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IBM Ramps Up AI, Analytics Via New File, Object Storage

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IBM Thursday introduced new storage hardware and software aimed at placing its storage at the center of large-scale data requirements for artificial intelligence and analytics workloads. The new offerings are aimed at helping to build the kind of information architecture needed to get the most out of businesses' fast-changing data, said Eric Herzog, IBM's chief marketing officer and vice president of worldwide storage channels. "The new stuff is all about storage solutions for AI, big data and business analytics," Herzog told CRN. "IBM thinks customers need an information architecture to build AI before they can collect and analyze their data and feed it into their AI systems." IBM storage technology has always been an important part of customers' high-performance computing, artificial intelligence and machine-learning infrastructures, said John Zawistowski, global systems solutions executive at Sycomp, a Foster City, Calif.-based solution provider and IBM channel partner. "Why IBM? It's the way they integrated the AI software platform and storage," Zawistowski told CRN. "And the way IBM understands the importance of doing that. And the way IBM technology performs."


IBM Research releases differential privacy library that works with machine learning

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Differential privacy has become an integral way for data scientists to learn from the majority of their data while simultaneously ensuring that those results do not allow any individual's data to be distinguished or re-identified. To help more researchers with their work, IBM released the open-source Differential Privacy Library. The library "boasts a suite of tools for machine learning and data analytics tasks, all with built-in privacy guarantees," according to Naoise Holohan, a research staff member on IBM Research Europe's privacy and security team. "Our library is unique to others in giving scientists and developers access to lightweight, user-friendly tools for data analytics and machine learning in a familiar environment–in fact, most tasks can be run with only a single line of code," Holohan wrote in a blog post on Friday. "What also sets our library apart is our machine learning functionality enables organizations to publish and share their data with rigorous guarantees on user privacy like never before."


Global Big Data Conference

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As real-world AI deployments increase, IBM says the contributions can help ensure they're fair, secure and trustworthy. IBM on Monday announced it's donating a series of open-source toolkits designed to help build trusted AI to a Linux Foundation project, the LF AI Foundation. As real-world AI deployments increase, IBM says the contributions can help ensure they're fair, secure and trustworthy. "Donation of these projects to LFAI will further the mission of creating responsible AI-powered technologies and enable the larger community to come forward and co-create these tools under the governance of Linux Foundation," IBM said in a blog post, penned by Todd Moore, Sriram Raghavan and Aleksandra Mojsilovic. Specifically, IBM is contributing the AI Fairness 360 Toolkit, the Adversarial Robustness 360 Toolbox and the AI Explainability 360 Toolkit.


What is Data Science? A Complete Data Science Tutorial for Beginners - DataFlair

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Data Science has become one of the most demanded jobs of the 21st century. It has become a buzzword that almost everyone talks about these days. But what is Data Science? In this article, we will demystify Data Science, the role of a Data Scientist and have a look at the tools required to master Data Science. So, let's start Data Science Tutorial. "Data Science is about extraction, preparation, analysis, visualization, and maintenance of information.


Hitachi's Microsoft Agreement: A Game-Changer in Cloud-based Logistics

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Today, global innovation company Hitachi has announced its next-generation digital transformation solutions will run on Microsoft. The two companies have signed a strategic agreement to advance AI, Robotics, and IoT capabilities across logistics and manufacturing industries based in South Asia and Japan. The digital solutions would also be made available to the North American market. Each industry is unique in the way it adopts digital tools to transform its core operations. Logistics, manufacturing and supply industries are the most potent markets for digitalization.


Kantar Brand Growth Lab is developing Quantum Machine Learning solutions in Singapore

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With the continuous support and partnership of Singapore's Economic Development Board (EDB), Kantar established its Brand Growth Lab in Singapore in 2018 to develop AI/ML solutions. The Lab, an advanced analytics hub, is dedicated to discovering new ways to leverage big data to drive strategic decision-making for business. On January 2nd of this year, Kantar was granted its first patent by the Intellectual Property Office of Singapore for a method of optimising AI/ML predictions from a classical data feed with a hybrid simulator generated from classical and quantum model structures. Some of the other organizations with a patented invention in the Quantum technology field in Singapore are Oxford University Innovation, D-Wave, IBM and Google. "Quantum technology will revolutionize Artificial Intelligence and Machine Learning. This patent indicates our commitment to lead in this field. We are proud to have been awarded this patent as it demonstrates our advancement in the field of data science," commented Hernan Sanchez, Managing Director, Kantar Brand Growth Lab.


A closer look at SageMaker Studio, AWS' machine learning IDE

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Back in December, when AWS launched its new machine learning IDE, SageMaker Studio, we wrote up a "hot-off-the-presses" review. At the time, we felt the platform fell short, but we promised to publish an update after working with AWS to get more familiar with the new capabilities. When Amazon launched SageMaker Studio, they made clear the pain points they were aiming to solve: "The machine learning development workflow is still very iterative, and is challenging for developers to manage due to the relative immaturity of ML tooling." The machine learning workflow -- from data ingestion, feature engineering, and model selection to debugging, deployment, monitoring, and maintenance, along with all the steps in between -- can be like trying to tame a wild animal. To solve this challenge, big tech companies have built their own machine learning and big data platforms for their data scientists to use: Uber has Michelangelo, Facebook (and likely Instagram and WhatsApp) has FBLearner flow, Google has TFX, and Netflix has both Metaflow and Polynote (the latter has been open sourced).


A closer look at SageMaker Studio, AWS' machine learning IDE – IAM Network

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Back in December, when AWS launched its new machine learning IDE, SageMaker Studio, we wrote up a "hot-off-the-presses" review. At the time, we felt the platform fell short, but we promised to publish an update after working with AWS to get more familiar with the new capabilities. Pain points and solutions in the machine learning pipeline When Amazon launched SageMaker Studio, they made clear the pain points they were aiming to solve: "The machine learning development workflow is still very iterative, and is challenging for developers to manage due to the relative immaturity of ML tooling." The machine learning workflow -- from data ingestion, feature engineering, and model selection to debugging, deployment, monitoring, and maintenance, along with all the steps in between -- can be like trying to tame a wild animal. To solve this challenge, big tech companies have built their own machine learning and big data platforms for their data scientists to use: Uber has Michelangelo, Facebook (and likely Instagram and WhatsApp) has FBLearner flow, Google has TFX, and Netflix has both Metaflow and Polynote (the latter has been open sourced).


22 Widely Used Data Science and Machine Learning Tools

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What are the best tools for performing data science tasks? And which tool should you pick up as a newcomer in data science? I'm sure you've asked (or searched for) these questions at some point in your own data science journey. There is no shortage of data science tools in the industry. Picking one for your journey and career can be a tricky decision.


Where AI Meets Big Data, Innovation is Assured

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When you work around the idea of the fourth industrial revolution, two technologies that make a quick flash into your brain are – Big Data and Artificial Intelligence. A lot has been said and done in this regard. The combination of both has driven numerous industries towards success. While data or big data is considered the lifeblood of modern businesses, AI on the other hand is the heart to fill life into it. Speaking more technically, we can say, big data is at the core of every business and the technology to harness its true value, AI is extremely essential to extract true meaning from data.