DataTalks.Club

We talked about: Alexander’s background The role of Partner at Königsweg Being part of the data and AI community How Alexander became chair at PyData Alexander’s many talks and advice on giving them Explaining AI to managers Why being able to explain machine learning to managers is important The experimentational nature of AI and why it’s not a cure-all Innovation requires patience Convincing managers not to use AI or ML when there are better (simpler) solutions The role of MLOps in enterprises Thinking about the mid- and long-term when considering solutions Finding Alexander online Links:  Alexander's Twitter: https://twitter.com/hendorf Alexander's LinkedIn: https://www.linkedin.com/in/hendorf/ Königsweg: https://www.koenigsweg.com PyData Südwest: https://www.meetup.com/pydata-suedwest/ PyData Frankfurt: https://www.meetup.com/pydata-frankfurt/ PyConDE & PyData Berlin: https://pycon.de ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Danny’s background What an MLOps Architect does The popularity of MLOps Architect as a role Convincing an employer that you can wear many different hats Interviewing for the role of an MLOps Architect How Danny prioritizes work with data scientists Coming to WhyLabs when you’ve already got something in production vs nothing in production Market awareness regarding the importance of model monitoring How Danny (WhyLabs) chooses tools ONNX Common trends in tooling setups The most rewarding thing for Danny in ML and data science Danny’s secret for staying sane while wearing so many different hats T-shaped specialist, E-shaped specialist, and the horizontal line The importance of background for the role of an MLOps Architect Key differences for WhyLogs free vs paid Conclusion and where to find Danny online Links: Matt Turck: https://mattturck.com/data2021/ AI Observability Platform: https://whylabs.ai/observability Danny's LinkedIn: https://www.linkedin.com/in/dleybz/ Whylabs' website: https://whylabs.ai/ AI Infrastructure Alliance: https://ai-infrastructure.org/ ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: DataTalks.Club intro Tereza’s background Working as a coach Identifying the mismatches between your needs and that of a company How to avoid misalignments Considering what’s mentioned in the job description, what isn’t, and why Diversity and culture of a company Lack of a salary in the job description Way of doing research about the company where you will potentially work How to avoid a mismatch with a company other than learning from your mistakes Before data, during data, after data (a company’s data maturity level) The company’s tech stack Finding Tereza online Links:  Decoding Data Science Job Descriptions (talk): https://www.youtube.com/watch?v=WAs9vSNTza8 Talk at ConnectForward: https://www.youtube.com/watch?v=WAs9vSNTza8 Slides: https://www.slideshare.net/terezaif/decoding-data-science-job-descriptions-250687704 Talk at DataLift: https://www.youtube.com/watch?v=pCtQ0szJiLA Slides: https://www.slideshare.net/terezaif/lessons-learned-from-hiring-and-retaining-data-practitioners MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Christine’s Background Private sector vs Public sector Public policy The challenges of being a community organizer How public policy relates to political science Programs that teach data science for public policy Data science for public policy vs regular data science The importance of ethical data science in public policy How data science in social impact project differs from other projects Other resources to learn about data science for public policy Challenges with getting data in data science for public policy The problems with accessing public datasets about recycling Christine’s potential projects after Master’s degree Gender inequality in STEM fields Corporate responsibility and why organizations need social impact data scientists What you need to start making a social impact with data science 80,000 hours Other use cases for public policy data science Coffee, Ethics & AI Finding Christine online Links: Explore some Data Science for Social Good projects: http://www.dssgfellowship.org/projects/ Bi-weekly Ethics in AI Coffee Chat: https://www.meetup.com/coffee-ethics-ai/ Make a Social Impact with your Job: https://tinyurl.com/80khours Course in Data Ethics: https://ethics.fast.ai/ Data Science for Social Good Berlin: https://dssg-berlin.org/ CorrelAid: https://correlaid.org/ DataKind: https://www.datakind.org/ Christine's LinkedIn: https://www.linkedin.com/in/christinecepelak/ Christine's Twitter: https://twitter.com/CLcep  MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Olga’s career journey Hiring data scientists now vs 7 years ago The two qualities of an excellent data scientist What makes Alexey do this podcast How Alexey get the latest information on data science How Olga checks a candidate’s technical skills How to make an answer stand out (showing your depth of knowledge) A strong mathematical background vs a strong engineering background When Auto ML will replace the need to have data scientists Should data scientists transition into management? (the importance of communication in an organization) Switching from a data analyst role to a data scientist Attracting female talent in data science Changing a job description to find talent Long gaps in the CV Eierlegende Wollmilchsau Links: Olga's LinkedIn: https://www.linkedin.com/in/olgaivina/  Olga's Twitter: https://twitter.com/olgaivina MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about:  Will’s background Will’s open source projects S3Fs and PyFile systems Inspiration for open source projects Will as a freelancer Starting a company from a tweet (Rich and Textual) Building in public (Will’s approach to social media) The workforce and roadmap of Textualize.io The importance of working on open source for Textualize employees The workflow of and contributions to Textualize Getting your first thousand GitHub Stars (going viral) Suggestions for those who wish to start in the open-source space Finding Will online Links:  Twitter: https://twitter.com/willmcgugan Textualize website: https://www.textualize.io/ Textualize GitHub: https://github.com/textualize MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Lisa’s background Centralized org vs decentralized org Hybrid org (centralized/decentralized) Reporting your results in a data organization Planning in a data organization Having all the moving parts work towards the same goals Which approach Twitter follows (centralized vs decentralized) Pros and cons of a decentralized approach Pros and cons of a centralized approach Finding a common language with all the functions of an org Finding the right approach for companies that want to implement data science How many data scientists does a company need? Who do data scientists report huge findings to? The importance of partnering closely with other functions of the org The role of Product Managers in the org and across functions Who does analytics at Twitter (analysts vs data scientists) The importance of goals, objectives and key results Conflicting objectives The importance of research Finding Lisa online Links: LinkedIn: https://www.linkedin.com/in/cohenlisa/ Twitter: https://twitter.com/lisafeig Medium: https://medium.com/@lisa_cohen Lisa Cohen's YouTube videos: https://www.youtube.com/playlist?list=PLRhmnnfr2bX7-GAPHzvfUeIEt2iYCbI3w MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Merve’s background Merve’s first contributions to open source What Merve currently does at Hugging Face (Hub, Spaces) What is means to be a developer advocacy engineer at Hugging Face The best way to get open source experience (Google Summer of Code, Hacktoberfest, and sprints) The peculiarities of hiring as it relates to code contributions Best resources to learn about NLP besides Hugging Face Good first projects for NLP The most important topics in NLP right now NLP ML Engineer vs NLP Data Scientist Project recommendations and other advice to catch the eye of recruiters Merve on Twitch and her podcast Finding Merve online Merve and Mario Kart Links: Hugging Face Course: https://hf.co/course Natural Language Processing in TensorFlow: https://www.coursera.org/learn/natural-language-processing-tensorflow Github ML Poetry: https://github.com/merveenoyan/ML-poetry Tackling multiple tasks with a single visual language model: https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model Hugging Face big science/TOpp: https://huggingface.co/bigscience/T0pp Pathways Language Model (PaLM) blog: https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Misra’s background What data scientists do Consultant data scientists vs in-house data scientists (and freelancers) Expectations for data scientists The importance of keeping up to date with AI developments (FOMA) How does DALL·E 2 work and should you care? Going to conferences to stay up to date The most pressing issue for data scientists Fighting FOMA and imposter syndrome Knowing when you have enough knowledge of a framework The “best” type of data scientist Being a generalist vs a specialist Advice for entry-level data entering an oversaturated market Catching the eye of big AI companies Choosing a project for your portfolio The importance of having a Ph.D. or Master’s degree in data science Finding Misra online Links: Mısra's YouTube channel: https://www.youtube.com/channel/UCpNUYWW0kiqyh0j5Qy3aU7w Twitter: https://twitter.com/misraturp Hands-on Data Science: Complete Your First Portfolio Project: https://www.soyouwanttobeadatascientist.com/hods  MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.htm

We talked about: Adrian’s background Freelancing vs Employment Risk and occupancy rate in freelancing The scariest part of freelancing Adrian’s first projects Freelancing 5 years later Pay rates in freelancing Acquiring skills while freelancing Working with recruitment agencies and networking Looking for projects and getting clients Freelancing vs consulting Clarity in clients’ expectations (scope of work) Building your network Freelancing platforms Adrian’s data loading prototype Going from freelancing to making your own product (and other investments) The usefulness of a portfolio Introverts in freelancing Is it possible to work for 3 months a year in freelancing? Choosing projects and skill-building strategy (focusing on interests) Freelancing in Berlin Clients’ expectations for freelancers vs employees Working with more than one client at the same time Adrian’s freelance cooperative on Slack Other advice for novice freelancers (networking) Finding Adrian online Links: Github: https://github.com/scale-vector Slack Community: https://join.slack.com/t/berlindatacol-szn7050/shared_invite/zt-19dp8msp0-pP4Av3_fVFBbsdrzPROEAg MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html