Falkonry’s unique approach to deep learning models exploits GPU acceleration at scale and optimally enables AI for industrial automation

Find out how AI-enabled automated strip break classification can improve the efficiency of a tandem cold steel rolling mill.

Nikunj Mehta, Founder & CEO, Falkonry talks to Cybernews about AI adoption in the near future, digital transformation, information security risks and more.

Crick Waters, SVP Enterprise at Falkonry, talks about the challenges, opportunities, and benefits of applying AI at scale in steel industrial transformation

With steel plants producing terabytes of data, automated AI analysis is the only way to make sense of it. Yet AI implementation challenges still remain.

Are you sure the needle you’re searching for is the most important thing in that data haystack? How does one figure out what else is in there?

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.

Overcome the organizational impedance mismatch between operations and data science to sow the seeds of a widespread, productive analytics culture.

Building analytics capability from a position of lower technical readiness but high cultural readiness lets you focus on “how” instead of “why.”

Building analytics capability from a starting position of lower technical and cultural readiness does not guarantee failure. It might actually be a blessing

The analytics foundation you build depends on your current capabilities. This is a framework for thinking about those capabilities.

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.

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

It is crucial that a F&B manufacturer enables predictive maintenance and predicting quality issues to cope with fluctuating demand and lower costs

The U.S. DoD is pushing for digitalizaton through machine learning. A key area of focus is to improve asset readiness and performance

AI software should be available in every desired cloud - SaaS, Private and Airgap. This is a must for industrial and national security organizations.

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

COVID-19 has shown digital transformation is a requirement. Post COVID strategies will emphasize resilience, requiring more agile operations than ever before.

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.

Successfully crossing the POC stage into production is hard with most attempts ending in failure. What can be done to maximize your chances for success?

Learn about the four drivers a technology needs to be adopted at scale and how they apply specifically to AI and predictive operations.

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.