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As they journey toward AI, most organizations establish data science teams staffed with people skilled in ML/DL algorithms, frameworks and techniques. Yet, many of those organizations struggle to make their AI projects truly relevant to the business, instead failing to get the projects into full production and integrated with existing applications and processes. It’s why so many line-of-business stakeholders consider only a small percentage of AI projects to be true successes.

We’re seeing clients across industries quickly recognizing that they need a systematic approach to “operationalizing” AI in order to drive AI success. That approach means managing the complete end-to-end…


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More and more cloud-computing experts are talking about “multicloud”. The term refers to an architecture that spans multiple cloud environments in order to take advantage of different services, different levels of performance, security, or redundancy, or even different cloud vendors. But what sometimes gets lost in these discussions is that multicloud is not always public cloud. In fact, it’s often a combination of private and public clouds.

As machine learning (ML) continues to pervade enterprise environments, we need to understand how to make ML practical on multicloud — including those architectures that span the firewall.

Let’s look at three possible…


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Photo: Science Museum London | Sir Ernest Rutherford’s laboratory, early 20th century | Creative Commons license

Machine Learning itself is not new. The concepts have been around for decades, and many companies have been building ML models and doing predictive analytics for a while. So what exactly is IBM doing in this space?

I would like to say “IBM takes an enterprise approach to ML”, but that sounds too vague. This post details IBM’s commitment to machine learning and how our approach goes beyond hype and hand waving to offer the platforms, tools, and processes our enterprise customers need.

I would like to focus on three things:

  • Democratizing machine learning
  • Operationalizing machine learning
  • Hybrid machine learning…


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The Machine Learning revolution is underway and is changing industries and delivering outcomes that were unimaginable a few years ago. In this video of John J. Thomas’s keynote at ApacheCon on May 17, 2017, learn how Apache Spark and other related projects are being used by innovative companies to remake products and services and enabling data-driven decision making.

For more information, visit the Data Science Experience.

Video courtesy of the The Linux Foundation (https://www.linuxfoundation.org/) via YouTube.

John Thomas

IBM Distinguished Engineer. #Analytics, #Cognitive, #Cloud, #MachineLearning, #DataScience. Chess, Food, Travel (60+ countries). Tweets are personal opinions.

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