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Future of Ecommerce: How to Improve the Online Shopping Experience Using Machine Learning?
The document discusses the future of e-commerce by leveraging machine learning to enhance online shopping experiences, featuring speakers with extensive backgrounds in AI and product development. Key topics include intelligent product recommendations, personalized customer service, and streamlined product catalog management using machine learning technologies. The presentation highlights the importance of automation in data management and provides insights into overcoming challenges faced by organizations in adopting AI and machine learning.
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Future of Ecommerce: How to Improve the Online Shopping Experience Using Machine Learning?
- 1. Future of Ecommerce Howto Improve Online Shopping Experience with Machine Learning?
- 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 of simplifyingproduct 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 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 & Labelling 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. How E-commerce giantsuse Machine Learning to drive revenue and improve customer experience 01
- 10. Intelligent Product Recommendations ‘Similarproduct’ 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 Betterthan 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 customerservice 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 & Accurateproduct 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 ofsimplifying Ecommerce product catalog classification problem using Machine learning 02
- 15. 8 stages ofMachine Learning workflow
- 16. Live Demo ofsimplifying Ecommerce product catalog classification problem using Machine learning
- 17. 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
- 18. Advantages of aunified platform Speed, Visibility, Quality, Collaboration, Flexibility 03
- 19. Data Collection -Flexible options (CSV bulk upload, APIs, Mobile capture, Form based…)
- 20. DataSecurity- on premisesolutions (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 withEffective 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 tobuild strong data intuition ( visuals for data composition, data adequacy)
- 26. One click trainingat 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 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.
- 28. ● 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)
- 29. 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
- 30. 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
- 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.
- 33. 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 improve online shopping experience with Machine Learning?. My name is Edwin and I’ll be your host today. First off, I’d like to introduce 3, AI-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 a 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 us 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 Now, 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 which is 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 guys 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
- #11 user level personalization can improve similar product recommendations. On the left hand side, we have a query product. On the right hand side, the first row shows the non-personalized similar product recommendations. The second row shows how ideal ranking will look like if the user generally likes floral dresses. And the third row shows the ranking in case of a user who has affinity towards lighter colours
- #15 How
- #17 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
- #18 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
- #19 Benefit
- #31 Now, we
- #32 Thank you Nisha and Shruti, for the wonderful presentation and demo. As mentioned earlier, the recording of the webinar will be sent to you by email 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 personalized demo as well as a 15 days free trial. 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 consultation on a pilot project of your choice and an AI implementation playbook to go along. This is a great opportunity to see how Skyl can provide Machine Learning solutions to the challenges that you or your company might have. 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 as you see on the screen
- #33 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 get to some questions. Sample questions: For Shruti 1. (Vanessa) How much is the devops effort in building a model deployment pipeline in Skyl? 2. (Christian) How can I know the fairness of a model? 3. (Katherine) Why is re-training required for ML models? Nisha: 1. (Nate) How can Skyl help me with my data labelling needs if I have data privacy issues? 2. ()If a create data collection jobs or data labeling jobs, would skyl be providing collaborators as well for those jobs? 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.
- #34 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 them as well Thank you for joining and I hope you have a wonderful day.