Machine Learning Information Systems

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Advanced Machine Learning Algorithms - This post is the eighth in a series discussing a machine learning use case for a mobile app provider. The link to the full case study can be found at the end of the post. The first post can be found at https://www.inetsoft.com/blog/machine-learning-concepts-defining-churn-predictive-metrics/ Our previous posts have covered the machine learning algorithms that would be most commonly used and that would more accessible for non technical people. What other advanced machine learning algorithms should a business person know about? There are others that are more advanced and really would be used by data scientists. But since your machine learning journey might end up with collaboration with a data scientist at some point, it helps to know the other tools that are available to them. Two areas, deep learning and dimensionality reduction, are of interest. Deep learning, also call modern neural networks, uses a large number of computing nodes to simulate a biological neural network. Because of it is technical in nature, normally data scientists are needed to implement these ML algorithms. It is interesting to know that, many times these are just a different approach for classification and regression types of models already discussed in earlier posts. This is especially true when the data is not regular, structured business data. For example, photo recognition and image classification are commonly done with deep learning. Dimensionality reduction can also be of interest to you. These types of algorithms are used to simplify data so that the result can be more easily applied in other machine learning algorithms. Another application is to simplify data for human consumption such as for visualization...

 

Advantages of an Orchestration Engine Over Spark Clustering - When comparing orchestration engines to Spark clustering, it's important to understand that they serve different purposes within the data processing and infrastructure management ecosystem. Apache Spark is primarily a distributed data processing framework optimized for large-scale data analytics, while orchestration engines (like Kubernetes, Apache Airflow, or AWS Step Functions) manage the deployment, scheduling, and coordination of various applications and services, including Spark jobs. Here are the key advantages of using an orchestration engine over relying solely on Spark's native clustering capabilities...

AI and Machine Learning - A set of buzzwords we are starting to hear every day is Artificial Intelligence (AI) and Machine Learning (ML). In addition to the vast business potential of these technologies, the more popular take for most people is perpetrated by the entertainment industry, which has us all thinking of what could happen if AI or ML equipped devices became self-aware. This is by no means a new fear. In 1927, Fritz Lang's film Metropolis effectively expressed the fear that the society of his time had for thinking machines. More disturbing portrayals showed in both 1984's The Terminator and 1999's The Matrix giving a chilling picture of what might happen if these autonomous digital minds were able to take over completely. Much of this fear is grounded in the definitions of the two tools: AI refers to when non-human systems utilize cognitive functions such as learning and problem solving. While Machine Learning has powerful computers sorting through massive amounts of data and extrapolating probabilities from there. The implications of this technology has wide-reaching promises for business and daily life, but the chance of us bowing to computer overlords is more remote than Hollywood would like to make it seem...

Applying Machine Learning to Improve Customer Service - Now let's move on to the next topic in applying machine learning to improve customer service. Twenty percent of companies are already utilizing virtual digital assistants. They interact with employees and with customers in a fast paced manner. If you look to the future, more than two thirds of the organizations are considering implementing such digital assistants over the course of the next two to three years. I'm going to share one example as well which is support ticket specification. If you look at the common customer service issues and you have a lot of tickets coming in and these issues contain common keywords like bill or payment. They appear often in the support ticket category. If we look to machine learning, they could learn the distinction between these words and between categories, and they can identify the regular patterns. They can support agents to use their application to automatically categorize tickets and provide a first suggestion so this speeds up the whole process of support. The algorithms give this suggestion for a level of accuracy, and then the machine learning algorithm directly adds words to tickets based on the predictive category for the next agent, and this speeds up this whole support process. This is an area where we're going to see rapid improvement just like the improvement in cars. It started with no automation and then maybe you had a cruise control and maybe road lane assist and maybe an adaptive cruise control and maybe a navigation system and soon you're on your way to self-diving vehicles...

Before You Start a Machine Learning Project - I think it's just really important to think about before you start a machine learning project or an analytics project, how are you going to tell if this is making sense, if you're saving money, if you're creating revenue, if you're finding knowledge? Before you get involved with one of these projects you need to think about how you're going to assess it. That varies a lot by different businesses but being able to have a feedback loop where you can tell how well your machine learning project did, you need to think about that from the beginning. How am I going to work that into my machine learning solution? What are my assessment criteria going to be? Am I trying to create revenue? Am I trying to find savings? Am I trying to generate knowledge? Just be aware of that, it's a hugely important part of the process, but we're running out of time and we're just going to go to questions. All right so we have time for one to two questions. Let's lead off this one: do you have any practical examples in the area of manufacturing? Yes, but unfortunately I can't talk that much in detail about it for confidentiality reasons since this is a real customer use case. We work with a large manufacturer of high tech devices that are used in computers and cell phones. It's an older company. They have their manufacturing process nailed down just perfectly, but they want to keep pushing that. They want to keep improving them...

Benefits of Machine Learning Applications for Better Marketing - Machine learning is not the future, but reality - these technologies are already used in dozens of fields and industries, help to automate and optimize the work with data. Marketing was not an exception, which is undergoing yet another transformation in modern digital conditions. Now is a great time to work in the marketing department, as in the modern information world, marketing is becoming increasingly important in most organizations. But it also means that the life of the marketer has become more complicated, despite all the tools at his disposal. Marketing specialists have to solve many problems...

Changing Business Analytics with ML - Thank you all for joining us today for a discussion about how machine learning is changing business analytics. We have four major points that we want to discuss today. The first one being the importance of data science and data scientist and bringing machine learning into organizations. The second one being we've all heard of the V's of big data. and we know that one is velocity, and we know that there's a lot of streaming data out there now. This is going to be a big part of organizational strategies moving forward. Point three, how an organization can keep creativity with machine learning. We have all of these different tools to choose from today, all of this different data, but we deal with regulation, we deal with documentation, we deal with productionizing code. How do we keep infusing creativity into the machine learning workflow within an organization? Then, we've also heard a lot about the citizen data scientist recently and just in general more and more people in organizations wanting to get involved with analytics and machine learning, so that's point four. Okay, so we're going to start our discussion here. Is any of this really new? Is machine learning new? Is data science new? To me this is resounding no. In fact, machine learning has been studied at least since the 1950s, maybe before. Data science you could say goes back to John Tukey's 1962 Future of Data Analysis Paper. There's a great recent paper by David Donoho out of Stanford that talks about 50 years of history of data science, and I urge you to read that. We have a link to that at the end. We're seeing machine learning in organizations now. This isn't coming out of the blue. This has a long history, and so we wanted to spend a little bit of time here. One good thing to do at first is of course to define machine learning, and that's really tricky. I think for better or for worse, in a certain sense machine learning has taken on sort of a pop culture, meaning it's just the rebranding of analytics or data mining...

Creating Data to Analyze with ML - So we talk about machine learning and analytics, predictive analytics, the platform itself actually becomes a data creation platform, and that also adds to the different varieties of data. So it actually ends up compounding the drive to work towards the variety of the platform itself. The opportunities that it creates for using the data that comes in will undoubtedly be unlocked. It will keep growing. The varieties are never going to go in other direction. It's all is going to be increasing, and that's a great opportunity. It means different data types, script data and non SQL data will continue to increase a little. Coming back to how you actually get that data coming the other direction with SQL becoming more and more important, but in the underlying data, the variety is only going to increase. Abhishek: Larry, any thoughts you may want to add in here...

Data needed for machine learning tools to detect and predict churn - This post is the second in a series discussing a machine learning use case for a mobile app provider. The link to the full case study can be found at the end of the post. The first post can be found at https://www.inetsoft.com/blog/machine-learning-concepts-defining-churn-predictive-metrics/ What data is needed for machine learning to detect and predict churn? The use case we are discussing used 60 days of user activity data before a 30-day no-use window. Sometimes, straight raw data can be used from an organization's operational data stores,but many times, data needs to be transformed or cleansed for machine learning modeling. For this activity-based use case, it is apparent that raw data must be aggregated to create a new metrics. User activity data and any other data items associated with a user that the machine learning model will use as inputs are called "features." Examples for a B2B cloud-based solution provider would be subscription period and number of support cases. Correspondingly, each user is also marked as "churned" or "not-churned," which is called a "label". In other words, each user will have a set of associated features as inputs that determine the output of the "label." Each labeled user, in this case, is called an "observation." Machine learning uses existing observations to study the relationship between features and the label. The goal is to produce a machine learning model that can assign a label given a set of features about a user. Some features are apparent and readily available. But most times, this step requires intimate business knowledge to pick out the right data likely to be correlated or causative with the outcome. In the real world, this also probably will be an iterative process of experimenting by examining machine learning model test results. This is also a collaborative process with the technologist because machine learning requires data in certain ways. For example, when two numerical features are on very different scales, their influence can be different. Then these features must be normalized so that their scale will not distort the learning model...

Definition of Machine Learning Operations - To guarantee that machine learning models can be deployed, monitored, and maintained at scale, the technique of "machine learning operations" (MLOps) integrates machine learning with DevOps. MLOps gives businesses end-to-end visibility into the machine learning lifecycle while automating processes to streamline the entire machine learning pipeline, from development to deployment. We will go through the definition of MLOps, its advantages, and some best practices for using it successfully in this post. To guarantee that machine learning models can be deployed, monitored, and maintained at scale, the technique of "machine learning operations" (MLOps) integrates machine learning with DevOps. MLOps offers enterprises a platform for quickly developing, evaluating, and deploying machine learning models. Moreover, it makes it possible for businesses to manage machine learning models as they change over time. Data preparation, model training, testing, deployment, and monitoring are just a few of the tasks involved in MLOps. It makes use of the DevOps tenets of continuous integration, continuous delivery, and continuous deployment to automate these processes and guarantee their efficient and reliable execution...

Difference between AI and Machine Learning - A set of buzzwords we are starting to hear every day is Artificial Intelligence (AI) and Machine Learning (ML). In addition to the vast business potential of these technologies, the more popular take for most people is perpetrated by the entertainment industry, which has us all thinking of what could happen if AI or ML equipped devices became self-aware. This is by no means a new fear. In 1927, Fritz Lang's film Metropolis effectively expressed the fear that the society of his time had for thinking machines. More disturbing portrayals showed in both 1984's The Terminator and 1999's The Matrix giving a chilling picture of what might happen if these autonomous digital minds were able to take over completely. Much of this fear is grounded in the definitions of the two tools: AI refers to when non-human systems utilize cognitive functions such as learning and problem solving. While Machine Learning has powerful computers sorting through massive amounts of data and extrapolating probabilities from there. The implications of this technology has wide-reaching promises for business and daily life, but the chance of us bowing to computer overlords is more remote than Hollywood would like to make it seem...

Difference Between Machine Learning and Data Mining - I think there is a difference between machine learning and data mining. Machine learning is similar to data mining because a lot of the pioneers in data mining are still around, and they are pioneers in machine learning. In my mind part of the main difference was the emphasis even in the terms. One emphasizes mining. The other one emphasizes learning in terms of the branding. I think that's one of my observations. The other thing of course is the notion that we have to separate empirical results versus theory. So people in industry I guess they care about theory to some extent, but at the end of the day it's empirical results that matter. I think the connotations data mining sometimes brings up, and if that's historical consequence I don't know, is that, it's about torturing data. It's about mining it till it confesses with whatever the preconceived notions you had going into it. I think maybe that's a little bit unfortunate. Indeed I think that's probably more where we want to be than in that drill-till-you-find-something mentality. Machine learning has always been used as a part of data mining. Data mining involves all this data storage and data manipulation, and also machine learning or statistics is where we learn from the data. So I think a lot of comments are leading us towards this theme of automation. There is one more point that I wanted to emphasize which is actually for whatever reason, the techniques that statisticians have historically brought to the table became ill-suited at some point, because they didn't scale particularly to the number of variables...

Easy Machine Learning with InetSoft - Ever since the creation of the Apache Hadoop project more than 10 years ago, many attempts have been made to adapt it for data visualization and analysis. The original Hadoop project consisted of two main components, the MapReduce computation framework and the HDFS distributed file system. Other projects based on the Hadoop platform soon followed. The most notable was Apache Hive which added a relational database-like layer on Hadoop. Together with a JDBC driver, it had the potential to turn Hadoop into a Big Data solution for data analysis applications. Unfortunately, MapReduce was designed as a batch system, where communication between cluster nodes was based on files, job scheduling was geared towards batch jobs, and latency of up to a few minutes is quite acceptable. Since Hive used MapReduce as the query execution layer, it was not a viable solution for interactive analytics, where sub-second response time is required. Easy Big Data Analytics Dashboard Example View more examples in the InetSoft visualization gallery data intelligence intro Register This didn't change until Apache Spark came along. Instead of using the traditional MapReduce, Spark introduced a new real-time distributed computing framework. Furthermore, it performs executions in-memory so job latency is much reduced. In the same timeframe, a few similar projects have emerged under the Apache Hadoop umbrella such as Tez, Flink, and Apex. Finally, interactive analysis of Big Data was within reach...

Education Sector's Applications of Machine Learning - When most students hear about machine learning, they believe that it means that there is no teacher or some alien technology involved. In truth, things are more prosaic as the education system has only started implementing innovative machine solutions in practice. There are online video lessons and automatic guides that help students proceed through the course, yet machine learning goes further than that. It also stands for mobile learning applications, video surveillance systems, and artificial intelligence, which are used on a daily basis. The future of such an approach is most likely to develop in terms of learning assistance and providing help akin to what a physical teacher offers. It will always evolve according to human needs, yet it should not be seen as a substitute for existing practices or an actual psycho-social interaction between educators and students. The key is to implement machine learning wisely and study the benefits...

Enabling the Intelligent Enterprise with Machine Learning - I recently read that two-thirds of the CEOs of global 2000 enterprises are going to have digital transformation as the center of their corporate strategy. In order to achieve this, enterprises are going to have to embrace machine learning. An enterprise needs to start reimagining their customer value and the internal operating model by implementing machine learning solutions now. Machine learning intelligently connects people, things and businesses, and it's connecting usual experiences. It's connecting devices and APIs that promote a network of efficiency and innovation throughout the whole organization. Forecasts indicate machine learning will grow by almost 50% by 2020 and is having a huge impact on the knowledge economy, of course. Machine learning systems and knowledge workers will be able to work together much more effectively and much more successfully with the help of machine learning. Furthermore the market is expected to be worth almost $47 billion by 2020. I think enterprises should begin to consider implementing a machine learning strategy into their infrastructure. It's, of course, not without any challenges. Enterprise needs to have a modern business environment conducive to adapting machine learning technologies. Legacy systems and a silo approach to data analytics are not optimal environments for supporting machine learning strategies...

Examples of Why Machine Learning Still Has Human Bias - So let me just give you one or two examples of why that frankly is not good guidance. The first is you have to select the data set that you're going to use. With the machine-leaning technique, human bias is front and center. What do you select? The second is you need to select a machine-learning technique against the data that you now selected, right? That is point of human bias number two right. Then three, just to make it a nice round number is you then have to basically interpret and tweak the output. In many cases in machine-learning, a technique is actually used. What is the point of bias number three? You can't remove human bias from this, simply because you're throwing around the marketing term, visioneer, which is machine-learning right. And it's kind of just front and center of having some basis about what your claiming to understand. It stirred things up to be that declarative about why that was not terribly the best guidance. Ultimately what was interesting is that the people involved tried to turn it into me being you know not a nice guy because I pointed out some facts here. They never ever actually push back on the idea that you know what they said was not correct. The reason for bringing this up is that anyone that makes assertions in this space that is unwilling to engage on their assertion or provide evidence for what they say is probably someone that doesn't know what they're talking about. You need to look at things people say very carefully whether it's us or it's other people in the space, consultants you or software sellers, anyone that makes assertions or claims that are big and sweeping, and that are universal...

Faster Better Cheaper Analytics and Decision-Making with ML - Okay well, faster, better, cheaper analytics and decision-making, these are the goals I see very often in many verticals. The stakes here and the scale here just seem to be much larger. So speaking of scale you have a fourth generation analytic solution. Tell us at the high level a little bit about that before we delve a bit more detail about the technological requirements. What is it that you've developed that helps deal with these issues of scale? Jim: So our platform really harnesses big data pipeline technology that makes it accessible to users in a very interactive and highly seamless in immersive environment will allows them to zero in on pieces of data. And the analytic tools allow them to make decisions very quickly and with very high quality confidence that the data is going to really help to make a high quality decision. So we bring large amounts of claims and other sources of data together, and we fuse that together into a very large data set, and we run a lot of machine learning analytics over the top of that and provide a variety of visualizations and navigational constructs for them. We allow them to see what the provider looks like in terms of how much money that they've been billing, what kinds of activities have they been doing, what relationships they have to other providers or other institutions and to really surface in a very visual way that interconnectivity that they had into a healthcare network or a plan...

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