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What Happened to Hadoop? And Where Do We Go from Here?

Apache Hadoop emerged on the IT scene in 2006 with the promise to provide organizations with the capability to store an unprecedented volume of data using cheap, commodity hardware. Hadoop facilitated data lakes were accompanied by a number of independent open source compute engines – and on top of that, “open source” meant free! What could go wrong?

Supercharge Data Science Applications with the Intel® Distribution for Python

Intel® Distribution for Python is a distribution of commonly used packages for computation and data intensive domains, such as scientific and engineering computing, big data, and data science. With Intel® Distribution for Python you can supercharge Python applications and speed up core computational packages with this performance-oriented distribution. Professionals who can gain advantage with this product include: machine learning developers, data scientists, numerical and scientific computing developers, and HPC developers.

DarwinAI Generative Synthesis Platform and Intel Optimizations for TensorFlow Accelerate Neural Networks

DarwinAI, a Waterloo, Canada startup creating next-generation technologies for Artificial Intelligence development, announced that the company’s Generative Synthesis platform – when used with Intel technology and optimizations – generated neural networks with a 16.3X improvement in image classification inference performance. Intel shared the optimization results in a recently published solution brief.

Addressing Governmental Challenges when Engaging AI, ML and Data Analytics

Gartner recently stated that all industries and levels of government agree the top three game-changing technologies today are AI/machine learning, data analytics/predictive analytics and cloud technologies. However, there are some primary sticking points when it comes to innovation in these areas. Government organizations continue to encounter challenges when trying to pursue these initiatives due to complex security and compliance requirements, poor scalability of legacy IT infrastructure, and perceived risks associated with cloud and IT modernization efforts. How can these challenges be addressed?

The Future of Open Source Big Data Platforms

Three well-funded startups – Cloudera Inc., Hortonworks Inc., and MapR Technologies Inc. — emerged a decade ago to commercialize products and services in the open-source ecosystem around Hadoop, a popular software framework for processing huge amounts of data. The hype peaked in early 2014 when Cloudera raised a massive $900 million funding round, valuing it […]

Interview: Atif Kureishy, Global VP, Emerging Practices at Teradata

I recently caught up with Atif Kureishy, Global VP of Emerging Practices at Teradata, during the 2019 edition of the NVIDIA GPU Technology Conference, to get a deep dive update for how Teradata is advancing into the fields of AI and deep learning. He also speaks about the ways Teradata and NVIDIA are accelerating time to value for enterprise AI environments and gathering financial services insights from GPUs.

Interview: Steven McMullen, Senior Business Intelligence Manager at Stack Overflow

I recently caught up with Steven McMullen, Senior Business Intelligence Manager at Stack Overflow, to discuss how his company tapped pre-IPO data analytics unicorn, Looker to modernize their marketing ops, acquire new leads and new customers. The business results were impressive, transforming Stack Overflow’s marketing strategy for the better.

Interview: Tom Wilde, CEO of Indico

I recently caught up with Tom Wilde, CEO of Indico, to survey his perspectives for how AI is revolutionizing the modern enterprise including AI use cases, successful and failed AI initiatives, unstructured vs. structured content, democratizing AI, and citizen data scientists.

Field Report: GPU Technology Conference 2019 #GTC19

I eagerly attended my 3rd GPU Technology Conference (GTC): “Deep Learning & AI Conference,” in Silicon Valley, March 23-26 as a guest of host NVIDIA. GTC has become my favorite tech event of the year due to its highly focused topic areas that align well with my own; data science, machine learning, AI, and deep learning; plus the show has an academic feel that I appreciate.

The Power of Crunching Big Data Effectively

In this contributed article, Lex Boost, CEO of Leaseweb USA, points out that according to an Accenture study, 79% of enterprise executives agree that companies not embracing big data will lose their competitive edge. Considering that data creation is on track to grow 10-fold by 2025, it’s crucial for companies to be able to process it more quickly, and meaningfully.