Guide to end end machine learning projects
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Skyl.ai is an end-to-end machine learning platform that simplifies building and deploying ML models, focusing on unstructured data and collaborative workflows. The document outlines key aspects of machine learning projects, including the importance of defining business outcomes, ethical considerations, and the structured lifecycle of ML projects. It also highlights the platform's benefits, such as eliminating upfront costs and providing a unified AI experience from data collection to model monitoring.
Guide to end end machine learning projects
- 1. 85 Broad Street, New York, NY, 10004 +1 718 300 2104, +1 646 202 9343 [email protected] Confidently build AI capabilities in software and services Guide to end-to-end Machine Learning Projects
- 2. About Skyl Skyl is an End-to-end Machine Learning platform Build & deploy ML models faster on unstructured data Guided machine learning workflow Collaborative Data Collection and Labelling Easy-to-use & scalable AI SaaS platform.
- 3. The Speaker ● Proven experience in defining technology vision, designing product architecture with expertise in developing Artificial Intelligence and Machine Learning solutions ● Specialized in technology innovation, rapid product development and delivering solutions across geographically dispersed teams for Healthcare, Education, Retail and Media industries Bikash Sharma CTO, Skyl.ai @bikashsharmabks
- 4. The Panelist ● Technology leader specialized in defining AI/ML strategy for organizations with a prime focus on delivering business impact ● More than 2 decades of experience in developing B2B and B2C (Web / Mobile, Cloud / Analytics/Machine Learning and Artificial Intelligence) products in Telecom, Banking, Education, Media and Healthcare industries world-wide Nisha Shoukath COO, Skyl.ai @nishashoukath
- 5. GoToWebinar… get familiar ● All dial-in participants will be muted to enable the presenters to speak without interruption ● Questions can be submitted via GoToWebinar Questions chat window and will be addressed at the end ● The webinar recording will be emailed to you after the webinar
- 6. ...In the next 45 minutes ● Brief around Machine Learning ● Building a successful machine learning project ● The end-to-end machine learning workflow ● Machine learning project life cycle ● Technology stack ● A Demo: Skyl Platform for End-End machine Learning workflow
- 7. Get started
- 8. Machine Learning Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" with data, without being explicitly programmed.
- 9. Machine Learning = Disruptive Technology Machine learning changes the way we think about a problem and fundamentally changes the way we solve it. Adoption of AI/ML will allow us to: ● Reduces the time of programming. ● Scale your product and services. ● Solves problems which are unprogrammable
- 10. Building Successful Machine Learning Project
- 11. Things to consider before you start ● AI is experimental in nature ● Treat Data as your source-code not just algorithm. ● Machine Learning is all about continuous learning and iteration. ● Right technology choice which take experiments to production. ● Build team with right skills.
- 12. Feasibility of a ML project Cost of data acquisition Cost of wrong predictions Computational resources available for training and inference
- 13. Defining your AI Project: Defining business outcome (is it scaling your business and by what %?) AI goal (automate the process of vehicle insurance claim approval) AI ethics (avoid bias and being fair to all customers )
- 14. 8 stages of Machine Learning workflow
- 15. Machine Learning project release cycle Ver 1.X Model Exploration objective (1-2 weeks) ● Prove the hypothesis and build confident data intuition ● Define a limited scope ● Determine the implementation approach for machine learning ● Understand the data quality and quantity required to refine the model ● This model may not be ready for production Ver 2.X Model Refinement objective (2-3 weeks) ● Focus on creating right balanced model metrics - accuracy, recall, precision, etc. ● Aim to attaining high fairness ● Increase the scope of a model ● Attain a business outcome ● Candidate for production Ver 3.X Model Maintenance objective (4-6 weeks) ● Keep the model up to date with the highest fairness ● Attain business outcome ● Retrain the model to accommodate any additional training data points
- 16. Technology Stack
- 17. Skyl Platform Key Benefits ● Unified AI Platform from Data Collect, Labeling to Model training, deployment to Monitoring. ● Guided ML workflow makes it easy even for BAs/PMs to start ML experimentation. ● No infrastructure setup required aka no upfront cost. ● Complete visibility at all stages. ● Allows you to take your experiments to production in no time with scale ● Faster model release iteration cycles.
- 18. Demo
- 19. Try out 15 days free trial Register https://skyl.ai/form?p=start-trial
- 21. 85 Broad Street, New York, NY, 10004 +1 718 300 2104, +1 646 202 9343 [email protected] We hope to hear from you soon Thank you for joining!