I recently worked on a project, where I needed to train a Machine Learning model that would run on the Edge -- meaning, the processing and prediction occur on the device that collects the data. As usual, I did my Machine Learning part in Python and I haven't thought much about how we're going to port my ML stuff to the edge device, which was written in Java. When the modeling part was nearing the end, I started researching how to load a LightGBM model in Java. Prior to this, I had a discussion with a colleague who recommended that I retrain the model with the XGBoost model, which can be loaded in Java with XGBoost4J dependency. LightGBM and XGBoost are both gradient boosting libraries with a few differences.

The normal life cycle of a machine learning model includes several stages, see Figure 1. There are countless online courses and articles about preparing the data and building models but there is much less material about model deployment. Yet, it is precisely at this stage where all the hard work of data preparation and model building starts to pay off. This is where models are used to score (or get predictions for) new cases and extract the benefits. My intent here is to fill this gap, so that you will be fully prepared to deploy your model using time tested resources.

In this article, we will discuss how to go from the research phase to the production phase for ML projects and what are the different options to do so. If you try to have your training and server code in the same repository you would probably end up with a big mess that is hard to maintain. Training models and serving real-time prediction are extremely different tasks and hence should be handled by separate components. Last but not least, there is a proverb that says "Don't s**t where you eat", so there's that too. Thus, a better approach would be to separate the training from the server.

How MLeap allowed us to scale our existing predictive platform from our local machines to Apache Spark in the cloud with zero loss of functionality and sub-second response times. At Red Ventures, we partner with the nation's top brands to seamlessly connect customers with the products and services they need most using our advanced digital marketing and sales capabilities. Along a customer's journey, each interaction with Red Ventures presents an opportunity to make an influential decision: from the website creative they see to the time they spend waiting in a queue to speak to an agent. However, those decisions aren't meaningful if they can't use data and make a recommendation in real time. To that end, we have developed a machine learning platform that is constantly making decisions and is constantly learning to account for new data and trends.

In this special guest feature, Dr. Michael Zeller, SVP, AI Strategy & Innovation at Software AG, discusses how enterprises have reached a pivotal moment for operationalizing machine learning. For a while now artificial intelligence has been over hyped, its benefits over promised and, in the end, it always under-delivered. Now the hype is back stronger than ever. For AI to not fade away again, however, it needs to be made actionable. Previously, Dr. Zeller was Co-Founder of Zementis, where he used his vision is to help companies deepen and accelerate insights from big data through the power of predictive analytics.

Artificial intelligence (AI) and the Internet of Things (IoT) go together like philosophy goes with Greece. It was AI, the IoT and a shared philosophy of openness that brought Software AG and Zementis together; first into a productive partnership and then into a merger of the two companies. Michael Zeller, co-founder of Zementis and now senior vice president of AI Strategy & Innovation at Software AG, said: "What we had in common was an open platform philosophy; that openness led us both to customer success during our partnership." The synergies were obvious, said Zeller. "As part of Software AG, we could do so much more; with Apama for streaming analytics and us for ML we can go way beyond what each could do separately." AI, along with machine learning (ML), offers organizations the ability to turn data into business value.

Over the last decade, we have seen tremendous interest in the application of data mining and statistical algorithms, first in research and science and, more recently across various industries, that has led to the development of myriad solutions by the data science community. Most of the times data science algorithms are built standalone on platforms like R or python etc. In order to build a data-driven product or use these algorithms for real-time predictions it's essential these algorithms get integrated or ported over to the application stack. Let's say your data Science team has built an amazingly accurate model in R using some package which has a built-in algorithm and we are ready to put it to work. However application servers run on Java, and this particular package is not available in Java.

Operational deployment in your business process is where AI, machine learning and predictive algorithms actually start generating measurable results and ROI for your organization. Therefore, the faster you are able deploy and use these "intelligent" models in your IT environment, the more your business will reap in the benefits of smarter decisions. In the past, the operational deployment of AI, machine learning and predictive algorithms used to be a tedious, labor- and time-intensive task. Predictive and machine learning models, once built by the data science team, needed to be manually re-coded for enterprise deployment in operational IT systems. Only then predictive models could be used to effectively score new data in real-time streaming or big data batch applications.

On Monday, I outlined my view of what makes "an analytic" and the jargon that goes along with it. I ended the post wondering whether "an analytic" is just another name for "business rules" that organizations tend to follow today. In my view, the main difference between the two, which might be no difference at all for a given situation, is that "analytics" are generally more complex mathematically and operate on a more general "feature space". This generalization allows rigorously developed techniques from statistics and applied mathematics to have a chance at being applied to a messy real world problem. Business rules tend to be more human understandable and more directly embedded into the specific data formats or information processing software used by a business.

Mainframe machine learning poised to take off. Is Terminator Skynet far off? So far the mainframe big data story has been very useful, but pretty tame: logs for operational intelligence, improved cybersecurity, improved retention period, fancier dashboards. Here's betting that it's going to get much more interesting -- and probably already is in some shops. ML is a discipline that Google has fully embraced.