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How to analyze text data for AI and ML with Named Entity Recognition
The document outlines a webinar on analyzing text data for AI and machine learning using named entity recognition (NER), featuring experts from Skyl.ai with extensive experience in technology and AI strategy. Topics covered include leveraging NER for various sectors such as HR, procurement, and healthcare, along with a demonstration of customer review moderation. The Skyl.ai platform is highlighted as a solution for building and deploying machine learning models efficiently on unstructured data.
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How to analyze text data for AI and ML with Named Entity Recognition
- 1. 1 How to analyzetext data for AI and ML with Named Entity Recognition
- 2. Technology leader with20+ 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 buildingfuture 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 andCo-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 CustomerReviews 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 automationplatform 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 whatstage 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.
- 10. 80% of worldwidedata will be unstructured by 2025 - IDC Examples of unstructured data Text files Audio files Images Web pages Video files Emails
- 11. Challenges with unstructuredtext 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 : Extractphrases 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 andextract 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 informationlike 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 opinionor 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 CustomerReviews Moderation with NER 02
- 19. 8 stages ofMachine Learning workflow
- 20. Live Demo on CustomerReviews Moderation with NER
- 21. POLL #2 Some challengesthat 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 challengesthrough 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 withEffective 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 tobuild strong data intuition (visuals for data composition, data adequacy...)
- 29. One click training (Easyfeature 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 yourdeployed 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 ofmetrics 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 costin 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 - asML 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 1month Trial + POC ⊚ Complimentary 30 min consultation ⊚ AI Implementation Playbook www.skyl.ai contact@skyl.ai Special offer for you...
- 35.
- 36. 36 We hope tohear 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
Editor's Notes
- #2 Hello everyone and welcome. Thank you for joining today’s webinar on How to analyze text data, for AI and ML, with Named Entity Recognition. My name is Edwin and I’ll be your host today. First off, I’d like to introduce 3 expert speakers for today’s webinar..
- #3 First we have Nisha Shoukath - Nisha is a technology entrepreneur with background in investment banking. She’s co-founded two successful technology startups and has worked with wide variety of global organizations from different industries. She helps enterprises with defining AI strategy, and AI adoption roadmaps. Welcome, Nisha!
- #4 Next we have Shruti Tanwar - Shruti is an expert in data science who is a veteran in building SaaS products using Machine Learning and AI. Her expertise includes Deep Learning and Data Analysis, as well as full stack development and building tech products in various different fields such as ecommerce, travel, and healthcare. Welcome, Shruti!
- #5 Finally, we have Bikash Sharma joining today. Bikash is CTO and Software Architect with 15 years of experience in leading innovative software projects and solutions. He’s co-founded Skyl with his expert knowledge in AI and Machine Learning. Welcome, Bikash!
- #6 Before we begin, I’d like to briefly talk about some relevant Zoom features. All participants in the webinar will be muted to avoid any interruptions during the session. Any questions you might have can be submitted to the Zoom Questions chat window in the control panel, located on the bottom of the screen. We’ll make sure to address your questions during the Q&A session. Also, the recording of the webinar will be emailed to you afterwards, just in case you’ve missed any talking points or wish to view it again. So that’s all for the introduction - now we’ll get started with the webinar and I’ll hand over the session to Nisha
- #9 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
- #10 NER also known as entity chunking and entity identification is a process where an algorithm takes a string of text (sentence or paragraph) as input and identifies relevant nouns (people, places, and organizations) that are mentioned in that string.
- #14 In procurement, both the buyers and vendors have to ensure that the documentations remain consistent in the transaction. The contents of purchase orders, invoices, and order receipt notes, etc. have to match.
- #15 In procurement, both the buyers and vendors have to ensure that the documentations remain consistent in the transaction. The contents of purchase orders, invoices, and order receipt notes, etc. have to match.
- #16 In procurement, both the buyers and vendors have to ensure that the documentations remain consistent in the transaction. The contents of purchase orders, invoices, and order receipt notes, etc. have to match.
- #17 1.Mining these biological associations from literature can provide immense support to research ranging from drug-targetable pathways to biomarker discovery. However, time and cost of manual curation heavily slows it down Through NER, certain hidden information in the diagnosis could be dug out and further contribute to improving existing medical systems. More importantly, medical information processing systems that rely solely on structured data are unable to directly access such kinds of hidden information in the medical text.
- #18 In procurement, both the buyers and vendors have to ensure that the documentations remain consistent in the transaction. The contents of purchase orders, invoices, and order receipt notes, etc. have to match.
- #19 How
- #21 5 minutes intro - 10 industry awareness - 15 min demo - 20 minutes QnA Define problem - Features model - How this model is built using skyl.ai Add slide of Pneumonia detection
- #22 Not started yet, so no challenges Data collection Data Labeling Data Bias Large volumes of data Identifying the right data set to train Lack of knowledge of ML tools Lack of end to end platform Lack of expertise Choosing the right algorithms Monitoring the model performance
- #23 Benefit
- #28 On-prem solutions For industries, where business depends upon sensitive data, Skyl provides the provision of on-prem solutions. Your data stays in your own servers, and in your own databases, giving you complete control over your data. Access controlled flow Defined and controlled access flows with different organizational roles like business owner, project lead, collaborators etc. allow for 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.
- #34 Now, we
- #35 Thank you Nisha and Shruti, for the wonderful presentation and demo. As mentioned earlier, the recording of the webinar will be emailed to you afterwards. [pause] Before we get to the Q&A, I want to mention some of the offers Skyl has for those of you that are curious about incorporating Machine Learning to your business. Skyl offers a free 1 month trial, plus Proof of Concept. You’ll be able to interact with real data on the screen, just like we showed in the demo. You’ll experience the process of going from collecting & labeling the data… all the way to deploying a model! Skyl also offers a complimentary 30 min consultation and an AI Implementation Playbook to go along. This is a great opportunity to see how Skyl can provide Machine Learning solutions to your challenges. If you’re interested in finding out more, please visit the skyl.ai website or you can send an email directly to contact@skyl.ai.
- #36 Alright, now it’s Q&A time! As a reminder, if you have any questions, go to the question box in your control panel - located on the bottom of your Zoom screen. We’ll try to answer as many questions as possible in the time that we have left. So let’s answer some questions. Sample questions: Shruti - (Anonymous) Regarding text, should it be written text or can it be scanned copies and the algorithm will identify the words in the scanned item? -(Julie) How much is the devops effort in building a model deployment pipeline in Skyl? - (Aaron) How can I keep track of my model’s performance and fairness? Nisha -(Vikas) Do you have in-prem facility? -(anonymous) What about data pre-processing? -(Alem) Apart from text, can I use Skyl for image based data, like screenshots, to build model for my customer center?? Ok, that’s all the time we have for questions today, but feel free to contact us with your specific questions and we’ll make sure to get them answered.
- #37 All right, so we have reached the end of the webinar. We hope you enjoyed it. We have a lot more webinars coming up on different machine learning topics and how they can be implemented into different businesses and industries, So don’t miss out and make sure you sign up for upcoming webinars as well Thank you for joining and I hope you have a wonderful day.