From the course: AWS Certified AI Practitioner (AIF-C01) Cert Prep
Unlock this course with a free trial
Join today to access over 24,700 courses taught by industry experts.
Foundation model business objective criteria - Amazon Web Services (AWS) Tutorial
From the course: AWS Certified AI Practitioner (AIF-C01) Cert Prep
Foundation model business objective criteria
- How do we determine if an AI model is delivering business value? Well, there are a number of objectives and considerations that we should take a look at to be able to do this. The first is just whether or not the result aligns with business objectives. Does it address specific goals? We can use performance metrics to find KPIs that can be used to measure whether or not business value is being delivered. We can solicit user feedback, qualitative insight from the end users and we can evaluate how well the model has integrated into the existing workflows without causing other issues and so how do we evaluate productivity? We have an example of a customer support AI chatbot. Well, some metrics we could use to help with this is the average response time and ticket resolution rate. We could even use sentiment analysis on the conversation as a sort of metric and the analysis for this would be to compare the the pre-AI model metrics with the post-AI model metrics to assess the impact on…
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.
Contents
-
-
(Locked)
Module 2: Fundamentals of AI and ML introduction36s
-
(Locked)
Learning objectives31s
-
(Locked)
Basic AI terminology4m 52s
-
(Locked)
Introduction to machine learning6m 38s
-
(Locked)
Introduction to deep learning2m 47s
-
(Locked)
Question breakdown, part 12m 53s
-
(Locked)
Question breakdown, part 22m 40s
-
(Locked)
-
-
(Locked)
Learning objectives36s
-
(Locked)
ML pipeline components5m 11s
-
(Locked)
ML model sources and deployment types2m 44s
-
Introduction to MLOps3m 46s
-
(Locked)
AWS ML pipeline services4m 34s
-
(Locked)
ML model performance metrics3m 11s
-
(Locked)
Question breakdown, part 12m 34s
-
(Locked)
Question breakdown, part 22m 49s
-
(Locked)
-
-
(Locked)
Module 3: Fundamentals of generative AI introduction41s
-
(Locked)
Learning objectives28s
-
(Locked)
Basic generative AI terminology4m 8s
-
(Locked)
Generative AI use cases4m 14s
-
(Locked)
Foundation model lifecycle2m 35s
-
Question breakdown, part 12m 53s
-
(Locked)
Question breakdown, part 22m 32s
-
(Locked)
-
-
(Locked)
Module 4: Applications of foundation models introduction41s
-
(Locked)
Learning objectives34s
-
(Locked)
Pretrained model selection criteria5m
-
(Locked)
Model inference parameters3m 54s
-
Introduction to RAG5m 1s
-
(Locked)
Introduction to vector databases4m 15s
-
(Locked)
AWS vector database service3m 16s
-
(Locked)
Foundation model customization cost tradeoffs3m 16s
-
(Locked)
Generative AI agents5m 17s
-
(Locked)
Question breakdown, part 12m
-
(Locked)
Question breakdown, part 22m 50s
-
(Locked)
-
-
(Locked)
Learning objectives35s
-
(Locked)
Prompt workflow2m 42s
-
(Locked)
Prompt engineering concepts4m 43s
-
(Locked)
Prompt engineering techniques6m 16s
-
(Locked)
Prompt engineering best practices2m 33s
-
(Locked)
Prompt engineering risks and limitations3m 53s
-
(Locked)
Question breakdown, part 13m 48s
-
(Locked)
Question breakdown, part 22m 50s
-
(Locked)
-
-
(Locked)
Module 5: Responsible and secure AI solutions introduction46s
-
(Locked)
Learning objectives43s
-
(Locked)
Responsible AI features4m 8s
-
AWS responsible AI tools3m 41s
-
(Locked)
Responsible AI model selection practices3m 26s
-
(Locked)
Generative AI legal risks3m 25s
-
(Locked)
AI dataset characteristics2m 47s
-
(Locked)
AI bias and variance4m 54s
-
(Locked)
AWS AI bias detection tools2m 1s
-
(Locked)
Question breakdown, part 13m 18s
-
(Locked)
Question breakdown, part 23m 16s
-
(Locked)
-
-
(Locked)
Learning objectives39s
-
(Locked)
Transparency and explainability definitions3m 20s
-
(Locked)
AWS transparency and explainability tools3m 48s
-
(Locked)
AI model safety and transparency tradeoffs3m 21s
-
(Locked)
Human-centered AI design principles3m 37s
-
(Locked)
Question breakdown, part 13m 22s
-
(Locked)
Question breakdown, part 23m 54s
-
(Locked)
-
-
(Locked)
Learning objectives39s
-
(Locked)
AWS AI security services and features6m 1s
-
(Locked)
Data citations and origin documentation3m 27s
-
(Locked)
Secure data engineering best practices4m 54s
-
(Locked)
AI security and privacy considerations4m 56s
-
(Locked)
Question breakdown, part 12m 38s
-
(Locked)
Question breakdown, part 22m 47s
-
(Locked)