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Interview: Terry Deem and David Liu at Intel

I recently caught up with Terry Deem, Product Marketing Manager for Data Science, Machine Learning and Intel® Distribution for Python, and David Liu, Software Technical Consultant Engineer for the Intel® Distribution for Python*, both from Intel, to discuss the Intel® Distribution for Python (IDP): targeted classes of developers, use with commonly used Python packages for data science, benchmark comparisons, the solution’s use in scientific computing, and a look to the future with respect to IPD.

Develop Multiplatform Computer Vision Solutions with Intel® Distribution of OpenVINO™ Toolkit

Realize your computer vision deployment needs on Intel® platforms—from smart cameras and video surveillance to robotics, transportation, and much more. The Intel® Distribution of OpenVINO™ Toolkit (includes the Intel® Deep Learning Deployment Toolkit) allows for the development of deep learning inference solutions for multiple platforms.

Field Report: KDD 2019

As a very long time member of the ACM and their SIGKDD group, I’d always wanted to attend a KDD conference (first one occurred in 1995). This year I received a gracious invitation to attend KDD2019 in Anchorage, Alaska, August 4-8. It satisfied two of my bucket list items: witnessing a KDD first-hand and also […]

The AI Opportunity

The tremendous growth in compute power and explosion of data is leading every industry to seek AI-based solutions. In this Tech.Decoded video, “The AI Opportunity – Episode 1: The Compute Power Difference,” Vice President of Intel Architecture and AI expert Wei Li shares his views on the opportunities and challenges in AI for software developers, how Intel is supporting their efforts, and where we’re heading next.

Fast-track Application Performance and Development with Intel® Performance Libraries

Intel continues its strident efforts to refine libraries optimized to yield the utmost performance from Intel® processors. The Intel® Performance Libraries provide a large collection of prebuilt and tested, performance-optimized functions to developers. By utilizing these libraries, developers can reduce the costs and time associated with software development and maintenance, and focus efforts on their own application code.

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 […]