machine learning

How to use Machine Learning for Testing and Implementing Optical Networks

January 22, 2020

Optical transport networks are evolving to have unprecedented flexibility with advances such as finely tunable bitrate transponders, allowing dynamically-adaptive …

Inside the bad actor's studio

January 22, 2020

Using research from more than one billion users, 500 billion events, and 50 million malicious accounts collected from global online services, Julian Wong details some of the sophisticated attack techniques being used by modern day online criminals and demonstrates how these types of attacks can be detected and mitigated by leveraging artificial intelligence.

Security + design * data science: A bot story

January 21, 2020

The security industry continues to struggle with alert fatigue as the talent shortage grows. Security has yet to fully embrace the power of UX to help security workers do more with less. Bobby Filar and Rich Seymour explain how they developed a chatbot, combining machine learning within an intuitive UI to expedite data search and discovery and enhance detection and response to security threats.

Can we make developers care about operations?

January 20, 2020

Can we make developers care about operations? Jrgen Cito shares real-world experience of developers struggling with operations and details a journey to incorporate runtime performance aspects into the developer's daily workflow and reduce performance problems reaching production.

Boosting Linux Performance with GCC/GLIBC Latest Technologies

January 19, 2020

As the Linux community continues to redefine the boundaries of what is possible in a server-based Linux distribution running on new silicon, both power and performance play an increasingly important r …

FPGA-accelerated data analytics (sponsored by Intel)

January 19, 2020

Microsoft has widely deployed field-programmable gate arrays (FPGAs) for accelerating search, networking, and machine learningwith a little help from Intel's software expertise and its FPGA programmers. Mike Strickland explains how a single FPGA can deliver significant acceleration for multiple workloads.

Apache Spark and machine learning on microservices

January 14, 2020

Hadoop-based data platforms that power ETL jobs and machine learning pipelines are great examples of monolithic architectures that could be redesigned with microservices. Stepan Pushkarev walks you through building and deploying data processing, reporting services, training, and prediction pipelines as decoupled microservices connected with the rest of the enterprise architecture.

Enterprise Open Source Adoption: The Final Frontier

January 14, 2020

From machine learning to increasing demand for data intensive customer facing apps and bullet proof scalability, the demands on open source are more than ever before.As open source adoption in enterpr …

A hands-on data science crash course for modeling and predicting the behavior of (large) distributed systems

January 9, 2020

Data science is a hot topic. Bart De Vylder and Pieter Buteneers offer a practical introduction that goes beyond the hype, exploring data analysis, visualization, and machine learning techniques using Python for modeling the behavior of distributed systems. You'll leave with a solid starting point to implement data science techniques in your infrastructure or domain of interest.

FTFY: Research advances in automatic bug repair

January 7, 2020

Claire Le Goues shares recent advances in academic software engineering and programming languages research that aims to bring that dream to reality, using everything from metaheuristic search to program synthesis to machine learning and search over big databases of existing code to make it happen.

An open source architecture for the IoT

January 5, 2020

Eclipse IoT is an ecosystem of organizations that are working together to establish an IoT architecture based on open source technologies and standards. Dave Shuman and James Kirkland showcase an end-to-end architecture for the IoT based on open source standards, highlighting Eclipse Kura, an open source stack for gateways and the edge, and Eclipse Kapua, an open source IoT cloud platform.

Considerations for hardware-accelerated machine learning platforms

January 4, 2020

The advances we see in machine learning would be impossible without hardware improvements, but building a high-performance hardware platform is tricky. It involves hardware choices, an understanding of software frameworks and algorithms, and how they interact. Mike Pittaro shares the secrets of matching the right hardware and tools to the right algorithms for optimal performance.

How machine learning with open source tools helps everyone build better products

January 2, 2020

Michelle Casbon explores the machine learning and natural language processing that enables teams to build products that feel native to every user and explains how Qordoba is tackling the underserved domain of localization using open source tools, including Kubernetes, Docker, Scala, Apache Spark, Apache Cassandra, and Apache PredictionIO (incubating).

How the IoT and machine learning keep America truckin'

January 2, 2020

Data is powering the largest trucks on Americas interstates, the buses that take our children to school, and the military vehicles that help protect our country. Terry Kline and Mike Olson look at how machine learning and predictive analytics keep more than 300,000+ connected vehicles rolling.

Journey to consolidation

January 1, 2020

Twenty years ago, a company implored us to think different about personal computers. Today, Apple continues to live and breathe that legacy. Its evident in the machine learning and analytics architectures that power many of the company's most innovative applications. Cesar Delgado joins Mike Olson to discuss how Apple is using its big data stack and expertise to solve non-data problems.

Music, the window into your soul

December 31, 2019

Have you ever wondered why Spotify just seems to know what you want? As a data-first company, Spotify is investing heavily in its analytics and machine learning capabilities to understand and predict user needs. Christine Hung shares how Spotify uses data and algorithms to improve user experience and drive business impact.

Oh buoy! How data science improves shipping intelligence for hedge funds

December 31, 2019

Abraham Thomas demonstrates how maritime data can be used to predict physical commodity flows, in a case study that covers every stage of the data lifecycle, from raw data acquisition, data cleansing and structuring, and machine learning and probabilistic modeling to conversion to tractable format, packaging for final audience, and commercialization and distribution.

Putting data to work: How to optimize workforce staffing to improve organization profitability

December 31, 2019

New machine learning technologies allow companies to apply better staffing strategies by taking advantage of historical data. Francesca Lazzeri and Hong Lu share a workforce placement recommendation solution that recommends staff with the best professional profile for new projects.

PyTextRank: Graph algorithms for enhanced natural language processing

December 30, 2019

Paco Nathan demonstrates how to use PyTextRankan open source Python implementation of TextRank that builds atop spaCy, datasketch, NetworkX, and other popular libraries to prepare raw text for AI applications in media and learningto move beyond outdated techniques such as stemming, n-grams, or bag-of-words while performing advanced NLP on single-server solutions.

Spark camp: Apache Spark 2.0 for analytics and text mining with Spark ML

December 30, 2019

Brooke Wenig introduces you to Apache Spark 2.0 core concepts with a focus on Spark's machine learning library, using text mining on real-world data as the primary end-to-end use case.

The age of machine learning

December 29, 2019

Ben Lorica explores the age of machine learning.

The essentials for digital growth (sponsored by MapR)

December 29, 2019

Jack Norris shares lessons learned by leading companies leveraging data to transform customer experiences, operational results, and overall growth and details the infrastructure, development, and data management principles used by successful leaders to drive agility regardless of application volume or scale.

The future of data science and machine learning (sponsored by IBM)

December 28, 2019

A changing market landscape and open source innovations are having a dramatic impact on the consumability and ease of use of data science tools. Carlo Appugliese examines the impact these trends and changes will have on the future of data science and how machine learning is making data science available to all.

The journey to Einstein: Building a multitenancy AI platform that powers hundreds of thousands of businesses

December 28, 2019

Salesforce recently released Einstein, which brings AI into its core platform to power every business. The secret behind Einstein is an underlying platform that accelerates AI development at scale for both internal and external data scientists. Simon Chan shares his experience building this unified platform for a multitenancy, multibusiness cloud enterprise.

Unraveling data with Spark using deep learning and other algorithms from machine learning

December 28, 2019

Vartika Singh and Jeffrey Shmain walk you through various approaches using the machine learning algorithms available in Spark ML to understand and decipher meaningful patterns in real-world data. Vartika and Jeff also demonstrate how to leverage open source deep learning frameworks to run classification problems on image and text datasets leveraging Spark.

A practical guide to conducting an AI snake oil sniff test

December 27, 2019

As artificial intelligence (and specifically machine learning) firmly takes hold in industry, there has been a significant increase in the amount of AI snake oil being developed, pitched, and sold. Joshua Joseph shares a practical guide for detecting AI products of questionable value or benefit, whether intentional or not.

AI is the new electricity.

December 27, 2019

Much like the rise of electricity, which started about 100 years ago, AI will revolutionize every major industry. Andrew Ng explains how AI can transform your business, shares major technology trends and thoughts on where your biggest future opportunities may lie, and explores best practices for incorporating AI, machine learning, and deep learning into your organization.

Applying AI to healthcare's biggest opportunity: Clinical variation

December 27, 2019

Mercy and Intermountain, two of the largest and most innovative hospital systems in the United States, have recently applied AI to tackle clinical variation within their systems. Todd Steward and Lonny Northrup discuss the application of machine intelligence for optimizing care and provide valuable insights into practice variation for improving clinical pathways.