Machine learning projects may seem similar to any software engineering endeavor, the reality is machine learning projects are onerous, demand high quality work from every person involved, and are sensitive to any tiny mistake. It seems that we cannot go five years without having some massive technology shift that becomes an essential part of our day-to-day lives. So, we will start with a proper definition of machine learning and how it is changing the way businesses analyze information. We will then continue by discussing proper ways to begin machine learning projects, including weighing the feasibility of a project, planning timelines, and the stages of the machine learning workflow once you start your project. After exploring the stages of the machine learning workflow, we will end the webinar with an example of a completed machine learning project. We will demonstrate how to create a similar project and give you the tools to create your own. What you'll learn: A deeper understanding of the end-to-end machine learning workflow. The tools needed to effectively create, design, and manage machine learning projects. The skills to define your goal, foresee issues, release models, and measure outcomes during the ML project lifecycle. Demo: Skyl Platform for End-End machine learning workflow. This is the slide deck for this webinar: https://skyl.ai/webinars/guide-end-to-end-machine-learning-projects

1. 85 Broad Street, New York, NY, 10004
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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

20. Questions?
Email us contact@skyl.ai

21. 85 Broad Street, New York, NY, 10004
+1 718 300 2104, +1 646 202 9343
contact@skyl.ai
We hope to hear from you soon
Thank you for joining!