Collaborating Authors


Supercharging Visualization with Apache Arrow


Imagine a future where Minority Report-style data visualizations run in every web browser. This is a big step forward for critical workflows like investigating security and fraud incidents, and making critical insights for the next level of BI. Today's options are dominated by rigid Windows desktop tools and slow web apps with clunky dashboards. The Apache Arrow ecosystem, including the first open source layers for improving JavaScript performance, is changing that. Frustrated with legacy big data vendors for whom "interactive data visualization" does not mean "sub-second", Dremio, Graphistry, and other leaders in the data world have been gutting the cruft from today's web stacks.

Intro to text classification with Keras: automatically tagging Stack Overflow posts Google Cloud Big Data and Machine Learning Blog Google Cloud Platform


Posted by Sara Robinson (Developer Advocate), Josh Gordon (Developer Advocate), and Marianne Linhares Monteiro (DA Intern). As humans, our brains can easily read a piece of text and extract the topic, tone, and sentiment. Up until just a few years ago, teaching a computer to do the same thing required extensive machine learning expertise and access to powerful computing resources. Now, frameworks like TensorFlow are helping to simplify the process of building machine learning models, and making it more accessible to developers with no background in ML. In this post, we'll show you how to build a simple model to predict the tag of a Stack Overflow question.

Career Opportunities: Full Stack Developer for Machine Learning Incubation Team, SAP Singapore (149956)


As market leader in enterprise application software, SAP helps companies of all sizes and industries innovate through simplification. From the back office to the boardroom, warehouse to storefront, on premise to cloud, desktop to mobile device – SAP empowers people and organizations to work together more efficiently and use business insight more effectively to stay ahead of the competition. SAP applications and services enable customers to operate profitably, adapt continuously, and grow sustainably. The SAP Innovation Center Network is a strategic innovation entity within SAP, combining software engineering excellence with thought leadership and entrepreneurial spirit. To ensure a successful go-to-market, we follow an end-to-end responsibility approach.

5 Reasons Why Your Data Science Team Needs The DGX Station


However, for our current projects we need a compute server that we have exclusive access to." Access to a deep learning workstation will increase the speed of innovation and improve security." RESEARCHER "I felt I won the software stack lottery as NVIDIA- docker was already installed. I immediately pulled a container and started work on a CNTK NCCL project, the next day pulled another container to work on a TF biomedical project. I haven't looked back at how to reimage because felt too productive."

Operationalizing Data Science Models on the Pivotal Stack


At Pivotal Data Science, our primary charter is to help our customers derive value from their data assets, be it in the reduction of cost or by increasing revenue by offering better products and services. While we are not working on customer engagements, we engage in R&D using our wide array of products. For instance, we may contribute a new module to PDLTools or MADlib - our distributed in-database machine learning libraries, we might build end-to-end demos such as these or experiment with new technology and blog about them here. Last quarter, we set out to explore data science microservices for operationalizing our models for real-time scoring. Microservices have been the most talked about topic in many Cloud conferences of late. They've gained a large fan following by application developers, solution architects, data scientists and engineers alike.

Just Buying Into Modern BI and Analytics? Get Ready for Augmented Analytics, the Next Wave of Market Disruption - Rita Sallam


Machine learning automation is affecting all of enterprise software, but will completely transform how we build, analyze, and consume data and analytics. Tableau, Qlik, Tibco Spotfire) have disrupted the traditional BI market (e.g. Yet, as transformative as these tools have been, analytics is once again at a critical inflection point. Across the analytics stack, tools have become easier to use and more agile, enabling greater access and self-service. And yet organizations' processes for preparing data for analysis, analyzing data, building advanced analytics models, interpreting results and telling stories with data remain largely manual and prone to bias.

The next generational shift in enterprise infrastructure has arrived


The big data stack is crumbling as Hadoop gets replaced by new tools natively integrated with cloud native workload schedulers. As old systems shed, new ones powering the Systems Of Intelligence revolution are emerging. The race is on for stream processing as new stream native solutions like Kafka Streams and Twitter Herron go up against multi-purpose batch systems with stream bolt-ons like Spark. Open source has been all the rage in machine learning, but deployment and model/data collaboration are still a major challenge that commercial vendors like Algorithmia, Dataiku, and Pachyderm are working to solve with a cloud native and serverless bent.

When Milliseconds Count – Using AI to Buy Advertising


Summary: What changes in your analytics and Big Data stack would you have to make if you only had 10 milliseconds to make a decision? There's an entire industry that has to live by that rule. This is a great story about the collision of ecommerce, digital advertising, predictive analytics, and AI in a new digital battleground, the automation and optimization of advertising targeting and spend. Like so many interesting opportunities this new market starts with a pain point and an unmet need. In this case the lag in digital advertising spend, particularly in mobile devices. Combines Open Source, Cloud, Big Data and Machine Learning for DevOps and SRE


Those that survive and thrive either capitalize on a new technology or provide a timely response to a new market development. does both, combining the power of at least 4 technology trends--open source, cloud computing, big data analytics, and machine learning--while addressing a new group of influencers in IT purchasing--DevOps staff and Site Reliability Engineers (SREs). Logz tells its customers what's going on with their software applications. It offers an enhanced version of the open source ELK stack which combines an enterprise search engine with log analytics and visualization tools. On top of ELK, it has developed Cognitive Insights, an artificial intelligence platform that detects overlooked and critical events and provides the user with actionable data about context, severity, relevance, and recommended next steps.

CenturyLinkVoice: 2017 Predictions: 3 Technologies That Will Put Your New Year On The Right Track

Forbes - Tech

If you've been following any of the latest business and technology headlines, you've no doubt heard how 2017 will be the year that ground-breaking technologies like virtual reality and artificial intelligence will create new paradigms in our healthcare systems, schools, universities, and enterprises. Even small and mid-sized businesses (SMB) stand to gain immeasurable benefits from these technological advancements, helping managers to understand, manage and measure businesses operations and customer interactions in ways that were never possible before. We'll have the opportunity to become more efficient, innovate faster and ultimately, make better decisions as long as we have the network foundation required to sustain this innovation. Here's a look at three types of technology that can help you establish the groundwork required to get ahead in 2017: Fiber Optics: Fiber is the foundation of any business technology stack with its inherent speed and scalability. With fiber, you're driving on a supersonic raceway; without it, your business will operate like the digital equivalent of driving on a dirt road.