Slalom and AWS bring in Launch Centers to help enterprises accelerate business transformation, modernize IT services

Amazon Web Services Inc. (AWS), an Amazon.com company, and Slalom, announced a new multi-year, global, strategic collaboration relationship, to build joint AWS | Slalom Launch Centers (Launch Centers) that will accelerate customers’ migration to the cloud and help them modernize their IT services. 

As part of the relationship, three Launch Centers will initially open in Seattle, Chicago, and Atlanta with plans to open more centers around the world within in the next three years. The Launch Centers will help customers to accelerate their IT modernization and migration strategies to achieve fully optimized, cloud based operating models.

The Launch Centers uniquely combine AWS Professional Services, a global team of AWS experts who help customers reach their desired outcomes with the cloud, with Slalom business transformation, software engineering, and analytics capabilities to address these customer transformations. 

Mutual AWS and Slalom customers can temporarily relocate their IT teams to work from designated Launch Centers, to reap the benefits of working directly with experts in a secure, immersive, collaborative environment that enables a culture of innovation and learning.

The Launch Centers will provide a variety of offerings, including expediting customers’ initial digital launches to the AWS Cloud, and a customized approach to scale their competencies and processes for the future on AWS. 

The AWS and Slalom multi-disciplinary experts can deliver a comprehensive path to the cloud, taking customers through a series of steps, from defining their business strategies and building their cloud migration roadmaps, to optimizing their move to the cloud and implementing a modernized approach for ongoing operational excellence. They will also guide customers through business rationalization and organizational change management, as well as help them to overcome technical readiness concerns, by providing training on how to manage their infrastructure.

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.

New Cohesity Partner Program enables partners to monetize opportunities in expanding data management segment

Cohesity announced advancements in its channel partner program to meet the growing opportunities for partners and customers globally. Cohesity is investing in its partner program so it can keep pace with the rapid market acceptance of Cohesity data management solutions.

Building on last year’s initial program, the enhanced program focuses on simplicity and transparency, with generous bonuses for bringing in new customers, streamlined processes for incentives and market development fund (MDF) management, as well as the ability to clearly differentiate partner capabilities.

The enriched Cohesity partner program is divided into three tiers that provide clear direction to partners on how they can grow their business with Cohesity. The tiered program is also designed to make it easy for Cohesity customers to identify knowledgeable and experienced channel partners that have a proven record of success. 

The partner tiers include Premier Partner top tier for “premier partners” to work directly with Cohesity executives on joint business planning and custom growth programs. Premier Partners can provide feedback through several vehicles, including the Partner Advisory Council. To reach the premier partner tier, partners must meet a set of requirements and complete training accreditations.

The next tier consists of Preferred Partner. Channel partners that complete specific trainings are recognized as “preferred partners” with access to Cohesity partner teams, MDFs for marketing and enablement, along with instructor-led training courses and not-for-resale (NFR) software for customer demos and education.

The last tier is made up of Associate Partner: The starting “associate partner” tier provides access to the Cohesity Partner Portal where resellers across the globe can obtain pre-built marketing campaign kits, access sales tools, and gain access to web-based sales and enablement training.

Kinetica Active Analytics Platform and RAPIDS available on Oracle Cloud infrastructure to boost predictive data analytics performance

Kinetica announced Tuesday that the Kinetica Active Analytics Platform can now run predictive analytics on Oracle Cloud. The Kinetica Active Analytics Platform and Oracle Cloud Infrastructure bring historical data analytics, streaming data analytics, location intelligence, and artificial intelligence together in the cloud, simplifying the typical architecture for smart applications.

As new, highly complex data challenges arise as a result of the growth of the IoT, AI, and the smart device market, businesses across industries can address a variety of issues with the Kinetica Active Analytics Platform paired with RAPIDS, running on Oracle Cloud Infrastructure, from predicting portfolio risk to limiting relevant variables in drug trials, programming inventory management to optimizing telco network capacity. 

Kinetica and RAPIDS on Oracle Cloud Infrastructure deliver active analytics to industries including healthcare, energy, telecommunications, automotive, government, retail, and financial services, among others.

Kinetica and RAPIDS accelerate machine learning-driven predictive analysis by leveraging Oracle Cloud Infrastructure’s GPU instances for model training and inferencing, significantly reducing time to value. The Kinetica Active Analytics Platform now incorporates GPU-accelerated data science libraries using RAPIDS that are available on Oracle Cloud Infrastructure, a set of complementary cloud services to build and run a wide range of applications and services in a highly available hosted environment.

Data scientists can now streamline development and improve accuracy by GPU-accelerating their entire data pipeline with the Kinetica Active Analytics Platform and RAPIDS, incorporating machine learning into active analytical applications. 

Data scientists are able to explore data within Kinetica, launch an environment for model development and training, and automatically deploy the model into the application for production use. On Oracle Cloud Infrastructure, this enables end-to-end GPU-accelerated data science in the cloud, at enterprise scale. With Oracle and Kinetica, organizations can now combine data analysis, location intelligence, and machine learning into their active analytical applications.

“With data emerging as the undisputed most valuable corporate asset, it is essential to offer the full spectrum of corporate tools to deliver relevant, responsive data analysis at scale,” said Irina Farooq, Chief Product Officer, Kinetica. “We are pleased to combine the power of the Kinetica Active Analytics Platform with RAPIDS on Oracle Cloud to enable cloud-ready active analytics at enterprise scale.”

Google announces progress on effects of AI to the reporting process, beginning with news gathering and distribution

Google released Monday a report which highlights how AI offers new powers to journalists across the reporting process, from news gathering to distribution. It also underlines how news organizations that want to explore this potential must be ready to consider and carefully monitor the ethical and editorial implications of these new technologies.

This research is the result of Journalism AI, a year-long collaboration between Polis, the international journalism think tank at the London School of Economics and Political Science, and the Google News Initiative, to educate newsrooms about the potential offered by AI-powered technologies through research, training and networking.

The Journalism AI report is based on a survey of 71 news organisations in 32 different countries regarding artificial intelligence and associated technologies. A wide range of journalists working with AI answered questions about their understanding of AI, how it was used in their newsrooms, and their views on the wider potential and risks for the news industry.

What emerges from this research is that AI is a significant part of journalism already but it is unevenly distributed. AI is giving journalists more power, but with that comes editorial and ethical responsibilities.

The future impact of AI is uncertain but it has the potential for wide-ranging and profound influence on how journalism is made and consumed. AI can free up journalists to work on creating better journalism at a time when the news industry is fighting for economic sustainability and for public trust and relevance. It can also help the public cope with a world of news overload and misinformation and to connect them in a convenient way to credible content that is relevant, useful and stimulating for their lives.

Artificial intelligence is helping transform many businesses, and journalism is no exception. Newsrooms are already using AI to help organize and find videos and images, transcribe interviews in multiple languages and much more. But the industry  is still trying to understand the full impact AI can have.  

Newsrooms around the world are experimenting with AI, and responses to the Journalism AI survey came from 71 media organizations in 32 countries. Publishers, editors and reporters shared their detailed thoughts on the potential of AI for the news industry, how it is impacting their organizations and the risks and challenges involved with this new wave of technological innovation. 

The findings make it clear that journalism should pay attention to AI, which has the potential for wide-ranging and profound influence on how journalism is made and consumed. 

On one side, AI technologies promise to free up time for journalists to work on the more creative aspects of the news production, leaving tedious and repetitive tasks to machines. At a time when the news industry is fighting for economic sustainability and for the public’s trust, it’s easy to see why this promise is highly attractive.

On the other side, via personalization and smart recommendations, AI can help the public cope with news overload, connecting them in a convenient way to credible content that is relevant, useful, and stimulating for their lives.