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Operationalizing Machine Learning

#artificialintelligence

Machine Learning (ML) powers an increasing number of the applications and services that we use daily. For organizations who are beginning to leverage datasets to generate business insights -- the next step after you've developed and trained your model is deploying the model to use in a production scenario. That could mean integration directly within an application or website, or it may mean making the model available as a service. As ML continues to mature the emphasis starts to shift from development towards deployment. You need to transition from developing models to real world production scenarios that are concerned with issues of inference performance, scaling, load balancing, training time, reproducibility and visibility.


Operationalizing Machine Learning - DZone AI

#artificialintelligence

Machine learning (ML) powers an increasing number of the applications and services that we use daily. For organizations who are beginning to leverage datasets to generate business insights, the next step after you've developed and trained your model is deploying the model to use in a production scenario. That could mean integration directly within an application or website, or it may mean making the model available as a service. As ML continues to mature, the emphasis starts to shift from development towards deployment, you need to transition from developing models to real-world production scenarios that are concerned with issues of inference performance, scaling, load balancing, training time, reproducibility, and visibility. In previous posts, we've explored the ability to save and load trained models with TensorFlow that allow them to be served for inference.


Amazon brings machine learning to "everyday developers" » Banking Technology

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"Amazon has a long history of machine learning" Amazon Web Services (AWS) is looking to bring machine learning (ML) to ordinary developers, launching the SageMaker service to simplify building applications, reports Enterprise Cloud News (Banking Technology's sister publication). ML is too complicated for ordinary developers, AWS CEO Andy Jassy said at a keynote during the AWS re:Invent event. "If you want to enable most enterprises and companies to be able to use ML in an expansive way, we have to solve the problem of making it accessible to everyday developers and scientists," he said. Amazon has a long history of ML, Jassy says. "We've been doing ML at Amazon for 20 years," he said.


Building a natural language processing library for Apache Spark

@machinelearnbot

Check out David Talby's tutorial "Natural language understanding at scale with spaCy and Spark NLP" at the Strata Data Conference in San Jose, March 5-8, 2018. Registration is now open--save 20% with the code BIGDATA20. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. When I first discovered and started using Apache Spark, a majority of the use cases I used it for involved unstructured text.


building-a-natural-language-processing-library-for-apache-spark

@machinelearnbot

Check out David Talby's tutorial "Natural language understanding at scale with spaCy and Spark NLP" at the Strata Data Conference in San Jose, March 5-8, 2018. Registration is now open--save 20% with the code BIGDATA20. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. When I first discovered and started using Apache Spark, a majority of the use cases I used it for involved unstructured text.


Future Factories: How AI enables smart manufacturing Data of Big Interest

#artificialintelligence

Today's consumers are pickier than ever. They want customized, personalized, and unique products over standardized ones and prefer local, smaller producers over large-scale global manufacturers. Factories, power plants, and manufacturing centers around the world must rely on automation, machine learning, computer vision, and other fields of AI to meet these rising demands and transform the way we make, move, and market things. Since the industrial revolution, factories have been optimized to mass produce a few products rapidly and cheaply to satisfy global demand. "The largest inefficiency that most manufacturers face is inflexibility," says Jim Lawton, Chief Product & Marketing Officer of Rethink Robotics, maker of collaborative industrial robots.


Actionable Insights: Obliterating BI, Data Warehousing as We Know It

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I spent much of my time recently in conferences talking to customers and analysts and realized they all were saying many of the same things about the challenges of productized, modern analytics solutions. Digital businesses, mobility, and IoT depend on real-time actionable insights and machine learning. At the same time traditional big data, data warehousing (DW) and business intelligence (BI) solutions have mostly worked on batch and interactive data queries. Streaming solutions and machine learning logic have been added on top of legacy architecture (and are not well integrated), leading to complexity and sub-optimal performance. The time is ripe for re-architecting analytics to maximize the value of machine learning and real-time streaming, drive actionable insights, and enable continuous operations.


Kinect, Xbox and Windows 10: Why accessibility matters

ZDNet

Kinect is either Microsoft's biggest success or biggest failure, depending on how you look at it. Kinect brought voice control to the living room long before Alexa or Google Home. It's also just been cancelled -- at least as a separate product. The problem is perhaps that for gamers, and maybe developers, Kinect games never felt as much like the Star Trek Holodeck as we thought it was going to (not least because living rooms aren't that big outside a few places in the US) and somehow there was never quite the momentum behind it. So what does this mean for other novel ways of interacting with our devices?


How AI Careers Fit into the Data Landscape – Insight Data

#artificialintelligence

The goal of newly-formed AI teams is to build intelligent systems, focused on quite specific tasks, that can be integrated into the scalable data transformations of Data Engineering work and the data products and business decisions of Data Science work. The differences between Artificial Intelligence, Data Science, and Data Engineering can vary considerably among companies and teams. Artificial Intelligence, or AI, focuses on understanding core human abilities such as vision, speech, language, decision making, and other complex tasks, and designing machines and software to emulate these processes. These models typically require very large datasets, so while efficient manipulation and use of large amounts of data is a fundamental aspect of Data Engineering work, it is crucial for state-of-the-art AI systems.


How the Internet of Things will reshape future production systems

#artificialintelligence

The advent of IoT technologies--and the more general move to digital tools that support operations, communication, analysis, and decision making in every part of the modern organization--won't change the fundamental purpose of production systems. With the introduction of comprehensive, real-time data collection and analysis, production systems can become dramatically more responsive. Highly integrated, digitally enabled production systems won't just work differently from today's--they'll be built differently, too. Automated optimization systems will adjust manufacturing sequences and speeds to help balance lines and match production more closely to customer demand.