Validation Services
for AIML
Operationalizing machine learning ‘AIML’ is difficult
Dependencies in background knowledge create validation challenges:
- Individual ML efforts rely on the same background knowledge
- Background knowledge must get updated appropriately
- The training and deployment of different AIML algorithms is
based on that knowledge
These validation challenges must be overcome to operationalize AIML
without compromising system confidence, adaptability or robustness.
If you are trying to operationalize AIML.
Blender Logic can help.
Validation Services
for BI
Organizations are investing heavily in enterprise BI systems
that track,
analyze and predict. These systems define organizational
knowledge. AIML
appears to offer better predictive performance but is
disconnected from
existing BI systems The challenge is why and how to
extend BI systems with AIML.
If you are trying to use AIML
to improve your enterprise BI systems.
Blender Logic can help.
Validation Services
for Data Catalogs
for Data Catalogs
Organizations invest in machine learning data catalogs MLDCs
to better access enterprise data
The problem is MLDCs do not provide a formal basis
for labelling or attributing catalog entries:
- Workers rely on meta attributes to query data
- Machines cannot directly analyze the data made accessible
through the catalog - Chief Data Officers lack critical insight into the
asset and liability characteristics of their data