Einstein vision is a part of Einstein Platform services technology which provides services in the form of API to build AI-powered apps.

1. INTRODUCTION TO
EINSTEIN VISION
1

2. INTRODUCTION
TO EINSTEIN
VISION
Einstein vision is a part of Einstein Platform services
technology which provides services in the form of
API to build AI powered apps.
Using Einstein Vision, you can
▸ Leverage pre-trained classifiers
▸ Train custom classifiers
▸ Bring the power of image recognition to CRM or
other third party applications
2

3. The Einstein
Vision APIs
3
Einstein Image
Classification API
● Enables developers to train deep
learning models for image
classification and recognition
Einstein Object Detection
(Pilot) API
● Enables developers to train models to
recognize and count multiple distinct
objects within an image
● Provide granular details like the size
and location of each object.

4. WHERE CAN
YOU USE IT?
Gain new insights of your customer and leverage it
for Sales, Service and Marketing of your products or
services.
4 1. Visual Search
Visual Filters to
Find Product
Search By Product Photo

5. WHERE CAN
YOU USE IT?
5
2. Brand Detection
Understand Customer
Preferences
Monitor User-Generated
Images
Evaluate Banner
Advertisement Exposure

6. WHERE CAN
YOU USE IT?
6
3. Product Identification
Early Product Issue
Identification
Streamline Inventory
Restocking
Optimize Product Selling
Priorities

7. How Einstein
Vision work?
7

8. HOW EINSTEIN
VISION WORK?
Einstein Vision is a concept from Deep Learning,
deep learning is a branch of Machine Learning.
Machine Learning allows computers to predict
more accurately without being explicitly
programmed.
8

9. EINSTEIN VISION
FUNDAMENTALS
9

10. EINSTEIN
VISION
FUNDAMENTALS
10 Dataset Label Training
Prediction Model
It defined set of the
similar data at one
place.
It is a set of data that
need to be pass to API
of the Einstein Vision
User need to train the
data by classifying
user's dataset.
Finally the Model will
return results where the
input matches in the
dataset.
Machine learning will
construct the unique Model
on providing correctly
trained dataset.

11. Build Your Own
Einstein Vision
Application
11

12. Have you
registered for
Einstein
Platform
Services?
12 Download Private Key
(einstein_platform.pem)
Register on
api.einstein.ai/signup
Upload key on
api.einstein.ai/token
Upload Private Key
(.pem file) in the org
(Files->Upload Files)
Get TokenAdd Remote Site
Settings -
api.einstein.ai

13. 13
Steps:
▸ Create Examples
(following these
rules) from a zip file
▸ To Create a Dataset
we need to upload
the .zip file (from
Local or Cloud)
using cURL
▸ Upload a .zip file to
the location
https://api.einstein.
ai/v2/vision/dataset
s/upload
How do we
Create a
Dataset?
For Better
performance, it is
recommended that
to create a Dataset
and uploading a
.zip file.

14. Command to
Upload a .zip
file at Local
Machine
Request:
curl -X POST -H "Authorization: Bearer
358101028905754f22cf4e96ce8d9f6071ecc5a5" -H "Cache-Control:
no-cache" -H "Content-Type: multipart/form-data" -F "type=image" -F
"data=@D:/Projects/Salesforce Einstein/Electronics_SAC.zip"
https://api.einstein.ai/v2/vision/datasets/upload
Response:
{"id":1014352,"name":"Electronics_SAC","createdAt":"2017-09-27T05:54:
10.000+0000","updatedAt":"2017-09-27T05:54:10.000+0000","labelSummary
":{"labels":[]},"totalExamples":0,"available":false,"statusMsg":"UPLO
ADING","type":"image","object":"dataset"}
Get Status: Using Dataset id we get in response
curl -X GET -H "Authorization: Bearer
85c1d97ee86b43551ebdb28ed9414689a55f6e61" -H "Cache-Control:
no-cache" https://api.einstein.ai/v2/vision/datasets/1014352
14

15. Request:
curl -X POST -H "Authorization: Bearer
358101028905754f22cf4e96ce8d9f6071ecc5a5" -H "Cache-Control:
no-cache" -H "Content-Type: multipart/form-data" -F "type=image"
-F "path=http://einstein.ai/images/mountainvsbeach.zip"
https://api.einstein.ai/v2/vision/datasets/upload
Response we get and Status can be checked as similar to mentioned
in the previous slide
Command to
Upload a .zip
file in the
Cloud
15

16. Request:
curl -X POST -H "Authorization: Bearer
dbc706627fae59df668940d32c14d00170e45ac5" -H "Cache-Control:
no-cache" -H "Content-Type: multipart/form-data" -F "name=Animal
Model" -F "datasetId=1014363" https://api.einstein.ai/v2/vision/train
Response:
{"datasetId":1014363,"datasetVersionId":0,"name":"Animal
Model","status":"QUEUED","progress":0,"createdAt":"2017-09-27T08:35:1
9.000+0000","updatedAt":"2017-09-27T08:35:19.000+0000","learningRate"
:0.0,"epochs":0,"queuePosition":1,"object":"training","modelId":"TWJX
XO432WIVBNKMM7AVPGGTIA","trainParams":null,"trainStats":null,"modelTy
pe":"image"}
Get Training Status: Using Model id that we get in response
curl -X GET -H "Authorization: Bearer
dbc706627fae59df668940d32c14d00170e45ac5" -H "Cache-Control:
no-cache"
https://api.einstein.ai/v2/vision/train/TWJXXO432WIVBNKMM7AVPGGTIA
Train the
Dataset
**To Train the
dataset you need to
have 40 examples
16

17. Prediction
Using Image
File
Request:
curl -X POST -H "Authorization: Bearer
85c1d97ee86b43551ebdb28ed9414689a55f6e61" -H "Cache-Control:
no-cache" -H "Content-Type: multipart/form-data" -F
"sampleId=Photo Prediction" -F
"sampleContent=@/FileToPredict/camel.jpg" -F
"modelId=TWJXXO432WIVBNKMM7AVPGGTIA"
https://api.einstein.ai/v2/vision/predict
Response:
(Prediction:[label=Domestic Animal, probability=0.5282774],
Prediction:[label=Wild Animals, probability=0.47172257])
Application UI:
Download Code
17

18. What’s new
with
Einstein
Vision in
Winter’ 18?
18
Switch to the new API End point
Use Feedback API to Optimize Your
Model
Identify Multiple Objects with a Multi-Label
Model
Classify Images with two Prebuilt Models
Add Data to a Model with Global Datasets
Creating a Dataset Requires a Dataset
Type

19. Created By:
Jina Chetia (Founder)
Amar Kulkarni (Sr. Tech Lead)
Sukanya Banekar (Salesforce Developer)
Designed By:
Chetan More (Digital Marketer)
Nilesh Bhongale (Graphic Designer)
19

20. Advisory Services
Integrated Solution
Salesforce Automation
Lightning Development
Product Development
Salesforce Customization & Development