1.
Future of Ecommerce
How to Improve Online Shopping
Experience with Machine Learning?

2.
Technology leader with 20+ years expertise in Product
Development, Business strategy and Artificial Intelligence
acceleration. Active contributor in the New York AI
community
Extensively worked with global organizations in BFSI,
Healthcare, Insurance, Manufacturing, Retail and
Ecommerce to define and implement AI strategies
Nisha Shoukath
Co-founder,
People10 & Skyl.ai
The Speaker

3.
Extensive experience building future tech products using
Machine Learning and Artificial Intelligence.
Areas of expertise includes Deep Learning, Data Analysis,
full stack development and building world class products
in ecommerce, travel and healthcare sector.
Shruti Tanwar
Lead - Data Science
The Speaker

4.
Bikash Sharma
CTO and Co-founder at
Skyl.ai
CTO & Software Architect with 15 years of experience
working at the forefront of cutting-edge technology
leading innovative projects
Areas of expertise include Architecture design, rapid
product development, Deep Learning and Data Analysis
The Panelist

5.
Getting familiar with ‘Zoom’
All dial-in participants will be muted to enable the presenters
to speak without interruption
Questions can be submitted via Zoom Questions chat
window and will be addressed at the end during Q&A
The recording will be emailed to you after the webinar
Please familiarize yourself with the Zoom ‘Control Panel’ on your screen

6.
Live Demo of
simplifying product
catalog classification
problem using
Machine learning
How E-commerce
giants are using
Machine Learning to
drive more sales and
seamless customer
experience
Know the secret
sauce of automating
time-intensive,
repetitive steps to
quickly build models
1 2 3
...In the next 45 minutes

7.
Machine Learning automation platform for unstructured data
A quick intro about Skyl.ai
Guided Machine Learning Workflow
Build & deploy ML models faster on
unstructured data
Collaborative Data Collection & Labelling
Easy-to-use & scalable AI SaaS platform

8.
POLL #1
At what stage of Machine learning adoption your
organization is at?
⊚ Exploring - Curious about it
⊚ Planning - Creating AI/ML strategy
⊚ Experimenting - Building proof of concepts
⊚ Scaling up - Some departments are using it
⊚ In production - Using it in product features
⊚ Transforming - AI/Ml driven business

9.
How E-commerce giants use
Machine Learning to drive
revenue and improve
customer experience
01

10.
Intelligent Product Recommendations
‘Similar product’ or ‘Top
picks for you
Recommendations based
on individual buyer history
Predict customer behaviour & offer recommendations to individuals
based on their preference
Credit: Research paper Personalizing Similar Product Recommendations in Fashion E-commerce,
Rank products based on
individual preferences and
behavior to find products
that resonate more with
buyer intent
Related products aligned
with shopper’s affinity to
up sell /cross sell

11.
Visual Product Discovery
Better than text search:
‘I am looking for this shirt’
‘What is the price of this table’
enabling quick discovery of products
Shopping for your favorite celebrity look
becomes much easier
Drive more conversion by automatically showing visually similar
products of things you see around
Credit: ‘Convolutional Neural Networks for Fashion Classification and Object Detection’ paper by Brian Lao and Karthik
Jagdish

12.
Offer contextual customer service
Faster resolution of customer queries
or issues based on past user behavior
data
Enhance ongoing customer
relationship with personalized emails
Learn about past behavior & pattern of users to provide quick,
satisfactory and effective service

13.
Faster & Accurate product catalog management
Data tagging and product classification
using Machine Learning improves
⊚ Accuracy
⊚ Engagement
⊚ Trust
Ability to organizing large volume of
products
Minimize the risk of product abandonment due to incomplete or
inaccurate product details

14.
Live Demo of simplifying
Ecommerce product catalog
classification problem using
Machine learning
02

15.
8 stages of Machine Learning workflow

16.
Live Demo of simplifying
Ecommerce product catalog
classification problem using
Machine learning

17.
POLL #2
Some challenges that you are facing while
implementing AI & Machine Learning
⊚ Not started yet, so no challenges
⊚ Data collection
⊚ Data Labeling
⊚ Large volumes of data
⊚ Identifying the right data set to
train
⊚ Data Security
⊚ Lack of knowledge of ML tools
⊚ Lack of end to end platform
⊚ Lack of expertise
⊚ Choosing the right algorithms

18.
Advantages of a unified
platform Speed, Visibility,
Quality, Collaboration,
Flexibility
03

19.
Data Collection - Flexible options
(CSV bulk upload, APIs, Mobile capture, Form based…)

20.
DataSecurity- on premise solutions
(encrypted data sources, access controlled flow..)

21.
Data Labeling - Simple 4 steps process
(collaboration jobs, guided workflow…)

22.
Data Labeling - Real-time early visibility
(class balance, missing data…)

23.
Data Labeling - Early Visibility
(data frequency, data intuition, outliers, trends, labeling accuracy…)

24.
Data Labeling with Effective Collaboration
(Job allocation, trend, statistics, interactive messaging…)
Analyse trends and progress
of your data labeling job in
real time with statistics and
interactive visualizations
Manage collaborator
progress, activity, interactive
messaging

25.
Data Visualization to build strong data intuition
( visuals for data composition, data adequacy)

26.
One click training at scale
(Easy feature sets, out of the box algorithms, API integration, hyper
parameter tuning, auto scaling…)
● Train, Deploy and Version your models
by creating feature-sets in no time with
our easy feature selection provision.
● Choose from state-of-art neural
network algorithms, tune
hyperparameters and see logs for
your training in real time.
● Integrate our powerful inference API
with your application for AI-driven
actionable intelligence.
● Auto scaling of model training based on
data and hyperparameters.

27.
Model Monitoring of metrics in real-time
(inference count, execution time, accuracy…)
● Monitor your deployed
models and analyse
inference count, accuracy
and execution time.
● See how your models are
performing in real-time.
No black boxes here.

28.
● Monitor your deployed
models and analyse
inference count, accuracy
and execution time.
● See how your models are
performing in real-time. No
black boxes here.
Model Evaluation - Release Confidently
(Accuracy, Precision, Recall, F1 Score)

29.
No upfront cost in Infrastructure set up
(no DevOps needed, auto-deploy, SaaS & On-prem models…)
No DevOps
required
01
Latest tech
stack
02
On premise
and saas
models
03
Scalable
On
demand
04

30.
Skyl.ai - as ML automation platform
Efficient
Data Management
Solve your data issues; collect and manage data
efficiently
Accuracy
& Quality
Maintain accuracy and quality; train and test
faster; monitor quality
Effective
Collaboration
Collaborate and manage projects efficiently
Early
Visibility
Get early visibility; visualize and affirm correctness
on every step of the way
Scalable
High - Performance
Access on-demand and scalable, high-
performance infrastructure
Reduce
Cost
Reduce cost of implementation; do it with less
specialized resources

31.
Offers for you!
1. Personalised demo
2. 15 days free trial with data credits
3. Complimentary consultation on pilot project
4. AI Implementation Playbook
www.skyl.ai contact@skyl.ai

32.
Questions?
?

33.
We hope to hear from you soon
Thank you for joining!
85 Broad Street, New York, NY, 10004
+1 718 300 2104, +1 646 202 9343
contact@skyl.ai