Machine Learning Program Information

InetSoft's machine learning program is built into its industry pioneering BI solution. Read articles about its features and request a personalized demo.

Some Good ML Examples - Today I wanted to write an article to talk about a topic of growing popularity in the business intelligence market: machine learning. Why do we need machine learning? What are the kinds of things we use it for? What are some examples of what machine learning can do? The reason we need machine learning is that there are some problems where it's very hard to write software program to solve. Take recognizing a three-dimensional object, for example. When it's from a novel viewpoint in new lighting conditions in a cluttered scene, it is very hard to do for a non-human system. We didn't know what program to write because we didn't know how it's done in our brain. And even if we did know what program to write it might be that it was a horrendously complicated program. Another example is detecting a fraudulent credit card transaction where there may not be any nice simple rules that will tell you it's fraudulent. You would need to combine a very large number of not very reliable rules, and also those rules change over time because people change the tricks they use for fraud. So we need a complicated program that combines unreliable rules, and those rules need to be changed easily...

Click this screenshot to view a two-minute demo and get an overview of what InetSoft’s BI dashboard reporting software, Style Intelligence, can do and how easy it is to use.

Start Implementing A Machine Learning Solution - This brings us to the next point about how we can get started implementing machine learning solutions. First I think we've seen increasing machine learning processing power that's really required, and it enables you to increase your computing power. Additionally enterprises can implement machine learning when they're dealing with very specific scenarios and very specific conditions. We were discussing already a ton of examples for this. Machine learning is able to make predictions on new data and automate repetitive tasks like the support ticket classification I mentioned earlier, which is a good way to start. We see also enterprises need to properly prepare the data by minimizing their information silos and developing a real time modern data analytics infrastructure. Now we see many organizations are organized by department and have data silos. They need to integrate the data from all the different sources, such as customers and their supplier sources because otherwise you cannot fully use the algorithms if you don't have the relevant data and if you don't have the right quality of data. I think what we see as well is a movement towards the cloud for data storage. To have your data all in the cloud in order to process these high volumes of data that are integrated from all various sources. Moreover, enterprises need to align, and this I think is very important because it's not only technology. They need to align their people, the processes and the technology to create a different and a better organizational foundation that supports this digital core and data driven thinking...

Starting 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...

Supercharging BI with Spark, Easy Machine Learning with InetSoft - Even 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...

The invaluable role of machine learning in modern business intelligence (BI) - In the Age of Information, having access to data alone will not propel your business forward. To evolve your business and gain a sustainable edge on the competition, squeezing every last drop of value from your organization's data is essential. Modern BI software provides a powerful outlet for cleaning, curating, and visualizing data. Coupled with machine learning (ML) technology, it can help business users uncover a layer of insight often overlooked by even the most experienced analysts. Machine learning is becoming increasingly prominent in today's hyper-connected digital landscape, offering a wealth of business-boosting insight. Here, we explore the role ML plays in business intelligence and data analytics while looking at some essential trends, applications, and insights....

Transforming Innovation with ML Applications - Where are we witnessing the most transformative level of innovation in machine learning applications? The deep learning revolution that started in 2013 or so, this started out in the image processing space. It has since branched into video. It has branched into speech recognition and audio processing because the improvements on all new processing and quality on your mobile phones are largely driven by deep learning techniques that came into widespread use two years ago or so. They are now increasingly being applied to natural language processing with very good results. I think the next frontier for the deep learning and machine learning revolution is to also upset this status quo on reasoning and logical conclusions. This is an area where the rule based systems -- best solutions for things like natural language processing or image processing before â€" still reign supreme. We're at the next wave of deep learning based technology. It is very likely to also bring in and drive substantial improvements and assumption that help us address completing more use cases. I would definitely be reasoning as the next big frontier of what deep learning research can help deliver. Which industries will be most impacted by these developments? Looking at multiple consulting studies on this, what they all have in common is that they are from 2014, 2015 or 2016. They show a landscape of impacts all over the map. What seems to be different in how ML impacts these industries? For some it's the change in the core product or service that they are offering, like automotive manufacturers going to become self-driving car manufacturers...

Click this screenshot to view a two-minute demo and get an overview of what InetSoft’s BI dashboard reporting software, Style Intelligence, can do and how easy it is to use.

University ML Examples - We are currently witnessing how technology changes the world and education in particular. Big Data and Machine Learning have always on everyone's radar due to its unexpected still paramount influence on our daily lives. We certainly remember all the retail, social media, those Tinder ground-breaking cases and other ways to use Big Data, but how has it changed things as fundamental as, say, education? According to Knewton, there are five types of data in the education sector: personal data e-learning (digital workbooks, online courses) student engagement data learning material effectiveness data administrative data forecasting data Let's find out how each sort of data contributes to shaping and improving contemporary education. Personalized Education Globally, the objective of Machine Learning is to enhance the processes and education industry is not an exception for that matter. Educational Data Mining is seen as the most powerful instrument to increase the effectiveness of education as it is today. The following is being achieved through designing those data analysis methods which will enable us to rethink the approach, fill in the gaps and adapt the accumulated experience in order to transform the system...

Use Cases That Were Impossible Before ML - Let's go to the other side of the business to speak about machine learning use cases that were impossible before. What if you could not just put marketing dollars in putting your brand or logo somewhere, what if you couldn't just sponsor a team or a particular venue or event but you could measure the impact and the outcome of that in real time. Thanks to computer vision and full HD video processing we are able to find your logos, your products, your offerings in real time in commercial broadcast quality and video path and to determine accurate impact metrics so that you can measure what you pay for in terms of scholarship for advertising and make that available in near real time. These are capabilities that have never been available on the market before, and this aims to revolutionize the way we do advertising impact metrics and return on advertising investment calculations. We look forward to deriving many additional visual and video base use cases that bring concrete business value to the enterprise. It's interesting to see that machine learning touches basically every aspect of a business, whether it's sales or marketing, whether it's technology, whether it's operation or whether it's finance. Machine learning is and will be everywhere. You will see. This brings us basically to the next topic about some of the popular and innovative machine learning technology and applications that are being implemented today...

Read how InetSoft was rated #3 for implementation in G2 Crowd's user survey-based index.

Using AI Machine Learning Software in Your Business - To many people, AI and Machine Learning are only concepts, things of the distant future, something that we have to wait years for. However, the truth is that we're already on the verge of AI revolution, and the increase in the ease of use of machine learning software has been rapidly increasing its impact in a number of industries. From simple AI-powered chatbots using natural language processing technology to complex autonomous driving systems and warehouse management systems able to deliver a huge increase in efficiency and effectiveness, the world has been using AI-powered tools for quite a while now. For businesses, AI and machine learning software represents a great opportunity to gain competitive advantage, increase sales, and remain relevant for years to come. Many well-known companies are already using machine learning to achieve their goals; for example, DHL deploys it to increase effectiveness and efficiency of their logistics chains and warehouse management, UPS uses machine learning to determine the fastest routes for drivers, and Tesla has AI-powered platforms for self-driving cars. In other words, the future is much closer than you think. "Adopting AI to improve operations is something that you have to do as soon as possible to become an early adopter and outperform your competition," recommends Allison Borders, a digital business advisor at Collegepaper. Here are some potential uses of machine learning software to advance your business and meet the challenges of the future...

Using Machine Learning in Customer Service - Now let's move on to the next topic in using 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...

Click this screenshot to view a two-minute demo and get an overview of what InetSoft’s BI dashboard reporting software, Style Intelligence, can do and how easy it is to use.

Using Machine Learning to Personalize Marketing Offers - In yet another example of how machine learning is being used to help humans make better decisions, Allianz Travel Insurance has upgraded its online platform with added personalization based on machine-learning and artificial intelligence technology to deliver better travel insurance policy recommendations, fighting the perception that a travel insurance partner is a poor place to buy an insurance policy. This new platform selects from 100 different offerings in a matter of seconds, and gets more effective with customer use. According to a recent article in Forbes: "The technology behind this customization is impressive. Allianz collects anonymized information to power a personalization engine from a partner like Priceline to help it find the right product for the customer. That reduces the time spent researching companies and products and virtually eliminates the chance that customers will pay for coverage they don't need… The engine itself takes multiple trip and traveler attributes into consideration, selecting from about 100 different products. What's more, the personalization engine gets smarter every day." The utility demonstrated here underscores how machine learning can make decisions based on far more factors than a human mind can process at once. Just a few of the factors that the system takes into account are: customer service hours self-managed vs outsourced travel assistance services in house medical team vs outsourced trip length connecting flights trip cost group size age of travelers reasons for travels connecting flights timing of trip This type of complex decision making enabled by machine learning algorithms is now available to InetSoft customers, incorporated alongside interactive visualizations, striking a balance between human and machine generated decisions. InetSoft's flexible data layer also makes incorporating even off premise data (such as the anonymized customer data from a third party provider in this example) easy and intuitive. For example, the InetSoft sample dashboard pictured below displays flight delays across the United States in real-time, by continuously pulling the data from the FAA website. Click on the screenshot below to open up the live dashboard. The data is accurate and up to date, so you can even use this dashboard to check and see if your flight is ontime...

Using Machine Learning Technology to Identify Sepsis in Newborns - As covered by tech news site Futurism (click here for the article) the Children's Hospital of Philadelphia has successfully trained machine learning algorithms to identify cases of sepsis in newborns significantly faster, greatly increasing the infants chance of survival. "Using electronic health record data, such as vital signs like blood pressure and temperature, from 618 infants in the CHOP neonatal intensive care unit from 2014 to 2017, the team trained eight machine-learning models to compare vital signs to 36 potential indicators of infant sepsis. Because the data was retroactive, the research team was able to compare the machine-learning models' accuracy to clinical findings. Of the eight models, six were able to accurately identify cases of sepsis up to four hours earlier than clinicians had." Weighing 36 potential indicators is just what machine learning was created for: tasks that require the type of pattern recognition that traditional computers lack, recognizing patterns at a level of complexity that human brains are incapable of processing. Having a machine learning model that you can repeatedly train to make better predictions is the equivalent of having a data scientist with a genius level IQ who is constantly experimenting and writing better predictive models for your organization. Curious as to how these ML models can be integrated with interactive visualizations and automated reporting? Click on the image below to open an InetSoft Customer Churn dashboard which includes a trainable predictive model of customer churn factors...

“Flexible product with great training and support. The product has been very useful for quickly creating dashboards and data views. Support and training has always been available to us and quick to respond. - George R, Information Technology Specialist at Sonepar USA

What data is 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...