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Kickstarting your career in AI - Priyanka Kulkarni
- 1. November 16–20 MACHINE LEARNING, AI, AND DATA SCIENCE CONFERENCE
- 2. Kickstarting your career in AI Priyanka Kulkarni Senior Machine Learning Scientist Office Experience Organization linkedin.com/in/priyankakulkarni11/
- 3. 3 Session goals Demystify data professional roles in the industry Learn strategies to land your first job as a data professional Make you fall in love with the process of learning!
- 4. 4 What role do you want to play? Career Core Activity Core Skills Machine Learning Researcher Advance human knowledge Scientific method, math, basic programming Data Scientist Stories from data Statistics, data manipulation, communication (Applied) Machine Learning Scientist Build predictive models Machine learning algorithms, domain specific feature engineering, basic programming Machine Learning Engineer Integrate machine learning into systems Software engineering, conceptual machine learning Machine Learning Architecture / Program Manager Design solutions that leverage machine learning Software design skills, customer empathy, Strong conceptual machine learning Credits: Geoff Hulten Blog | Subscribe
- 5. 5 Evaluate : Now vs role.next() Identify the delta Evaluate Which roles energize you? aka.ms/careers is your friend! Domain : Ex. Healthcare, advertising, security Basic requirements : Ex. Coding in python/R, knowledge of statistics and basic ML algorithms Specialization : Ex. Experience with distributed processing, privacy preserving ML
- 6. Essential skills Analysis • Extraction • Wrangling • Statistical reasoning • Understand the business Imagination & Data manipulation • Pick a language • Ask questions • Use data to answer them • Data thinking vs data reporting Visualization • Translate data into consumable visuals • Take that visualization course / tutorial / book Communication • Explain your results • Numbers Implications Decisions • Use data to build trust
- 7. 7 Learningframework • Alice and Bob want to dedicate 2 hours per week with the goal to build their first deep learning model for image classification Decide on a learning budget and outcome • Bob learns best with structure and strict timelines, so they prefer a part time masters vs Alice enjoys the flexibility of learning from blogsFormal vs Informal learning • Bob identifies the following specific topics - working with image data, evaluation techniques, CNNs Backtrack - identify specific topics • Alice wants to implement their models from scratch and wished to fully comprehend the underling mathDecide - Deep vs Wide • Alice aggregates all the relevant blogs and books they plan to read and starts with the fundamentalsGather resources • Bob likes to take one class per week and dedicate 2 hours for assignments Fit them as per your learning budget • Because Alice & Bob already have! Start now!
- 8. Art of assimilating research papers Credits: Andrew Ng Assimilating research papers Read title/abstract/figures (first pass) Intro + Conclusion + figures + skim the rest (skip related works) Read the paper but skip the math. Read the whole thing but skip parts that don’t make sense 1.What did the authors try to accomplish and what were the key elements of the approach What can you use yourself ?
- 9. Art of assimilating research papers Assimilating research papers Read title/abstract/figures (first pass) Intro + Conclusion + figures + skim the rest (skip related works) Read the paper but skip the math. Read the whole thing but skip parts that don’t make sense 1.What did the authors try to accomplish and what were the key elements of the approach What can you use yourself ?
- 10. Art of assimilating research papers Assimilating research papers Read title/abstract/figures (first pass) Intro + Conclusion + figures + skim the rest (skip related works) Read the paper but skip the math. Read the whole thing but skip parts that don’t make sense 1.What did the authors try to accomplish and what were the key elements of the approach What can you use yourself ?
- 11. Art of assimilating research papers Assimilating research papers Read title/abstract/figures (first pass) Intro + Conclusion + figures + skim the rest (skip related works) Read the paper but skip the math. Read the whole thing but skip parts that don’t make sense 1.What did the authors try to accomplish and what were the key elements of the approach What can you use yourself ?
- 12. Art of assimilating research papers Assimilating research papers Read title/abstract/figures (first pass) Intro + Conclusion + figures + skim the rest (skip related works) Read the paper but skip the math. Read the whole thing but skip parts that don’t make sense 1.What did the authors try to accomplish and what were the key elements of the approach What can you use yourself ?
- 13. Keep learning by building projects Apply skills immediately • Ex: just read about pandas indexing ? Take a dummy dataset, try it out now • Following a statistics textbook? Try to solve exercise problems with code Join a competition / solve real word problem • Download that dataset and attempt modeling • Try different evaluation methods and understand when to use them • Go back to the math later (but don’t skip it!) Learn Software Engineering best practices • Gradually learn how to build scripts for your data science workflow • Keep track of your code using source control tools • Put effort in structuring your code
- 14. 14 Be a part of the AI scene Subscribe to receive regular AI updates – it’s free (mostly) and invaluable! Identify ML / Data Science groups – inside and outside Microsoft Attend at least one conference per year – and share your learnings Find a mentor!
- 15. 15 Opportunitiesfortransition Create ML opportunities within your current role/ project •Start your own ML side project & recruit Make the most of hackathons Identify Data Science roles in your current domain/ organization Roles that excite you across the industry Evaluate Domain + requirements + specialization Informational session with the hiring manager Talk to folks who already have the role you aspire towards
- 16. 16 Key learnings and insights Identify the role you’d ideally want to play Evaluate – now vs next role Upskill Identify & create opportunities Get that role!
- 17. 17 Q & A
- 18. © Copyright Microsoft Corporation. All rights reserved.
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