better business decision


5 Steps to Making Better Business Decisions with Machine Learning - Cloudera Blog

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Wine quality is judged by an external group of fancy wine people who will determine your wine's future. If you get an Excellent quality rating, it's good for business. It's like Yelp reviews, mostly out of your hands but can make or break your business. So there is clearly a business benefit in trying to produce a wine that will get a good rating. While that seems self-evident – make good wine and people will like it – there is enough subjectivity in wine preferences that it makes this hard to do.


DeCoded: Artificial Intelligence…Machines Humans Better Business Decisions

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Pairing the efficiency of machines with human intelligence can improve a wide range of issues for businesses and consumers alike. In this episode of Decoded, Melanie Nuce, SVP of GS1 US, discusses the realities of A.I., machine learning, and automation and the real-world opportunities they present to industry. Quality, structured data is critical to the ingestion, digestion, and dissemination of data by AI models for the benefit of human interpretation. We've been led to believe that robots are going to take over the world, and that artificial intelligence is actually achieving the same level as human intelligence. But I would suggest to you that's not entirely true.


What is the most important question for Data Science (and Digital Transformation) - KDnuggets

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Have you just completed a boot camp or graduated with your degree in data science, computer science, machine learning, etc., so you're armed and ready to sling some code and build one of these models? Consider for a moment a different perspective, that of someone far up your leadership chain, the corporate executive. You may feel that they don't understand what you do. Because for most of them, these lists of what is trending in AI/ML and data science make them feel beaten downplaying buzz word bingo on a constantly changing board. Just when they were ramping up on machine learning, suddenly everyone is referring to AI, and they can't sort out exactly how the two are related, let alone what to do about it.


What is the most important question for Data Science (and Digital Transformation) - KDnuggets

#artificialintelligence

Have you just completed a boot camp or graduated with your degree in data science, computer science, machine learning, etc., so you're armed and ready to sling some code and build one of these models? Consider for a moment a different perspective, that of someone far up your leadership chain, the corporate executive. You may feel that they don't understand what you do. Because for most of them, these lists of what is trending in AI/ML and data science make them feel beaten downplaying buzz word bingo on a constantly changing board. Just when they were ramping up on machine learning, suddenly everyone is referring to AI, and they can't sort out exactly how the two are related, let alone what to do about it.


How to Make Better Business Decisions With AI: James Taylor Lays the Groundwork

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Listen to my podcast with James Taylor, the CEO and Principal Consultant of Decision Management Solutions. James is a leading expert in how to use decision modeling, business rules, and analytic technology to build decision management systems, and in this podcast he details how successful companies are using AI to enhance their key decisions -- and leave their competitors in the dust.


Machine Learning Engineer vs. Data Scientist--Who Does What? - AI Trends

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The roles of machine learning engineer vs. data scientist are both relatively new and can seem to blur. However, if you parse things out and examine the semantics, the distinctions become clear. While a scientist needs to fully understand the, well, science behind their work, an engineer is tasked with building something. But before we go any further, let's address the difference between machine learning and data science. It starts with having a solid definition of artificial intelligence.


Career Comparison: Machine Learning Engineer vs. Data Scientist--Who Does What? - Springboard Blog

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There's some confusion surrounding the roles of machine learning engineer vs. data scientist, primarily because they are both relatively new. However, if you parse things out and examine the semantics, the distinctions become clear. While a scientist needs to fully understand the, well, science behind their work, an engineer is tasked with building something. But before we go any further, let's address the difference between machine learning and data science. It starts with having a solid definition of artificial intelligence.


How AI-Based Enterprise Applications Can Help You Make Better Business Decisions

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Over the years, artificial intelligence (AI) has moved towards becoming a core component of enterprise applications (EAs) and a key determinant of successful business strategies. With AI's recent interventions in the corporate ecosystem, enterprises are now able to accomplish far more in less time, create compelling and personalized customer experiences but most importantly, predict business outcomes to drive greater profitability. A recent survey shows AI technologies are being extensively integrated in large retail, supply chain, legal, financial and IT companies. According to a 2017 report from McKinsey Global Institute (MGI), tech giants such as Amazon, Apple, IBM, Google and Microsoft spent around $30 billion on AI-based technologies in 2016, with over 90 per cent of the budget allocated towards research and deployment. The study has also revealed three central factors responsible for driving the development of AI across today's industries.


How AI-Based Enterprise Applications Can Help You Make Better Business Decisions

#artificialintelligence

Over the years, artificial intelligence (AI) has moved towards becoming a core component of enterprise applications (EAs) and a key determinant of successful business strategies. With AI's recent interventions in the corporate ecosystem, enterprises are now able to accomplish far more in less time, create compelling and personalized customer experiences but most importantly, predict business outcomes to drive greater profitability. A recent survey shows AI technologies are being extensively integrated in large retail, supply chain, legal, financial and IT companies. According to a 2017 report from McKinsey Global Institute (MGI), tech giants such as Amazon, Apple, IBM, Google and Microsoft spent around $30 billion on AI-based technologies in 2016, with over 90 per cent of the budget allocated towards research and deployment. The study has also revealed three central factors responsible for driving the development of AI across today's industries.