Charlie Burgoyne is the founder and CEO of Valkyrie. For a moment, let's set aside the abject tragedy of the Covid-19 pandemic and the demoralizing conditions through which the world continues to persevere. Instead, let's examine the state of affairs from a dispassionate and scientific position. Seismic changes in behavior are erupting as the burden of the pandemic forces transformation. Crippling inefficiencies in industry and volatile projections of markets have led to unprecedented uncertainty.

Understand the industrialization of machine learning (ML) and take the first steps toward identifying and generating the transformational disruptors of artificial intelligence (AI). You will learn to apply ML to data lakes in various industries, supplying data professionals with the advanced skills required to handle the future of data engineering and data science. Data lakes currently generated by worldwide industrialized business activities are projected to reach 35 zettabytes (ZB) as the Fourth Industrial Revolution produces an exponential increase of volume, velocity, variety, variability, veracity, visualization, and value. Industrialization of ML evolves from AI and studying pattern recognition against the increasingly unstructured resource stored in data lakes. Industrial Machine Learning supplies advanced, yet practical examples in different industries, including finance, public safety, health care, transportation, manufactory, supply chain, 3D printing, education, research, and data science.

The concept of machine learning is becoming better understood as we increasingly interact with it every day. From Netflix and Amazon recommendations, to Siri and Cortana voice recognition, to Google Maps travel time calculations, we're all becoming more familiar with machine learning technologies--even if we don't quite realize it yet. Applying machine learning in industry, however, is a different story. Though several companies are doing it, it's not nearly as ubiquitous as the consumer-oriented applications mentioned above. And that's what made a presentation by Kathy Applebaum and Kevin McClusky of Inductive Automation during the Ignition Community Conference 2018 so interesting.

The concept of machine learning is becoming better understood as we increasingly interact with it every day. From Netflix and Amazon recommendations, to Siri and Cortana voice recognition to Google Maps travel time calculations, we're all becoming more familiar with machine leaning technologies--even if we don't quite realize it yet. Applying machine learning in industry, however, is a different story. Though several companies are doing it, it's not nearly as ubiquitous as the consumer-oriented applications mentioned above. And that's what made a presentation by Kathy Applebaum and Kevin McClusky of Inductive Automation during the Ignition Community Conference 2018 so interesting.

While many organizations wrestle with this dilemma, the challenge is especially felt in healthcare. The industry has intense volumes and varieties of data coming from multiple sources, including electronic health records, digital scans, genomic data, wearables and smartphone apps. The goal is to find a way to consistently produce data-driven insights at enterprise scale. This can be done with industrial machine learning (IML), which provides a scalable solution for ingesting data, building algorithms, deploying them into production, and generating continuous insights to ongoing business problems. In healthcare, IML makes possible the kind of personalized care that organizations are hoping to achieve, one that goes beyond predictive analytics to add context to large varieties of data and distill them into something actionable.

Machine learning looks for changes in data patterns. This level of knowledge and insight into industrial operations enables facilities managers to gain greater control over the environment with a greater ability to predict when equipment failure may occur, allowing them to initiate pre-emptive repair and maintenance measures prior to failure. There is much knowledge and insight to be gained from machine learning systems and great benefit realized from its use. In this paper we present some steps to take when considering incorporating industrial machine learning as part of your information management ecosystem. Download this FREE tipsheet to learn how to get started.

It's that most wonderful time of the year, a time when our CTO, Dan Hushon, makes his technology predictions for the year ahead. For me, those predictions set the tone for the year to come and help me focus my attention on the trends that really matter. This year, the topic that has me most inspired is the advent of big, fast data – data with high volume and velocity. Big, fast data will improve machine intelligence by being a better source of training for machine-learning algorithms. This new machine intelligence will give rise to an unprecedented growth in innovation and enterprise productivity.

Most large, data-rich companies fail to benefit from information on an enterprise-wide scale because they are not equipped to effectively capture and analyze available data sources. A recent survey from PwC and Iron Mountain found that 57 percent of businesses are unable to extract significant value from their information, and 23 percent are unable to derive any real value at all. Regardless of size, geography or sector, businesses are struggling to fully realize value from their information. To address the trend, CSC has announced the expansion of its strategic alliance with Microsoft through the advancement of Industrial Machine Learning solutions that enable data scientists to produce new data-derived enterprise insights, leading to better and more timely business decisions. "CSC has been working with Microsoft on industry-specific approaches to the problem of creating data science that works," said Dan Hushon, chief technology officer, CSC.

TYSONS, Va.--(BUSINESS WIRE)--CSC (NYSE: CSC) today announced the expansion of its strategic alliance with Microsoft through the advancement of Industrial Machine Learning (IML) solutions that enable data scientists to produce new data-derived enterprise insights, leading to better and more timely business decisions. Most large, data-rich companies fail to benefit from information on an enterprise-wide scale because they are not equipped to effectively capture and analyze available data sources. A recent survey from PwC and Iron Mountain found that 57 percent of businesses are unable to extract significant value from their information, and 23 percent are unable to derive any real value at all. Regardless of size, geography or sector, businesses are struggling to fully realize value from their information. "CSC has been working with Microsoft on industry-specific approaches to the problem of creating data science that works," said Dan Hushon, chief technology officer, CSC.

Intelligent machines are becoming a ubiquitous and essential part of business operations. Algorithms trained by faster, smarter data now play a key role in determining customer demand, increasing customer trust and delivering unprecedented productivity and intelligence to the enterprise. But despite the success of some early adopters, most machine learning projects fail. Regardless of their size, location or industry, businesses have been struggling to fully unlock and realize the value of their information. In fact, a recent Information Value Index from PwC and Iron Mountain found that 57 percent of businesses are unable to extract significant value from their data; 23 percent are unable to derive any real value at all.