DataTalks.Club

We talked about: Knesia’s background Data analytics vs data science Skills needed for data analytics and data science Benefits of getting a masters degree Useful online courses How project management background can be helpful for the career transition Which skills do PMs need to become data analysts? Going from working with spreadsheets to working with python Kaggle Productionizing machine learning models Getting experience while studying Looking for a job Gap between theory and practice Learning plan for transitioning Last tips and getting involved in projects Links: Notes prepared by Ksenia with all the info: https://www.notion.so/ksenialeg/DataTalks-Club-7597e55f476040a5921db58d48cf718f Join DataTalks.Club: https://datatalks.club/slack.html

We talked about: Demetrious’ background and starting the MLOps community Growing MLOps community Community moderations and dealing with problems Becoming a community and connecting with people Feeling belonged Managing a community as an introvert Keeping communities active Doing custdev and talking to users Random coffee and meeting with community members Organizing community activities Is community a business? Five steps for starting a community in 2021 Shameless plug from Demetrious Links: https://mlops.community/ Join DataTalks.Club: https://datatalks.club/slack.html​

We talked about: Lars’ career Doing DataOps before it existed What is DataOps Data platform Main components of the data platform and tools to implement it Books about functional programming principles Batch vs Streaming Maturity levels Building self-service tools MLOps vs DataOps Data Mesh Keeping track of transformations Lake house Links: https://www.scling.com/reading-list/ https://www.scling.com/presentations/ Join DataTalks.Club: https://datatalks.club/slack.html​​​

We talked about: Ben’s background AI evangelism Ben’s first experiences speaking in public Becoming a great speaker  Key Takeaways and Call to Action Making a good introduction Being Remembered Writing a talk proposal for conferences Landing a keynote Good topics to start talks on Pitching a solution talk to meetup organizers Top public speaking skill to acquire Book recommendations Join DataTalks.Club: https://datatalks.club/slack.html​​​

We discussed monetization roles and the capabilities people need to move into those roles. The key roles are ML Researcher, ML Architect, and ML Product Manager. We talked about: Vin's career journey What does it mean to "monetize machine learning" Important monetization metrics Who should we have on the team to make a project successful Machine Learning Researcher (applied and scientist) - background, responsibilities, and needed skills Developing new categories  The best recipe for a startup: angry users + data scientists What research actually is ML Product Manager - background, responsibilities, and needed skills How product managers can actually manage all their responsibilities (and they have a lot of them!) ML Architect - background, responsibilities, and needed skills Path to becoming an architect  How should we change education to make it more effective  Important product metrics And more!  Links: https://twitter.com/v_vashishta​ https://linkedin.com/in/vineetvashishta​ https://databyvsquared.com/​ Join DataTalks.Club: https://datatalks.club/slack.html​

We talked about:  Admond's career journey What is personal brand How Admond started being active online Publishing on medium and LinkedIn Idea generation process and tools Other platforms Podcasts Offline presence 1x1 meetings Speaking on conferences Having confidence to publish Selling online courses Personal values Admond's course And many other things Links: https://twitter.com/admond1994 https://linkedin.com/in/admond1994 https://buzzsumo.com https://feedly.com/ https://lunchclub.com/ https://thelead.io/data-scientist-personal-brand-toolkit?utm_medium=instructor&utm_source=admond Join DataTalks.Club: https://datatalks.club/slack.html

Did you know that there are 3 types different types of data scientists? A for analyst, B for builder, and C for consultant - we discuss the key differences between each one and some learning strategies you can use to become A, B, or C. We talked about: Inspirations for memes  Danny's background and career journey The ABCs of data science - the story behind the idea Data scientist type A - Analyst  Skills, responsibilities, and background for type A Transitioning from data analytics to type A data scientist (that's the path Danny took) How can we become more curious? Data scientist B - Builder  Responsibilities and background for type B Transitioning from type A to type B Most important skills for type B Why you have to learn more about cloud  Data scientist type C - consultant Skills, responsibilities, and background for type C Growing into the C type Ideal data science team Important business metrics Getting a job - easier as type A or type B? Looking for a job without experience Two approaches for job search: "apply everywhere" and "apply nowhere" Are bootcamps useful? Learning path to becoming a data scientist Danny's data apprenticeship program and "Serious SQL" course  Why SQL is the most important skill R vs Python Importance of Masters and PhD Links: Danny's profile on LinkedIn: https://linkedin.com/in/datawithdanny Danny's course: https://datawithdanny.com/ Trailer: https://www.linkedin.com/posts/datawithdanny_datascientist-data-activity-6767988552811847680-GzUK/ Technical debt paper: https://proceedings.neurips.cc/paper/2015/hash/86df7dcfd896fcaf2674f757a2463eba-Abstract.html Join DataTalks.Club: https://datatalks.club/slack.html

We talked about: How we make decisions with machine learning What is decision optimization  Specifying the decision function Emulation for making the best decisions Decision optimization and reinforcement learning Getting started with decision optimization Trends in the industry Links: https://datatalks.club/people/danbecker.html https://www.decision.ai/​ Join DataTalks.Club: https://datatalks.club/slack.html

We covered: What is a feature store Problems it solves When to use a feature store  When not to use a feature store The main components When a team should start using a feature store  Links: Feast: https://feast.dev/ https://www.tecton.ai/blog/what-is-a-feature-store/  https://docs.greatexpectations.io/en/latest/reference/core_concepts.html Join DataTalks.Club: https://datatalks.club​​​

We covered: What is MLOps The difference between MLOps and ML Engineering Getting into MLOps Kubeflow and its components, ML Platforms Learning Kubeflow DataOps  And other things Links: Microsoft MLOps maturity model: https://docs.microsoft.com/en-us/azure/architecture/example-scenario/mlops/mlops-maturity-model Google MLOps maturity levels: https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning MLOps roadmap 2020-2025: https://github.com/cdfoundation/sig-mlops/blob/master/roadmap/2020/MLOpsRoadmap2020.md Kubeflow website: https://www.kubeflow.org/ TFX Paper: https://research.google/pubs/pub46484/ Join DataTalks.Club: https://datatalks.club​​