DynamicsPower! Melbourne AI for everyone: Virtual Agent & AI Builder
1. AI for Everyone: Dynamics 365 Virtual
Agent & AI Builder
Andre Margono (MVP)
Solutions Architect | Barhead Solutions
2. Agenda
Introduction
Virtual Agent Overview
AI Builder Overview
Findings on Virtual Agent and AI Builder
Design Considerations when Building Chat Bot
3. Introduction
AI & Chatbot have become the hot topic
since 2018.
More and more companies are adapting
and putting their effort in building the AI
and Chatbot offering
4. The (a bit of) History
of AI/Chatbot in
Microsoft World
Azure Machine Learning was
made as public preview
approximately 4 years ago (2015)
Microsoft Bot Framework was
introduced approximately 3 years
ago (2016)
In September 2016, Satya Nadella
stated Microsoft’s vision to
democratizing AI for every person
Azure Bot Service was introduced
a year later (2017). The platform
that speeds up Bot development.
5. Building AI/Bot Capability: 2 years ago
Bot: you will need a dedicated team of developers (Node.js or C#)
AI: you will need a team that consist of data scientist, data analyst, data
architect, really mature data mart/warehouse
Original Image src: https://image.slidesharecdn.com/dataiku-workshoptelecomparistech-bnp-november2015-howtobuildasuccessfuldatateam-151120194439-
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7. Introducing the
Dynamics 365 Virtual
Agent
Currently in Public Preview and only for
US tenant.
Expected to be GA around October
2019.
Rapid Bot Development toolkit! Less
hassle in managing build &
deployment.
Graphical user interface Bot Builder
Intuitive conversation builder
Intent prediction – Built-In as “Topic”
Extensible with Microsoft Flow “Action”
capability
No code required (Unless
using/building custom Flow connector)
8. Starts with the “Topics”
A topic defines the conversation
“Workflow”.
There are 2 types of topics User Topics and
System Topics
Out of the box, there are a lot of user topics
for example (good learning material). You
can delete the unnecessary ones.
Note: System topics can’t be removed, you
can only modify them.
9. Building the Flow that
Compatibles with Virtual Agent
The Flows must be created
within the same
environment as the Virtual
Agent
The Flows must be within a
solution. “My Flow”-only
flows won’t be accepted
The Flows must be built as
“API” (Receive HTTP Request
as the trigger) and must
accept “POST” request.
10. Analyse the Bot
Usage and
Performance
Us the Analytics tab to view
the sessions, feedback and
usage of the bot.
Useful to learn the
pattern/behaviour when we
want to update the bot
conversation/workflow.
12. Introducing AI
Builder
Currently in Public Preview and only for
US tenant. Expected to be GA around
October 2019.
Comes in 4 main AI Models:
Binary Classification
Text Classification
Object Detection
Form Processing
A “Hidden” model (PowerApps canvas
only): Business Card Reader
13. Binary Classification
This AI Model is used to predict specific outcome as
Yes/No output based on the trained historical
model.
Works to predict based on two-options (Yes/No)
field type in CDS + “variable” fields that drive the
outcome.
14. Form Processing
This AI Model provides OCR capability for extracting
key–value pair information from the document
Accepts only PDF, PNG, JPEG (for now)
15. Object Detection
This AI Model is used to detect specific object(s) in an
image.
Can provide the number objects (count) that present in
the image
16. Text Classification
This AI Model is able to classify the context of the text
and giving the tag of the text that it reads.
In short: customisable sentiment analysis
18. Findings: Virtual Agent
When building the Microsoft Flow action, always build the response body
directly, building it in variable renders the Flow to be missing/not recognised as
a valid one.
Array and Boolean output from Flow is not supported yet, but it is mentioned
to be in the immediate roadmap.
Virtual Agent expects a pre-set JSON response and exact structure is required
for it to work.
Rich text (images/files) are not supported yet.
Branding of the bot is not supported yet.
19. Findings: AI Builder – Binary Classification
Binary classification will only classify records where prediction = null, where by
default the prediction has default value. A workaround using Flow/Workflow
might be needed to automate the recalculation
After publishing, the AI Builder classification will run against the record on daily
basis (not real-time).
20. Findings: AI Builder – Object Detection
Object Detection: it is not a sophisticated AI model that can classify object with
different properties. E.g: car model classification (SUV, Hatch, Sedan, etc)
Object Detection: MUST have the category of the object in CDS
Object Detection: Data feed is a manual feed (no integration to keep pulling
data from a system and automatically train the model)
21. Chatbot Design Considerations
Building bot = building apps. Design based on the feature/functionality that
specified as predefined options during the greeting.
Guide the users by providing prompts as much as possible based on options,
rather than free text.
Intent/phrase prediction, this is a “prediction”, always put the “exit” clause if the
bot is not “smart” enough predicting the intent.
Always offer hand-over to human.
Analyse the usage pattern and refine the bot as it goes along.
22. Conclusion
Virtual Agent
With the Virtual Agent capability, you could build a bot with intuitive graphical interface,
with almost 0 code required
Virtual Agent + Flow = endless possibility
AI Builder
This tool is very useful to build AI Model based on pre-defined ML logic/template
No code & data science knowledge required to build the AI Model
Big potential for the future roadmap with more AI Models that relevant to the business