not, there is no going back.