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Alteryx Announces Acquisition of Trifacta

Alteryx, Inc. (NYSE: AYX), the Analytics Automation company, announced it has entered into a definitive agreement to acquire Trifacta, an award-winning cloud company that leverages scalable data management and machine learning to make data analytics faster and more intuitive.

The insideBIGDATA IMPACT 50 List for Q1 2022

The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with most of the firms making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, […]

Video Highlights: Expert.ai – Executive Interview

In this interview, expert.ai’s CEO Walt Mayo gives an update on the company’s recent FY20 results. He discusses the technology that is being developed as part of the company’s Path to Lead five-year strategy and outlines how the company expects to commercialise it. He discusses the wider natural language understanding/processing (NLU/NLP) market, highlighting recent M&A activity. Finally, he outlines the key milestones the company is targeting over the next 12 months.

68% of CTOs have Implemented Machine Learning at their Organization

Research from STX Next, Europe’s largest software development company specializing in the Python programming language, has found that 68% of chief technical officers (CTOs) have implemented machine learning at their company. This makes it overwhelmingly the most popular subset of AI, with others such as natural language processing (NLP), pattern recognition and deep learning also showing considerable growth.

The Secret Weapon Behind Quality AI: Effective Data Labeling

In this special guest feature, Carlos Melendez, COO, Wovenware, discusses best practices for “The Third Mile in AI Development” – the huge market subsector in data labeling companies, as they continue to come up with new ways to monetize this often-considered tedious aspect of AI development. The article addresses this trend and outlines how it is not really a commodity market, but can comprise different strategies for successful outcomes.

Surging to the Front of the Cloud Race: The S3 API and Object Storage

In this contributed article, editorial consultant Jelani Harper discusses object storage, the S3 protocol, and contemporary cloud developments. The competition for supremacy in the public cloud computing marketplace is one of the most eagerly watched contests throughout the data landscape. With AWS, Google, and Azure all vying with one another to offer the most advantageous services for what’s only a broadening user base, there’s no scarcity of innovations as they attempt to outmatch one another for customer patronage and loyalty.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – December 2021

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

The Missing Role your Organization Needs for the Success of your AI Initiatives

In this contributed article, Alankrita Priya, AI/ML Product Manager at Hypergiant, discusses how MLOps platforms can operationalize new technologies and fully bridge the gap between data scientists and end business users. There is a crucial role that AI PMs will play moving forward in both facilitating this deployment process and ensuring that companies practice responsible AI.

Deeplite Accelerates AI on Arm CPUs Using Ultra-Compact Quantization

Deeplite, a provider of AI optimization software designed to make AI model inference faster, more compact and energy-efficient, today announced Deeplite Runtime (DeepliteRT), a new addition to its platform that makes AI models even smaller and faster in production deployment, without compromising accuracy. Customers will benefit from lower power consumption, reduced costs and the ability to utilize existing Arm CPUs to run AI models.

The Challenges of Pruning AI Models on the Edge

In this special guest feature, Nick Romano, CEO, Deeplite, discusses how struggling to fit advanced models in edge devices with limited resources forces deep learning teams to start “pruning” models – essentially trimming parts of it that are deemed not critical, but that also comes with a price: significantly reduced model accuracy. For the power of AI to be unleashed at the edge with full accuracy and the ability to run on devices with limited resources, there’s a need for AI optimization.