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.

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SAP, at its SAPPHIRE NOW Converge online conference, announced it has further unified its Business Technology Platform around a set of curated microservices from SAP and third-party partners that IT organizations can mix and match to their needs.

Gunther Rothermel, president of the SAP Cloud Platform, says SAP is on a journey toward migrating away from monolithic applications in favor of microservices-based applications based mainly on Kubernetes that are more flexible, resilient and easier to maintain.

The SAP strategy revolves around microservices that are deployed on top of the SAP HANA database. That approach enables SAP to provide organizations with access to a large portfolio of microservices that all share a common data model along with a common set of security, identity and access management frameworks, says Rothermel. In the case of SAP, those frameworks are shared by a broad portfolio of application software that is being re-engineered to be deployed as a set of microservices.

Via the SAP Cloud platform based on the Cloud Foundry platform-as-a-service (PaaS) environment running on Kubernetes, additional microservices can be constructed by third-parties or internal IT organizations, he says. At the core of that capability is Kyma, an open source runtime for extending applications using microservices and serverless computing frameworks developed by SAP.

Each of those microservices then can be accessed via Discovery Center, which SAP has added to its Business Technology Platform. Those microservices are grouped logically together by business function, so Rothermel says one module is likely to be made of several microservices that collectively deliver a specific business value. In fact, he notes, one of the dangers with microservices is making them too fine-grained, which only serves to make them more challenging to consume and manage.

Of course, microservices are not a new idea. The concept of managing and delivering applications as a service stretches back to service-oriented architectures (SOAs) that gained popularity more than 20 years ago. What has changed is technologies such as REST application programming interfaces (APIs) have made it more feasible to achieve that goal, notes Rothermel.

Most SAP customers still rely on monolithic SAP applications. However, SAP is making progress on transitioning customers to the cloud. There are now more than 32,400 customers who have deployed the SAP HANA platform. SAP also now makes it easier to consume application software as a microservice via subscription-based pricing plans.

It’s too early to say to what degree or even rate organizations will embrace microservices. While there is clearly a massive amount of interest, many IT organizations are still trying to determine how best to build, deploy, manage and secure them. As such, microservices in many cases are being employed as a way to more easily extend the functionality of existing legacy monolithic applications.

Regardless of the rate at which microservices are being embraced, however, application modernization initiatives are well underway. The challenge is, nobody is quite sure where those efforts are likely to lead beyond providing an alternative to monolithic applications that have already shown themselves to be too inflexible to meet the current needs of most businesses.

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The overall aim of the alliance between Microsoft and Kubota will see microsoft accelerate the company’s digital transformation, by migrating its IT infrastructure and SAP mission critical systems onto Microsoft Azure’s cloud platform.

In doing this, Kubota will standardise and streamline its business operations, accelerate its innovation and transfer to a solution provision model. In addition the two companies will work together to develop new artificial intelligence (AI) based solutions for business in the food, water and environment sectors. 

Kubota is committed to harnessing agricultural machinery and services to address the increasing food demands around the world. In using this technology, Kubota looks to problem solve with its customers in the agricultural sector to ensure they have what they need to manage their fields. 

To accelerate its vision to offer comprehensive solutions and products for the water and environmental sector Microsoft and Kubota will work together to harness Microsoft technologies and other industry expertise to develop a sustainable future.

Breakdown of the strategic alliance

Modernisation 

With this alliance, Kubota’s IT infrastructure and SP mission critical systems will be migrated onto Microsoft Azure’s cloud platform. As a result, this modernisation of operations with the help of Microsoft Azure will provide Kubota with the benefits of scale and flexibility. 

In addition to streamlining its operations and accelerating the use of integrated data globally with this modernisation, the company will also take advantage of cutting edge technology, such as AI.

Innovation

Kubota will also work with Microsoft to launch a new AI Machine Learning Lab, which will be focused on accelerating innovations. The lab will be harnessed to train new developers to build AI solutions. 

In addition, Kubota will explore the use of AI within its business operations as well as for developing new services for agriculture, water systems infrastructure and environment customers.  Microsoft will utilise its extensive knowledge and advanced technology to support these ambitions.  

Based on the two companies sustainability initiatives, the two will also explore the use of innovative technology to drive a more sustainable future.

“Under the slogan of ‘For Earth, For Life,’ Kubota is trying to dedicate himself to solve global issues on food, water, and environment. It is indispensable to promote digital transformation to meet such issues worldwide. This alliance with Microsoft is an important step for digital transformation. We accelerate to create innovations and provide the solutions and the social value,” commented Yuichi Kitao, President and Representative Director at Kubota.

”Kubota is recognized as a leader in environmentally-compatible farming and agricultural equipment. Combining its deep industry experience with the power of Microsoft Azure and AI capabilities will enable delivery of new innovations that help feed a hungry planet, ensure access to clean water, and promote overall sustainability,” added Judson Althoff, Executive Vice President at Microsoft.

About Kubota Corporation

Established in 1890, Kubota Corporation has over 130 years within the manufacturing industry for the food, water and environmental sectors. Kubota is committed to developing businesses to benefit society, businesses and human wellbeing around the world.

Gonshiro Kubota, the founder of Kubota believed that, “If you try hard, you can get it done. Do not be afraid of making mistakes. For the prosperity of society, we need to put all of our efforts into creation. Our products should not only be technically excellent, but also useful for the good of society.”

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Hybrid cloud—environments that blend public and private cloud—are a central part of the modern IT landscape, particularly as organizations of all sizes work to incorporate legacy systems into future-looking digital strategies and while meeting regulatory requirements. Although well-established, hybrid cloud is often poorly understood by IT departments, CIOs and partners, and its long-term trajectory is unclear to many that work with the cloud. As Microsoft positions Azure to play in a hybrid cloud world, MSDW reached out to experts for their thoughts on where hybrid cloud is headed next.

Assessing the benefits of hybrid

When it comes to hybrid cloud, many organizations adopt a hybrid approach to keep data under their own control, while gaining the benefits of scalability in the cloud. Adam Mansfield, commercial advisory practice leader at UpperEdge explained:

Using a hybrid cloud, enterprises have the ability to put sensitive data in a highly secure and controlled private cloud environment while having the ability to also place other non-sensitive data in an often more scalable, reliable and cost-effective public cloud environment, like Microsoft Azure, AWS, or Google Cloud Platform (GCP).

According to Dr. Taras Filatov, CEO and founder of blockchain-provider Dappros, the proportion of cloud components relative to on-prem continues to grow with time. To improve hybrid cloud performance, Filatov recommends using Kubernetes—which is still at a low level of adoption—to containerize applications. Additionally, he suggests taking advantage of machine learning and blockchain add-ons as well as leveraging public cloud compliance tools for regulations like GDPR.

In many cases, hybrid scenarios are a way for companies to boost agility while continuing to get value out of on-prem data centers. But in other situations, hybrid is a way to handle poor connectivity with remote locations like rural factories, farms, or oil and gas sites. What’s more, hybrid gives a way for companies to spend time unpacking years of what Andrew Bandera, DMI practice lead and solutions architect, calls “enterprise accretion,” tidying up massive customized systems as a staging area before a move to the public cloud.

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Oracle introduced new data services that expand the number of services available on its cloud platform.

The marquee new service from the software giant is the Oracle Cloud Infrastructure Data Science offering — an evolved version of the DataScience.com platform that Oracle acquired in 2018.

The Oracle Data Science service provides an automated workflow for machine learning and data analysis. Oracle is also launching a new data catalog service that helps users organize data for analysis. Another new capability is the Cloud SQL service that enables users query cloud data stores, while the Data Flow service enables users to run Apache Spark big data analysis as a service.

Oracle is playing to its strength in data with the new services, unveiled Feb. 12, according to Nucleus Research analyst Daniel Elman.

“Oracle made its name on database technology and remains to this day a preeminent leader in the space,” Elman said. “With these services, it’s leveraging this expertise with data management and offering its thousands of database customers a natural route to enabling data science initiatives without having to migrate data or learn new specialized tools.”

Oracle Data Science positioned for ease of use

Oracle is marketing the Data Science service as a way for teams of data scientists to work together collaboratively to generate machine learning models and then apply them to production applications.

The data science service has a project environment that sets up all the infrastructure and the networking needed to access data assets, as well as providing the tools needed for data science, explained Greg Pavlik, senior vice president of product development, data and AI services at Oracle. Among the tools is an automated machine learning feature that provides these capabilities for common data science tasks such as algorithm selection.

Oracle getting into the data catalog market
Alongside the Oracle Data Science service, the vendor launched a new data catalog to help organizations track all the data sets that come into a cloud deployment.

“Say you’re setting up a data warehouse, we can introspect the data warehouse model, and allow users — it could be data scientists, it could be data stewards, it can be analysts — to find out what data is available, who owns it and what it’s meant to be used for,” Pavlik said.

The Oracle data catalog also provides tagging capabilities that enable administrators to define taxonomies and start to organize data sets hierarchically.

Data Flow service enables Apache Spark Big Data
The new Data Flow service also helps meet a different need, enabling users to run Apache Spark jobs as service in the Oracle cloud. One of the challenges some organization face with running Spark analytics jobs is that they are often running on top of Hadoop clusters, which introduces additional complexity, Pavlik noted.

All that’s needed to run a big data workload in the Data Flow service is to upload the script, click on an application that is sort of the pointer to the script, and then specify how many CPUs the job should run on, Pavlik explained.

“We will synthesize the job on the fly in a totally serverless architecture, executing in tens of seconds,” he said. “We really think about this as a big generational leapfrog in terms of how to how to make big data workloads consumable by the enterprise.”

Oracle is also expanding the ability of users to query data in the cloud with the new

Oracle Cloud SQL offering. Users can use the SQL capability to query against cloud-based object stores.

“So you can reach out into a cloud-based data lake and apply the full semantic richness of the Oracle database,” Pavlik said.

Data integration service is coming

In addition to the Oracle Data Science services, the vendor has more data services in the works, among them a data integration service. Pavlik said that an upcoming data integration service will provide data preparation and ETL capabilities.

“It figures out where’s the most cost-effective way to run elements of the flow so that it’s filtering data and minimizing data movement,” Pavlik said. “It’s also filled with a data immersive view, so you can really drill down, understand your datasets and manipulate the data.”

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