1. 1
How to analyze text data for AI
and ML with Named Entity
Recognition

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 on
Customer Reviews
Moderation using
NER
How organizations
are leveraging
Named Entity
Recognition
How to quickly
overcome the
challenges in
building ML 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 & Labeling
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 organizations are
leveraging NER01

10. 80% of worldwide data will
be unstructured by 2025
- IDC
Examples of unstructured data
Text files Audio files
Images Web pages
Video files Emails

11. Challenges with unstructured text data
⊚ Large Archives or records of data
⊚ Extracting hidden information needs manual efforts
⊚ Traditional rule based system can’t keep up with new changes

12. NER : Extract phrases in text that refer to real-world entity
Eg: Kimberley will be traveling to New York on Thursday
People - Kimberley
Place - New York
Time - Thursday
‘Named Entity Recognition’ to the rescue!

13. ⊚ Identify and extract relevant
information like aggressive
clauses, legal anomalies, future
financial obligations, renewal or
expiration dates, and even
summarise contract data down
to concise points.
Legal - Contract Analysis
Contract Title
Start Date
Contracting
Parties

14. ⊚ Extract skills, education, and
experience details of candidate
resumes/CVs
⊚ Check the extracted information
with the criteria of job description
and list preferable candidates
accordingly
⊚ Removes subconscious bias
HR and Recruitment - Profile Evaluation

15. ⊚ Extract information like delivery
address, vendor names, product
details, quantity, and pricing from
these documents.
⊚ Using the extracted data, AI can
match PO’s with their Invoices and
ORN’s, maintaining transaction
consistency.
Manufacturing - Procurement Matching
(Invoices, order receipt notes,…)

16. Biomedical - Research & Analysis
⊚ Understanding the correlation
between drugs and diseases, genes
and diseases etc.
⊚ Drug Discovery
⊚ Extraction of disease from
electronic health records

17. ⊚ Extract opinion or related
product mentions which may help
the seller and consumer to analyze
from 100s product review into
meaningful review mentions and
derive business actions.
Ecommerce - Customer Review Moderation
Review for Canon EOS 6D Mark II
26.2MP Digital SLR Camera

18. Live Demo on
Customer Reviews
Moderation with NER
02

19. 8 stages of Machine Learning workflow

20. Live Demo on
Customer Reviews
Moderation with NER

21. 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

22. Overcoming the AI/ML
challenges through Skyl.ai
Platform03

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

24. Data Labeling - Simple 4 steps process
(guided workflows, collaboration jobs,…)

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

26. Data Labeling with Effective Collaboration
(Job allocation, trends, 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

27. ⊚ On-prem solutions - Data stays in your own
servers, and in your own databases, giving you complete
control over your data.
⊚ Controlled access flow - Defined and controlled
access flow allows selective restriction so that you have full
command to regulate who can view or use resources in
your ML projects.
⊚ Encrypted data sources - All data sources are
encrypted in Skyl thus giving users an additional layer of
security, making sure your data stays safe and protected.
DataSecurity- on premise solutions
(encrypted data sources, access controlled flow..)

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

29. One click training
(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 powerful inference API with your
application for AI-driven actionable intelligence.
⊚ Auto scaling of model training based on
data and hyperparameters.

30. ⊚ 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)

31. 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.

32. 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

33. 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

34. ⊚ Free 1 month Trial + POC
⊚ Complimentary 30 min consultation
⊚ AI Implementation Playbook
www.skyl.ai contact@skyl.ai
Special offer for you...

35. Questions?
?

36. 36
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