How would you write an architecture strategy around AI & Machine Learning taking into account any business strategy and infrastructure and vendor strategies which may already be in place?
A.t.m.h.o. it should at least contain the following aspects (ranking without prioritization): 1.) Scalability 2.) Performance 3.) Interfaces to data collection and data provisioning 4.) Current framework and limitations, especially when they're regarded as obstacles 5.) Staffing an staff development connected to the architecture 6.) Resilience, security and cyber-security awareness 7.) Backup & Archiving 8.) Reviewing time intervals and external factors influencing the strategy and its adoption 9.) Platform demands in terms of ML / AI approaches used 10.) KPIs for success measurement (e.g. provisioning time, target time time for ML Ops roll out being supported by the architecture, etc.)
Content you might like
How often does your organization review and update privileged access policies?
Monthly16%
Quarterly51%
Biannually9%
Annually18%
Only after a security incident or audit finding4%
We do not have a regular review process
Other (please specify)2%
View Results
Anyone using a transcription service for meeting minute capture for larger meetings that are held in person? If so which tooling are you using? Seen lots of transcription tools for video meetings, but I'm looking to see if anyone has implemented a solution for meetings in person that might have 10 to 12 people in a room.
Digital transformation isn’t just about new tools, it’s about changing how teams work. Key lessons I’ve observed: 1. Start with process bottlenecks, not technology 2. Empower cross-functional teams 3. Measure outcomes, not outputs What has been the most surprising lesson your teams have learned during a transformation?
A.t.m.h.o. it should at least contain the following aspects (ranking without prioritization):
1.) Scalability
2.) Performance
3.) Interfaces to data collection and data provisioning
4.) Current framework and limitations, especially when they're regarded as obstacles
5.) Staffing an staff development connected to the architecture
6.) Resilience, security and cyber-security awareness
7.) Backup & Archiving
8.) Reviewing time intervals and external factors influencing the strategy and its adoption
9.) Platform demands in terms of ML / AI approaches used
10.) KPIs for success measurement (e.g. provisioning time, target time time for ML Ops roll out being supported by the architecture, etc.)