After overcoming challenges in time series data quality, ML Ops, alerting, and advanced analytics, Falkonry is now integrating MQTT, and Unified Namespace (UNS)
It is believed that maintenance events record the same information as machine data. Here are 4 myths of maintenance logs and machine data that betray this belief
Underground nuclear explosions produce a unique seismic signature, which can be monitored and analyzed in real-time using time series AI to raise alerts
Getting the right models to the right places at the right time is difficult at scale. MLOps solves that problem but many need help making it simple enough.
Finding opportunities to increase analytics capability in highly capable organizations is hard. It takes a team to ID the overlaps which bring success.
Building analytics capability from a starting position of lower technical and cultural readiness does not guarantee failure. It might actually be a blessing
Industrial data analytics evaluations face both technical barriers and expectation gaps. Overcoming both challenges requires a shift in how such evaluations are framed.
Improving analytic capabilities requires a concerted effort between the organizational culture and the technology deployed for using the data generated.
Altering an established Continued Process Verification (CPV) process is difficult because of regulatory challenges. But simply assisting CPV – leaving the regulatory portion untouched – can have significant benefits.
Falkonry's AI SaaS platform, Clue, is designed to leverage Azure's IoT infrastructure in such a way that integration is easy, and manufacturers begin to gain insights from existing operational data swiftly
The ubiquitous use of Fault Detection and Classification (FDC) systems in the semiconductor industry, highlights the importance of building a strong technical and cultural foundation, in order to support your predictive and prescriptive goals.
Traditional operational excellence strategies are being overtaken by machine learning, which are giving way to predictive digital twins. Find out how predictive digital twins help solve complex operational problems before they impact business.
The founding father of digital twin concept and others talk about their intrepretation of digital twin and digital product life cycle at The American Society of Mechanical Engineers (ASME) summit
Falkonry's time series AI platform provides a flexible modeling approach for SMEs to use operational data to quickly create models to solve unique problems
The Falkonry Time Series AI platform provides a path to machine learning with realtime data. Get better results using the richness of current production context.
Coaches are operational excellence and innovation staff that find the strategy, develop tactics, and secure tools. Operators are the players in the sport
The article highlights three substantial barriers to successful adoption of predictive operations when relying on historical data and describes a system which overcomes these barriers using a connected learning approach that leverages operations expertise.
By making Operational AI adoption and deployment easy and scalable, Falkonry customers now have an extremely cost-effective and risk mitigated path to start realizing significant ROI through predictive operations.
The work of achieving operational excellence has gotten more and more difficult. What is needed to successfully deploy predictive analytics in manufacturing?
Data patterns are often too complex for humans to see. Learn how Falkonry does multivariate pattern analysis, leading to powerful ML based predictions.
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