Daniel Peter presented on Einstein Intent at the Farmington Hills Salesforce Developer User Group meeting. He discussed how to authenticate with and access the Einstein Intent API, how to upload intent datasets in various formats, and how to evaluate an intent model by measuring accuracy, F1 scores, and confusion matrices. He also provided considerations for preparing data like cleaning, combining, reclassifying examples and ensuring there are at least 5 examples per label. The presentation aimed to provide an overview and demo of Einstein Intent and next steps for working with the API.

1. Farmington Hills Salesforce Developer User
Group
Salesforce Apex Hours :-Einstein Intent
#SalesforceApexHours #FarmingtonHillsSFDCDug
Speaker :- Daniel Peter(Salesforce MVP), Amit Chaudhary
Date :- Saturday, OCT 28, 2017 11:00 AM EST
Venue/Link :- Online

2. Who am I ?
Amit Chaudhary
• Active on Salesforce Developer Community
• Blogging at http://amitsalesforce.blogspot.in/
• Co-Organizer of :- FarmingtonHillsSFDCDug
• Salesforce MVP

3. Our Speaker
Daniel Peter
2x Salesforce MVP
22x Salesforce Certified
Lead Applications Engineer, Kenandy
Bay Area Salesforce Developer Group
Organizer

4. Einstein Intent and the bigger picture
Einstein Platform Services
Einstein Vision
Einstein Image Classification
Einstein Object Detection (Beta)
Einstein Language (Beta)
Einstein Sentiment (Beta)
Einstein Intent (Beta)

5. Authentication to access the APIs
▶ One time (key): RSA Private Key – download
▶ Ongoing (token): OAuth 2.0 JWT bearer token flow

6. Sign up, get your RSA Private Key
▶ https://api.einstein.ai/signup

7. Use your key to get a token
https://github.com/salesforceidentity/jwt/bl
ob/master/JWT.apex

8. Easy way to get the token…
▶ https://api.einstein.ai/token

9. Intent Datasets
▶ CSV / TSV

10. ▶ JSON
Intent Datasets

11. (location) Local Upload Remote URL
(format) CSV TSV JSON CSV TSV JSON
(transaction)
Sync 1 2 3 4 5 6
Async 7 8 9 10 11 12
Uploading datasets – 12 ways!

12. DEMO

13. Evaluating the ModelEvaluating the Model
1) Overall Accuracy Percentage train and test
2) F1 for each label
3) Confusion Matrix
(wikipedia)

14. Data Prep Considerations
Might need to clean / combine / reclassify /
de-dupe your examples and labels
You need at least 5 examples per label
Duplicate examples will only be added in the
first label they appear in
Labels can only be 180 characters. They
don’t need to be words.
Examples needs to be words to produce a
decent model.

15. Thank You
Thank you for attending the session.

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