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.

Overview

Agile Stacks announced at the HPE Discover 2019 conference that it was expanding its DevOps automation platform to support Google Cloud Platform (GCP), Microsoft Azure, and Red Hat OpenShift — the Kubernetes distribution — alongside its existing support for Amazon Web Services (AWS). The move positions the company to help enterprises manage DevOps pipelines across heterogeneous, hybrid cloud environments from a single control plane.

Hewlett-Packard Enterprise (HPE), an investor in Agile Stacks, used the same conference to outline how Kubernetes sits at the center of its own evolving hybrid cloud strategy — making the timing of the announcement strategically significant.

What the Agile Stacks Platform Does

According to company CEO John Mathon, the Agile Stacks platform provides a framework for automating the creation of DevOps pipelines across multiple tools and CI/CD platforms. Key capabilities include:

• Service Catalog: More than 80 DevOps tools and platforms available out of the box, including Kubeflow, Jenkins, GitLab, Spinnaker, Harbor, Minio, Ceph, and Portworx.
• Composable Stacks: DevOps teams configure custom stacks from catalog components. Each component is auto-configured to handle deployment, upgrades, rollbacks, promotions, cloning, backups, test-script generation, merges, performance testing, vulnerability scanning, and functional testing.
• GitOps Framework: An extensible GitOps-based integration layer ties together tools from multiple vendors, reducing the automation silos that typically emerge when organizations adopt best-of-breed tooling.
• Automated Cloud Tagging: Cost visibility across clouds is improved through automated cloud tagging applied via the SuperHub, helping teams track cloud spending without manual overhead.

The SuperHub: Automating DevOps Automation

At the core of the platform is the SuperHub automation hub. Rather than requiring DevOps engineers to wire together individual tools manually, SuperHub orchestrates the entire pipeline lifecycle — from initial provisioning through ongoing operations — using composable, version-controlled stack definitions.

Mathon described the product philosophy as “automating the DevOps automation.” Organizations that have matured their DevOps practices typically accumulate tools from multiple vendors. Without a unifying layer, those tools create isolated automation islands that can work at cross-purposes. The SuperHub and its GitOps framework are designed to resolve that fragmentation, allowing tooling choices to evolve as organizational DevOps maturity improves.

Addressing Hybrid Cloud Complexity

The multi-cloud expansion reflects a broader industry reality: as Kubernetes adoption accelerates, organizations increasingly want to run workloads across public clouds and on-premises infrastructure simultaneously. However, each platform carries its own DevOps tooling assumptions and operational patterns, which compounds complexity for platform and DevOps engineering teams.

Mathon argued that hybrid cloud adoption will ultimately push many organizations to standardize DevOps practices enterprise-wide — something that is difficult to achieve without a platform layer that abstracts away provider-specific differences. Agile Stacks positions its platform as that abstraction layer, enabling consistent pipeline behavior whether workloads run on AWS, GCP, Azure, OpenShift, or on-premises Kubernetes clusters.

Lowering the Barrier to DevOps Adoption

Beyond serving mature DevOps organizations, Mathon noted that Agile Stacks aims to reduce the initial complexity that prevents less experienced teams from adopting DevOps practices at all. By providing pre-integrated, auto-configured stacks through a service catalog, teams can get functional pipelines running without deep expertise in every individual tool.

This is particularly relevant for enterprises that are simultaneously navigating their first hybrid cloud deployments and trying to establish consistent delivery practices — two challenges that, when tackled together, can be overwhelming without appropriate tooling.

Key Takeaways

• Agile Stacks extended its platform to GCP, Azure, and Red Hat OpenShift in June 2019, adding to existing AWS support.
• The SuperHub automation hub uses composable, GitOps-managed stacks to orchestrate end-to-end DevOps pipelines.
• A service catalog of 80+ tools — including Jenkins, GitLab, Spinnaker, and Kubeflow — is available out of the box.
• Automated cloud tagging through SuperHub provides cost tracking across multi-cloud environments.
• The platform targets both DevOps-mature organizations struggling with tool fragmentation and newer adopters facing initial setup complexity.
• HPE is an investor in Agile Stacks and is aligning its own hybrid cloud strategy around Kubernetes.