Following the development of digitization, a growing number of large Original Equipment Manufacturers (OEMs) are adapting computer vision or natural language processing in a wide range of applications such as anomaly detection and quality inspection in plants. Deployment of such a system is becoming an extremely important topic. Our work starts with the least-automated deployment technologies of machine learning systems includes several iterations of updates, and ends with a comparison of automated deployment techniques. The objective is, on the one hand, to compare the advantages and disadvantages of various technologies in theory and practice, so as to facilitate later adopters to avoid making the generalized mistakes when implementing actual use cases, and thereby choose a better strategy for their own enterprises. On the other hand, to raise awareness of the evaluation framework for the deployment of machine learning systems, to have more comprehensive and useful evaluation metrics (e.g. table 2), rather than only focusing on a single factor (e.g. company cost). This is especially important for decision-makers in the industry.

IBM Corp. today introduced new versions of its Cloud Pak for Data and Cloud Pak for Automation products that will enable enterprises to harness deep learning models in their operations more easily. The Cloud Pak product family is a set of software solutions designed to streamline a variety of tasks ranging from cybersecurity to data analytics. The entire lineup is based on Red Hat OpenShift. Thanks to OpenShift, the solutions can run both in the public cloud and on on-premises infrastructure. Deep learning is a branch of artificial intelligence that uses artificial neural networks to learn from massive amounts of data. IBM Chief Executive Arvind Krishna (pictured) in October named the hybrid cloud and artificial intelligence as core pillars of the company's revenue growth strategy.

Today, we're making the Azure Service Operator available on OperatorHub.io to enable users to connect OpenShift clusters to Azure services, including EventHub, Azure SQL, CosmosDB, Storage Accounts, ...

Machine learning must address a daunting breadth of functionalities around building, training, serving, and managing models. Doing so in a consistent, composable, portable, and scalable manner is hard. The Kubernetes framework is well suited to address these issues, which is why it's a great foundation for deploying machine learning workloads. The Kubeflow project's development has been a journey to realize this promise, and we are excited that journey has reached its first major destination – Kubeflow 1.0. Always ready to work with a strong and diverse community, IBM joined this Kubeflow journey early on.

Harnessing the power of emerging technologies like artificial intelligence, machine learning and big data analytics to make smarter business decisions and improve customer experiences? It will, however, require that you make some complex technology decisions that work for your unique business needs. That's where global systems integrators (GSIs) often come in and do what their names suggest. They solve complex business challenges with customized solution integrations that bring together platforms, applications and hardware. With open source comes greater choice, control and freedom to do what you want.

When I speak to customers about digital transformation or innovation they understand the need for change but are held back on these areas due to internal IT pressures around migration and legacy architecture. These systems need to be rebuilt so they are robust, more secure and can deliver the right experience for the customer. Furthermore, such projects often take months to deliver so the benefits to a business can take several years to be realized. However, what if you don't need to reinvent the wheel but simply recalibrate it to work smarter for your business: ie. We've been working with Atos to deploy Atos Managed OpenShift (AMOS) across many industries, including manufacturing, pharmaceutical and logistics.

At the Computex event in Taiwan, NVIDIA unveiled EGX, a multi-cloud and AI-enabled edge computing platform for enterprises. NVIDIA EGX is a unified edge computing stack that can span from the tiny Jetson Nano to a full rack of T4 servers. Customers can start small with EGX and gradually scale to support full-blown GPUs. NVIDIA is optimizing the software stack to power devices such as drones to dedicated servers that can handle AI inferencing at scale. NVIDIA Edge Stack is an optimized platform powered by NVIDIA drivers, a CUDA Kubernetes plugin, a CUDA container runtime, CUDA-X libraries and containerized AI frameworks and applications such as TensorRT, TensorRT Inference Server and DeepStream SDK.

IBM has been on a roll lately with POWER Systems. Principal analyst and colleague Patrick Moorhead wrote about Google deploying POWER9 announced at the OpenPOWER Summit and last week I wrote about IBM expanding its platform HCI footprint with Nutanix. As another output of a long partnership, IBM and Red Hat recently announced the availability of IBM Cloud Private on Red Hat OpenShift/ Red Hat Enterprise Linux (RHEL). Additionally, IBM recently announced the availability of PowerAI for Red Hat Enterprise Linux (RHEL) running on IBM POWER9 platforms. What does this mean and how does this impact the enterprise IT organization?

Red Hat's customers are increasingly investing in and adopting artificial intelligence (AI) and machine learning (ML) to better serve their customers, create value, grow their business, and reduce cost and complexity. "Our customers see great potential in using Machine Learning (ML) to solve their business challenges. Technical advances in hardware acceleration and innovation in open source frameworks make ML a viable tool," said Chris Wright, Chief Technology Officer, Red Hat. We are listening to our customers. We are teaming up with Google and other members of the Kubernetes community with a goal of creating a strong open source community for AI and ML on Kubernetes and OpenShift -- Red Hat's enterprise Kubernetes platform.

Red Hat's Subin Modeel talks about how you can get started using OpenShift for your TensorFlow application development. The briefing is a definitive guide to getting Tensorflow applications and workflows deployed and trained on OpenShift. The principal purpose of the Machine Learning on OpenShift Special Interest Group is to discuss, develop, and disseminate best practices for deploying and managing Machine Learning workloads and applications on OpenShift built using (but not limited to) TensorFlow, Apache Spark, and other Open Source ML/AI frameworks. If you'd like to get notified of upcoming briefings and get invited to our slack channel, please join the OpenShift Commons here: https://commons.openshift.org#join Red Hatters, CNCF/Kubernetes project leads, and numerous other members of the OpenShift Commons will be gathering together in Austin for the upcoming OpenShift Commons Gathering co-located with Kubecon at the Austin Convention Center.