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Big Data Exchange enters Indonesian data centre market with joint venture deal

ZDNet

Eileen Yu began covering the IT industry when Asynchronous Transfer Mode was still hip and e-commerce was the new buzzword. Currently an independent business technology journalist and content specialist based in Singapore, she has over 20 years of industry experience with various publications including ZDNet, IDG, and Singapore Press Holdings. Big Data Exchange (BDx) has marked its entry into Indonesia's data centre market through a joint venture agreement with PT Indosat and the latter's two subsidiaries. The move aims to tap increasing demand for cloud services and connectivity. Estimated to be worth $300 million, the deal would see BDx enter a conditional sale and purchase agreement of shares (CSPA) and establish a joint venture with PT Indosat, PT Aplikanusa Lintasarta, and PT Starone Mitra Telekomunikasi (SMT). Under the agreement, BDx, Indosat, and Lintasarta would set up data centre and cloud operations in the Asian market, BDx said in a statement Thursday.


You are Not Using the Right AI/ML API: Here's Why

#artificialintelligence

Eden AI simplifies the use and deployment of AI technologies by providing a unique API connected to the best AI engines. Companies are increasingly using Artificial Intelligence services, especially when it comes to automating internal processes or improving their customers' experience. The strong development of AI makes it a commodity. These functionalities can be used in several fields: health, human resources, tech, etc. The big players in the cloud market (Amazon Web Services, Microsoft Azure or Google Cloud) offer solutions that provide access to this type of service, but there are also smaller providers that are already competing with them: Mindee, Dataleon, Deepgram, AssemblyAI, Rev.AI, Speechmatics, Lettria, etc.


Best Predictive Analytics Tools and Software 2022

#artificialintelligence

Managing data has always been a challenge for businesses. With new sources and higher volumes of data coming in all the time, it's more important than ever to have the right tools in place. Predictive analytics tools and software are the best way to accomplish this task. Data scientists and business leaders must be able to organize data and clean it to get the process started. The next step is analyzing it and sharing the results with colleagues.


Google partners with Asus IoT to spread availability of Coral on-device AI hardware

#artificialintelligence

Coral is Google's platform for adding on-device AI and inferencing capabilities to hardware. To make it more widely available, especially for Internet of Things use cases, Google is partnering with Asus IoT. Asus IoT is a sub-brand of Asus, and Google wants to scale manufacturing, distribution, and support for Coral with this agreement. With decades of experience in electronics manufacturing at a global scale, ASUS IoT will provide Coral with the resources to meet our growth demands while we continue to develop new products for edge computing. This will see Asus IoT "become the primary channel for sales, distribution and support" for Coral, with customers getting "dedicated teams for sales and technical support" in the process.


Testing Out HPC On Google's TPU Matrix Engines

#artificialintelligence

In an ideal platform cloud, you would not know or care what the underlying hardware was and how it was composed to run your HPC – and now AI – applications. The underlying hardware in a cloud would have a mix of different kinds of compute and storage, an all-to-all network lashing it together, and whatever you needed could be composed on the fly. This is precisely the kind of compute cloud that Google wanted to build back in April 2008 with App Engine and, as it turns out, that very few organizations wanted to buy. Companies cared – and still do – about the underlying infrastructure, but at the same time, Google still believes in its heart of hearts in the platform cloud. And that is one reason why its Tensor Processing Unit, or TPU, compute engines are only available on the Google Cloud.


The Hyperscalers Point The Way To Integrated AI Stacks

#artificialintelligence

Enterprises know they want to do machine learning, but they also know they can't afford to think too long or too hard about it. They need to act, and they have specific business problems that they want to solve. And they know instinctively and anecdotally from the experience of the hyperscalers and the HPC centers of the world that machine learning techniques can be utterly transformative in augmenting existing applications, replacing hand-coded applications, or creating whole new classes of applications that were not possible before. They also have to decide if they want to run their AI workloads on-premise or on any one of a number of clouds where a lot of the software for creating models and training them are available as a service. And let's acknowledge that a lot of those models were created by the public cloud giants for internal workloads long before they were peddled as a service.


Machine Learning on Google Cloud (Vertex AI & AI Platform)

#artificialintelligence

Are you a data scientist or AI practitioner who wants to understand cloud platforms? Are you a data scientist or AI practitioner who has worked on Azure or AWS and curious to know how ML activities can be done on GCP? If yes, this course is for you. This course will help you to understand the concepts of the cloud. In the interest of the wider audience, this course is designed for both beginners and advanced AI practitioners.


The real cost of cloud computing - VentureBeat - UrIoTNews

#artificialintelligence

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 – 28. The public cloud is growing rapidly and the market for the technology is expected to reach $1.3 trillion by 2025. The cloud has revolutionized the computing industry and enabled many applications, business models and enterprises, which otherwise wouldn't have been possible. Immediate availability, scalability, minimal capital expenditure and streamlined developer experience are its main advantages -- but it comes at a cost. Due to a lack of in-house infrastructure optimization capabilities, most enterprises stick to the cloud even after achieving certain maturity. To keep cloud spending under control, enterprises have built or acquired tools and services.


Top 3 Digital Transformation Strategies of 2022

#artificialintelligence

The past two years of the pandemic have been marked by a period of rapid technological change. Amidst supply chain disruptions and changes in consumer behaviours, organisations have turned to digital transformation strategies to stay agile and resilient. The COVID-19 crisis has made it clear that technology is the lynchpin of organisational resilience and agility. As the pandemic disrupted global supply chains, forced employees to work from home and triggered a massive shift of consumer behaviour to online channels, digital technologies have played a pivotal role in keeping organisations afloat. According to Google's State of the API Economy 2021, digital transformation was the leading business imperative of 2020, based on a survey of 700 IT decision-makers from around the world.


Cloud computing has all the momentum, but we still live in an on-premises world for now

ZDNet

Joe McKendrick is an author and independent analyst who tracks the impact of information technology on management and markets. As an independent analyst, he has authored numerous research reports in partnership with Forbes Insights, IDC, and Unisphere Research, a division of Information Today, Inc. With all the analyst, press and conference talk about the ascendency of cloud, one can be forgiven for assuming that the entire world is now running on AWS, Azure or Google Cloud and other providers. However, at this stage, only seven percent of enterprises are truly all-cloud. This number is likely to more than double over the next two years, but still represents the minority of enterprises.