Real-World lessons for Machine Learning in business

Oak tree

Machine learning seems to be getting all the interest and hype these days, and some are even saying that it’s going mainstream. There are even dedicated conferences and summits for ML just like the 2021 AWS Machine Learning Summit. For ML to go mainstream, in my perspective, there are still real-world lessons we’ll need to translate ML into production for businesses, and I was hoping to get some takeaways from this summit. I listed here some parts that made the most impact on me.

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AI-powered transcription startup Verbit raises $157M

Verbit AI

Verbit today announced the close of a $157 million series D round that the company says will bolster its product R&D and hiring efforts. CEO Tom Livne, who noted that the raise brings the company’s post-money valuation to more than $1 billion, said that the capital will also support the company’s geographic expansion as it prepares for an initial public offering.

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Battle of the Auto ML titans for People Analytics application


How powerful is it to look into the future and predict something that is yet to happen? In Data Science, Machine Learning enables this! Ingeniously, by learning from what has happened in the past to predict what might potentially happen in the future. As we can imagine, the applications of such a technique can be revolutionary in People Analytics, one classic use case being to predict employee resignations.

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Cirrascale Cloud Services broadens Deep Learning cloud offerings with world’s most powerful GPU for AI Supercomputing


Cirrascale Cloud Services, a premier cloud services provider of deep learning infrastructure solutions for autonomous vehicles, natural language processing, and computer vision workflows, announced its dedicated, multi-GPU deep learning cloud servers support the NVIDIA A100 80GB and A30 Tensor Core GPUs. With record-setting performance across every category on the latest release of MLPerf, these latest offerings provide enterprise customers with mainstream options for a broad range of AI inference, training, graphics, and traditional enterprise compute workloads.

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IBM releases AI model toolkit to help developers measure uncertainty

IBM AI toolkit

At its Digital Developer Conference today, IBM open-sourced Uncertainty Quantification 360 (UQ360), a new toolkit focused on enabling AI to understand and communicate its uncertainty. Following in the footsteps of IBM’s AI Fairness 360 and AI Explainability 360, the goal of UQ360 is to foster community practices across researchers, data scientists, developers, and others that might lead to better understanding and communication around the limitations of AI.

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Whatfix nabs $90M to help workers onboard and get the most out of their IT stacks


“Digital transformation” has been on the mind of many an organization in the last year: the pandemic and the shift it’s brought to how we work are speeding up investments in new apps, infrastructure and work practices to improve productivity regardless of where we sit all day. Now, it looks like we’re on to the next stage of that journey: actually figuring out how to adopt and run with all that new tech.

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Model validation and monitoring: New phases in the ML lifecycle

ML pipeline validation and monitoring

In the last decade, machine learning (ML) research and practice have gone a long way in establishing a common framework in designing systems and applications using machine learning models. Mostly using best practices from software engineering, the so called MLOps ecosystem also started to emerge recently. All is good and long awaited.

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Switzerland is on a mission to increase Diversity In AI

Swiss Alps - the Matterhorn

Without any doubt, Switzerland is a trusted hub for innovation and Artificial Intelligence (AI) in the world. Thanks to the simultaneous presence of world-leading academic research institutions and industry innovation labs, Switzerland regularly ranks among the leaders when it comes to AI. For the past ten years, it has held 1st place as the top innovation economy, according to the Global Innovation Index (WIPO).

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Bain and Crosspoint Capital acquire ExtraHop in $900M deal


Bain Capital Private Equity and Crosspoint Capital Partners today announced that they’ve entered into a definitive agreement to acquire ExtraHop, a network detection and response (NDR) provider headquartered in Seattle, Washington. As a part of the strategic transaction, which is valued at $900 million, ExtraHop CEO Arif Kareem and cofounders CTO Jesse Rothstein and CCO Raja Mukerji will continue in their respective roles and Rothstein and Mukerji will remain “significant” investors in the company.

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Packaging ML Models: Web Frameworks and MLOps

In this article, we’ll see the what, why and how, of the top packaging tools – web-based frameworks and MLOps – for Data Science and ML projects. Data scientists and machine learning engineers need specific tools for building, deploying and monitoring these projects end-to-end. We’ll go through several tools in detail, along with their key components and features. No need for an introduction – let’s go!

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Uber veteran launches her ‘AI Mindset’ Self-Driving startup with $83.5 million round

Waabi CEO Raquel Urtasan

Computer scientist Raquel Urtasan, an artificial intelligence expert who led a team of Toronto-based engineers for Uber’s self-driving vehicle program, is launching tech startup Waabi with an $83.5 million funding round and a new “AI mindset” approach to commercializing automated driving.

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Understanding the ubiquitous nature of Uncertainty through the Heisenberg Uncertainty Principle

The Heisenberg Uncertainty Principle is an idea from quantum physics that states you can never simultaneously know the exact position and exact momentum of an object. It proclaims that the more certain we become of an object’s position, the less certain we become of its momentum and vice versa.

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Can Deep Learning/ AI help automate Business Intelligence tasks? A tall order unfortunately!

Deep learning business automation

Have you ever wondered how some people are just plain brilliant with numbers? How they can spot data issues in a jiffy — or glean an insight that’s not apparent to you? In this new era of Artificial Intelligence, every Business Intelligence (BI) vendor, and for that matter most software vendors, claim to be using AI. In most cases this is great window dressing — but puns apart, given the complexity of BI insight discovery, can we automate this process with current AI technology?

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How AI analytics will disrupt Business Process Management and Automation

Process automation

Today, as Artificial Intelligence (AI) becomes more prevalent, businesses are considering whether making AI Chatbots a part of BPM tools will allow companies to optimize work and cut human errors out of processes while improving service to customers. Business process management (BPM) is in the throes of an extreme makeover and here are the areas where BPM will evolve.

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