Telecom
❯
Predictive Analytics
❯Product failure prediction for critical IT infrastructure
Product failure prediction for critical IT infrastructure
For:
QA engineers, manufacturing line technicians, technical sales
Scope:
Building an AI solution to augment QA engineers
Goal:
Other
Active antenna array satellite
For:
Operators of satellite communication systems
Users of satellite communication systems
Regulation authorities
Space agencies
Scope:
Determine optimal spot beam patterns for communication satellites in order to
react to changing geographic distribution and bandwidth requirements of
terminals.
Goal:
Other
Carrier interference detection and removal for satellite communication
For:
Operators of satellite communication systems
Operators of other communication systems (satellite or non-satellite) that are
potential sources of interference
Users of satellite communication systems
Regulation authorities
Space agencies
Scope:
Machine-learning-based detection, classification and removal of interference
signals for satellite communication systems.
Goal:
Other
AI for customers' loss prevention of telecommunication services
For:
Telecommunication companies
Goal:
Improved Customer Experience
Product failure prediction for critical IT infrastructure
For:
QA engineers, manufacturing line technicians, technical salesGoal:
OtherProblem addressed
Reduce the likelihood of releasing defective batches of hardware
Scope of use case
Building an AI solution to augment QA engineers
Description
The hardware manufacturing company was using a few QA
engineers to make subjective calls on whether or not a
specific batch was good enough to be released into the
market. The graphical representation of the shortfalls and
defects was also done manually. This led to inconsistent
labelling and many unsatisfied customers. To augment the
QA engineers, a deep learning AI model was developed to do
more accurate and consistent labelling of which batches
were likely to be most defective and the major type of
defects.