The document outlines an event hosted by the Farmington Hills Salesforce Developer User Group featuring speakers Amit Chaudhary and Prajakta Samant discussing AI, machine learning, and Einstein platform services. Key topics include machine learning terminology, the process of model training, and best practices for creating datasets. Participants are encouraged to utilize resources and APIs available for embedding image recognition and natural language processing into applications.

1. Farmington Hills
Salesforce Developer User Group
Apex Hours
Einstein Platform Services
#SalesforceApexHours #FarmingtonHillsSFDCDug
Speaker
Date
Venue/Link
Prajakta Samant, Amit Chaudhary
Saturday, SEPT 08, 2018 10:00 AM EST ( 7:30 PM IST )
https://zoom.us/j/659759919

2. Who am I ?
Amit Chaudhary (Salesforce MVP)
• Active on Salesforce Developer Community
• Blogging at http://amitsalesforce.blogspot.in/
• Co-Organizer of – FarmingtonHillsSFDCDug
• Follow us @Amit_SFDC or @ApexHours
#SalesforceApexHours #FarmingtonHillsSFDCDug

3. Our Speaker
Prajakta Samant
(Solution Architect, Eternus Solutions)
• Twitter @prajsszz
• Partner User Group Leader for Southern and Western
India region
• Director of Pune Women Who Code
• Speaker at Salesforce User Groups and Community
events
#SalesforceApexHours #FarmingtonHillsSFDCDug

4. ▶ What is AI, Machine Learning and Deep Learning
▶ Key concepts of ML
▶ Overview of Einstein Platform Services
▶ Demo
▶ Key Takeaways
AGENDA
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Machine Learning and AI
Deep learning is a subset of machine learning methodology that works based on learning from the
data representations instead of specific algorithms

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The Terminologies
Prediction - Logical output based on a model
Model - Classification system trained using sample data
Training - Process of creating model using data set
Dataset - Collection of examples with labels
Labels - Categories to classify the examples in data set

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The process
Training Data
Machine Learning
Algorithms
ModelInput Data Prediction

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Einstein Platform Services
Image
Classification
Object Detection Sentiment
Intent
Vision Language

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DEMO

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Best Practices and Limits
▶ Train a model with large quantity examples in data set, at least 1000 distinct samples.
▶ Use variety of examples in dataset
▶ If there is change in the dataset retraining the model
▶ Free (2000 predictions/calendar month)
▶ Paid – varies by tier

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Key Takeaways
 Embed image recognition and NLP into any app
 Pre built image and language models available for use
 REST APIs available to easily train deep learning models
 REST APIs available to add feedback to a dataset

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Q & A

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Resources
▶ https://trailhead.salesforce.com/modules/einstein_intent_basics
▶ https://trailhead.salesforce.com/projects/predictive_vision_apex
▶ https://github.com/salesforceidentity/jwt
▶ https://github.com/MetaMind/apex-utils

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THANK YOU