Simplify the digital enterprise journey with hybrid multicloud

Organizations are adopting a hybrid multicloud environment to accelerate their journey to becoming digital enterprises. IT leaders are faced with a challenge of demystifying the hybrid multicloud environment to unlock the true value of digital transformation.
According to IBM Institute for Business Value, by 2021, 90 percent of the organizations that are already on cloud plan to adopt multiple hybrid clouds. However, only 30 percent have the required procedures and tools in place and just 30 percent have a multicloud orchestrator or other multicloud management platforms.
Build for variety, velocity and volume
Moving IT functions to the cloud can give organizations many benefits, but orchestration and automation across multiple technologies, cloud environments and service providers can be complex and expensive. To ensure success, enterprises must seek answers to the following:

How to build cloud native and DevOps capabilities in a safe, secure and cost-effective manner
How to avoid vendor lock-in and leverage the benefits of open architectures
How to orchestrate across multiple technologies and clouds
How to quickly build a virtualized or containerized platform for faster application development and deployment
How to enable development team to provision or deprovision environments efficiently
How to build infrastructure services for a multicloud environment

To ride on the success of cloud initiatives in the digital era, businesses today must build their cloud for variety, velocity and volume.

Variety – Build cloud to manage the variety of heterogeneous technology complexities of both container and virtual workloads and topologies of cloud deployment models.
Velocity – Build cloud to manage the speed of change and reduce the timelines to incorporate changes across multiple cloud end points.
Volume – Build cloud design to manage the scalability of capacity as required without disruption in efficiencies.

Address hybrid multicloud orchestration challenges
Businesses are grappling with cloud orchestration challenges owing to complexities of multiple technologies, cloud platforms and service provider environments. IBM Cloud Deployment Services (ICDS) offers a multicloud orchestration and automation platform for both virtualization and container workloads powered by enterprise-ready standard blueprints.
IBM Cloud Deployment Servicesis technology agnostic, supports open architecture and can help businesses

Automate delivery of infrastructure, applications, and custom IT services
Deploy application workloads across on-premises and off-premises environments (for example, public and private clouds)
Offer integration with all leading public cloud providers, such as Amazon Web Services (AWS) and Microsoft Azure
Offer integration for ServiceNow, resiliency offerings, managed security services, and so on
Available in both single-tenant and multitenant architectures
Available with Red Hat OpenShift Container Platform in addition to IBM and VMware orchestration capabilities
Enables design and build of solution blueprints
Includes build and deployment services (with required hardware and software licenses and delivery services of the platform)

IBM Cloud Deployment Services simplifies the journey to cloud by building cloud for variety, velocity and volume. To know more, visit us at
The post Simplify the digital enterprise journey with hybrid multicloud appeared first on Cloud computing news.
Quelle: Thoughts on Cloud

Going to VMWorld? Learn to help data scientists and application developers accelerate AI/ML initiatives

IT experts from around the world are  headed to VMworld 2019 in San Francisco to learn how they can leverage emerging technologies from VMware and ecosystem partners (e.g. Red Hat, NVIDIA, etc.) to help achieve the digital transformation for their organizations.  Artificial Intelligence (AI)/Machine Learning (ML) is a very popular technology trend, with Red Hat OpenShift customers like HCA Healthcare, BMW, Emirates NBD, and several more are offering differentiated value to their customers. Investments are ramping up across many industries to develop intelligent digital services that help improve customer satisfaction, and gain competitive business advantages. Early deployment trends indicate AI/ML solution architectures are spanning across edge, data center, and public clouds.
If you are part of the IT group, you may have already been asked to support the data scientists and software developers in your organization that are driving the development of machine learning models and the associated intelligent applications. 
Data scientists play a vital role in the success of AI/ML projects. They are primarily responsible for ML model selection, training, and testing. They also need to collaborate with data engineers and software developers to make sure the source data is credible, and the machine learning models are successful deployed in application development processes.
Here are some of the key challenges faced by data scientists as they strive to efficiently build the ML models: 

Selecting & deploying the right ML tooling or framework
Complexities and time required to train, test, and select the ML model providing the highest prediction accuracy
Slow execution of ML modeling computational tasks because of lack of powerful IT infrastructure
Dependency on IT to provision and manage infrastructure 
Collaboration with other key contributors e.g. data engineers, application developers, etc. 

If I were  a data scientist, I would want a “self-service cloud like” experience for my ML projects. This experience should allow me to access a rich set of ML modelling frameworks, data, and computational resources across edge, data center, and public clouds. I should be able to share work and collaborate with my colleagues, and deliver my work into production with agility and repeatability to achieve business value.
This is where containers and Kubernetes-based hybrid cloud solutions like Red Hat OpenShift Container Platform and NVIDIA GPUs, on VMware vSphere, come into play. It can help extend the value of your vSphere investments, and drive the mainstream adoption of AI/ML powered intelligent apps. 
There are several benefits that can be achieved with this solution, including:

Agility across the ML pipeline by automating the install, provisioning, and autoscaling of the containers based ML models/frameworks.  NVIDIA GPUs can help speed up the massive computational tasks required to train, test, and fine tune the ML models without having to buy more compute and storage resources, with Red Hat OpenShift serving as the container and Kubernetes based “self service cloud.”
Portability and flexibility for ML powered apps to be developed and delivered across data center, edge, and public clouds. OpenShift also provides flexibility to offer ML-as-a-service to apps without having to embed the ML models directly in the application code for production use.
Efficient operations and lifecycle management for ML powered intelligent applications with automation of the CI/CD process, enabling more efficient collaboration and helping to boost productivity. 

While you are at VMworld, don’t miss your chance to learn more on this topic. Come check out the mini-theatre session from Red Hat’s Andrew Sullivan at the NVIDIA booth in the expo center at 12:45pm on Monday, August 26th, 2019.
Please also check out the Red Hat AI/ML blog here to learn more, and also our announcement with NVIDIA to learn more about the strategic partnership between Red Hat and NVIDIA to accelerate and scale AI/ML across Hybrid Cloud. 
The post Going to VMWorld? Learn to help data scientists and application developers accelerate AI/ML initiatives appeared first on Red Hat OpenShift Blog.
Quelle: OpenShift