Intel Research recognizes digital skills gap slowing Industry 4.0 in the manufacturing sector

Intel released Thursday results of a new study “Accelerate Industrial,” which represents comprehensive view of Industry 4.0, the digital transformation of the manufacturing sector. The research uncovered a serious skills gap that most Western industrial production training programs and government investment initiatives fail to address.

The study found that current leaders need to create tomorrow’s future-ready workforce. This requires the collaboration of universities, government and industry – including initiatives that focus on worker training for the transforming manufacturing sector.

“Accelerate Industrial” was conducted and authored by Dr. Faith McCreary, a principal engineer, experience architect and researcher at Intel, in tandem with Dr. Irene Petrick, senior director of Industrial Innovation for Intel’s Industrial Solutions Division. The study encompasses mobile ethnographies and interviews with over 400 manufacturers and the ecosystem technologists that support them. The work is being released as a series of reports.

A recent Deloitte/Manufacturing Institute study suggests that industries are entering a period of acute long-term labor shortages, with a shortfall in manufacturing expected to be 2.4 million job openings unfilled by 2028, resulting in a $2.5 trillion negative impact on the U.S. economy. Germany and Japan, two other developed economies, are expected to fare even worse in terms of this projected labor shortage.

With the increasing proliferation of data, connectivity and processing power at the edge, the industrial internet of things is becoming more accessible. However, successful adoption remains out of reach for many: two of three companies piloting digital manufacturing solutions fail to move into large-scale rollout.

The study uncovered the top five challenges cited by respondents that have the potential to derail investments in smart solutions in the future, with 36 percent citing “technical skill gaps” that prevent them from benefiting from their investment; 27 percent expect “data sensitivity” from increasing concerns over data and IP privacy, ownership and management; 23 percent found that they lack interoperability between protocols, components, products and systems; 22 percent citing security threats, both in terms of current and emerging vulnerabilities in the factory; and 18 percent reference handling data growth in amount and velocity, as well as sense-making.

“Accelerate Industrial” points to the rising importance of the digital skills required to navigate and succeed in this new landscape.

The research found that while there is a big appetite for digital transformation – 83 percent of companies plan to make investments in smart factory technologies – the most important skills and characteristics cited for that transformation are not ones that are typically emphasized by most industry job training programs or relevant policymakers.

Future skills cited by respondents point to the need to go beyond the basics of programming to embrace a deep understanding of digital tools, from data collection to analytics and real-time feedback directly to the operating environment. 

The top five future skills required to support digital transformation in manufacturing are “Deep understanding” of modern programming or software engineering techniques; “digital dexterity,” or the ability to leverage existing and emerging technologies for practical business outcomes; data science; connectivity, and cybersecurity.

Amazon SageMaker Operators for Kubernetes capability helps developers, data scientists to train, tune, deploy ML models

AWS released on Monday Amazon SageMaker Operators for Kubernetes capability that makes it easier for developers and data scientists using Kubernetes to train, tune, and deploy machine learning (ML) models in Amazon SageMaker. Customers can install these Amazon SageMaker Operators on their Kubernetes cluster to create Amazon SageMaker jobs natively using the Kubernetes API and command-line Kubernetes tools such as ‘kubectl’.

Many AWS customers use Kubernetes, an open-source general-purpose container orchestration system, to deploy and manage containerized applications, often via a managed service such as Amazon Elastic Kubernetes Service (EKS). This enables data scientists and developers, for example, to set up repeatable ML pipelines and maintain greater control over their training and inference workloads. 

Amazon SageMaker brings down deep learning inference costs by up to 75 percent using Amazon Elastic Inference to attach elastic GPU acceleration to Amazon SageMaker instances. For most models, a full GPU instance is over-sized for inference. Also, it can be difficult to optimize the GPU, CPU, and memory needs of your deep learning application with a single instance type. 

Elastic Inference allows users to choose the instance type that is best suited to the overall CPU and memory needs of your application, and then separately configure the right amount of GPU acceleration required for inference.

However, to support ML workloads these customers still need to write custom code to optimize the underlying ML infrastructure, ensure high availability and reliability, provide data science productivity tools, and comply with appropriate security and regulatory requirements. 

For example, when Kubernetes customers use GPUs for training and inference, they often need to change how Kubernetes schedules and scales GPU workloads in order to increase utilization, throughput, and availability. Similarly, for deploying trained models to production for inference, Kubernetes customers have to spend additional time in setting up and optimizing their auto-scaling clusters across multiple Availability Zones.

Amazon SageMaker Operators for Kubernetes bridges this gap, and customers are now spared all the heavy lifting of integrating their Amazon SageMaker and Kubernetes workflows. Starting Monday, customers using Kubernetes can make a simple call to Amazon SageMaker, a modular and fully-managed service that makes it easier to build, train, and deploy machine learning (ML) models at scale. 

With workflows in Amazon SageMaker, compute resources are pre-configured and optimized, only provisioned when requested, scaled as needed, and shut down automatically when jobs complete, offering near full utilization. 

Now with Amazon SageMaker Operators for Kubernetes, customers can continue to enjoy the portability and standardization benefits of Kubernetes and EKS, along with integrating the many additional benefits that come out-of-the-box with Amazon SageMaker, no custom code required.

Each Amazon SageMaker Operator for Kubernetes provides users with a native Kubernetes experience for creating and interacting with jobs, either with the Kubernetes API or with Kubernetes command-line utilities such as kubectl. Engineering teams can build automation, tooling, and custom interfaces for data scientists in Kubernetes by using these operators—all without building, maintaining, or optimizing ML infrastructure. 

Data scientists and developers familiar with Kubernetes can compose and interact with Amazon SageMaker training, tuning, and inference jobs natively, as users would with Kubernetes jobs executing locally. Logs from Amazon SageMaker jobs stream back to Kubernetes, allowing consumers to natively view logs for model training, tuning, and prediction jobs in command line.

stackArmor debuts stackArmor OpsAlert offering to reduce cloud costs by detecting and eliminating idle capacity

stackArmor has unveiled stackArmor OpsAlert cloud operations management solution for busy product managers, product owners and business managers with a stake in financially optimized cloud hosting. 

stackArmor OpsAlert provides actionable intelligence on cloud utilization and cost using simple business oriented metrics. These metrics allow business managers and product managers to detect idle capacity and improve utilization thereby increasing margins for cloud-based products.

Continuous monitoring of cloud operations and costs is a critical need for government program managers, who must stay compliant with Antideficiency Act requirements. It also comes with the capability to monitor cloud costs, detect cloud resource idling, and track cloud contract data in a single dashboard provides situational awareness and transparency.

stackArmor OpsAlert has been designed from the ground up to meet the needs of government agencies in monitoring cloud costs in compliance with FedRAMP, FISMA and DFARS requirements through its unique "ibn-boundary" deployment model. The "in-boundary" deployment model ensures that all operational and cost data remains within the system boundary, unlike SaaS solutions that install agents and are not FedRAMP-accredited.

stackArmor OpsAlert is geared for management oversight using easy to understand business metrics drive accountability. The stackArmor Cloud Idle Score provides an instant and easy to understand metric for detecting idle capacity that is being paid for but not utilized. 

It also aids in  accountability using simple categorization of cloud instances into highly idle, moderately idle or low idle allow managers to ask the right questions and drill-down to eliminate wastage, and increase margins. SaaS product hosted on AWS can improve their margins by improving operational efficiency of cloud assets by eliminating idle capacity.

The ability to interpret complex cost, operations and utilization data is essential for effective management. Using advanced data aggregation, integration and data science methods, stackArmor OpsAlert shows utilization, idle and cost data side by side to enable decision making.

stackArmor OpsAlert include actionable insights with proven algorithms to detect idle and waste to help product managers, SaaS CFO’s and business managers track cloud spend and bring it under control. It offers accountability across distributed teams with dashboards and emails to help agile product teams quickly see cloud spending and eliminate wastage using the Cloud Idle Score across Regions and AWS Accounts.

stackArmor OpsAlert is tailored for fast growing organizations that are unable to dedicate full-time resources for cloud cost and utilization management. Its fully managed billing and cloud operations support services are cost-effective tools for busy cloud product owners, product managers and program managers looking for force-multipliers.

Datameer releases Neebo cloud-native virtual analytics hub to discover, share, and collaborate on analytics and data science assets

Datameer introduced on Wednesday Neebo, its product that enables analytics and data science teams to find, combine, and publish trusted information assets in hybrid landscapes. 

Neebo's self-service platform enables analytics professionals and data scientists to initiate projects in minutes and promptly answer analytics questions or build new models, thereby enabling greater business agility. Neebo provides a unified access point for analysts, data scientists, and business stakeholders to more effectively leverage all their data science and analytics assets across the enterprise. 

Neebo works with information assets of any type such as data, documents, reports, code, dashboards, SaaS applications, and data science models, no matter where they reside: on-premises, in the cloud, in SaaS applications, or in web services.

Neebo uses virtualization and AI techniques to support a number of key capabilities. With Neebo, teams can connect to analytics and data science assets and use them no matter where they reside; find and explore these assets to help answer analytics questions; combine assets to create new ones that are customized to solve the problem at hand; publish assets that can be consumed by business intelligence and data science tools, and share and collaborate to re-use assets and build trust and knowledge across the enterprise.

By providing virtualized access to analytics assets, Neebo eliminates costly and error-prone movement, keeps assets securely in place, and ensures trusted, single source of truth for each asset. Neebo also includes security and governance capabilities that complement and integrate into existing frameworks.

Neebo embeds AI in many of its features including: assisting with discovery of assets so teams can find the optimal ones for their specific problems, providing data blend suggestions, and optimizing queries and caching. All this makes the work of analysts and data scientists easier and faster.

Neebo manages a wide variety of assets covering both traditional analytics and data science. This helps organizations unify all their analytics efforts and integrates data science initiatives into mainstream analytics processes and governance.

In September, Datameer announced general availability of Datameer X, a new release of its data preparation and exploration software built for data scientists and machine learning engineers. Now, organizations can speed up machine learning analytics cycles and create robust data flows that feed more data into machine learning models to increase their accuracy.