Intuitive IoT Machine Learning

By | IT/OT Management

Since the Falkonry Service was released, numerous users across several industries have used it to understand their #IoT data and recognize conditions. Their objectives are to prevent undesirable outcomes such as failures and low performance/yields. These industries range from power generation and transportation to agriculture, and building management to automotive and steel production. Our users are mostly non-data scientists and seek a solution that they can create and manage. At times, data scientists prefer Falkonry since it can do one thing really well – provide visibility of conditions without much effort.

Falkonry Service–now it’s easier

Taking into account all of the feedback we received from our users, we have redesigned the Falkonry Service to be simpler and yet more powerful. You can more easily work with many things, assessments, and conditions. This new User Interface has been rolled out to all users. Here’s an overview of this new design.

By distilling the raw information down to a one-dimensional timeline, Falkonry simplifies understanding of high speed, multivariate IoT data. The main design improvement in the updated UI is that you can understand Falkonry’s findings, at a macro level, instantly and drill down in terms of time and things easily. As you can see in the design, there’s the ability to compare the performance of two or more things side by side. Simultaneous visualization of conditions across things means that conditions can be interpreted comparatively.


Secondly, both live monitoring and learning views are designed to be analogous, which makes it easier to understand both. The seamless transition from learning to monitoring also means that users can drill-in from high level summary of conditions across an entire fleet, to a single individual thing for a particular period in time where a certain condition arises.


One thing that did not change is that it is just as easy to learn new predictive models without knowing data science or programming. Users can still easily annotate Falkonry’s findings and confirm the conditions visually, as well as in bulk through files.


Next, menus have been simplified so that our users can easily find the pipelines they work with to manage their account. The menu is always to the left and can easily be expanded or collapsed.

Finally, Falkonry’s machine learning continues to improve and find more interesting behavior on its own. As many of our users have told us, Falkonry’s unsupervised machine learning is the best thing to happen to IoT, and we have been working to make it even better. For example, Falkonry figures out from the data what patterns repeat and at what interval so that any periodicity is automatically spotted without the need to give hints or tweak anything.

We continue to make this intuitive experience of IoT data analytics and visualization even more powerful and usable. Even as our technology improves and the Falkonry Service is upgraded, you can be assured that your data and prediction models will be just a click away.

Falkonry’s advanced analytics and simple visualization means that computers are left to do what they are good at – computation – and people can control what the computations do without having to perform detailed debugging or adjustments. There should be no reason for people to pore over hundreds of plots constantly looking for patterns when such work can be efficiently and effectively performed by computers using Falkonry Condition Prediction Service.


This post was originally written February 6, 2016.

Falkonry Listed in Top 125 Startups in Industrial Internet of Things!

By | IT/OT Management

Falkonry thanks @CB Insights for its inclusion into the Industrial Internet of Things Top 125 Startups.  We’re in the Advanced Analytics, Edge Intelligence and Protection category.

Falkonry accelerates continuous improvement of production operations through intelligent pattern recognition/AI, to increase uptime, quality, yield.  It does this by connecting to the real-time data that exists within an enterprise–be that from an edge device, or from an enterprise application, like ERP, MES or PLM, and supplementing legacy process control applications such as a PLC, DCS or SCADA system. Together, we’re better!  For more information on how Falkonry helps an IIoT architecture, please visit Falkonry Pattern Recognition for IoT.

Falkonry is a Critical Component in the Next Wave of Technology

By | IT/OT Management

Falkonry discovers, recognizes, and predicts operating and performance conditions from time series data, using machine learning/pattern recognition. From its inception, the goal was to help industry become more productive using advanced signal processing techniques.


Falkonry logo

Falkonry builds on key technologies that are becoming prevalent in industry: #IoT/#IIOT (the Internet of Things); #ML (Machine Learning) and #AI (Artificial Intelligence).  Let’s look at each of these.

#IoT: Markets and Markets values the Internet of Things at $157.05 Billion in 2016, growing to $661.74 Billion by 2021, at a Compound Annual Growth Rate (CAGR) of 33.3% over the period. Growth is facilitated by a number of factors: the decreasing cost and proliferation of devices/sensors; the accessibility of low cost services, such as the #cloud and #big data processing, and the influx of new application providers, bringing choice into the market for users.  They highlight data management software is gaining high market traction with the help of cloud and predictive analytics to manage data generated from machineries. Falkonry, at its core, uses predictive analytics and pattern recognition to identify operations improvement opportunities, such as OEE (Overall Equipment Effectiveness) tuning.

#ML: Ironpaper.com defines machine learning as an area of computer science where machines learn patterns and recognize material without being explicitly programmed by a human. The cognitive computing market size is expected to surpass $12 billion by 2022, according to Grand View Research.  Falkrony uses Machine Learning to analyze data set features and provide condition analysis on a custom data set. It allows the automation of large-scale condition detection that previously required a massive human effort and cost to implement. Typical applications are Industrial and Transportation operations and maintenance, IT system anomaly detection, and others.

#AI: In a separate study, Markets and Markets forecasts the artificial intelligence (AI) market to be worth $16.06 Billion by 2022, growing at a CAGR of 62.9% from 2016 to 2022. Artificial intelligence is a consolidation of state-of-the-art technologies which are used to develop products which work similar to human intelligence.  AI is a broad category and includes hardware, software and services providers. Falkonry AI provides a cognitive/AI service that can automate the interpretation of telemetry and sensor data from industrial activity, IT processes, and high-end consumer assets. The Falkonry AI block allows you to analyze your massive realtime streams of data, and lets you build models or predict future action based on those data streams.

So, with Falkonry, the key technologies that are shaping the industrial market–#IoT, #ML, and #AI, are offered in one easy to use service. It allows companies that generate massive amounts of data, such as those in the process industries (oil and gas; metals, mining and minerals; pulp and paper) to detect, analyze, and understand the areas within a process that can be improved; where bottlenecks and choke points reside; and where in the process flow tuning can be applied.

For more information on Falkonry, please visit www.falkonry.com.