Whenever Kris and I chat, there's an agenda, which is totally useless. Every single time we've talked, the conversation goes into different (I'll argue better) directions. In this episode, Kris and I delve into the art and craft of programming, finding your tribe as a developer advocate, and so much more. I hope you enjoy this great and meandering conversation.

Developer Voices podcast: https://open.spotify.com/show/2gXhwz0AQRv2cvw61kobE5

Kris's LinkedIn: https://www.linkedin.com/in/krisjenkins/

Kris's Twitter: https://twitter.com/krisajenkins

If you like this show, give it a 5-star rating on your favorite podcast platform.

Purchase Fundamentals of Data Engineering at your favorite bookseller.

Subscribe to my Substack: https://joereis.substack.com/

We talked about;

Antonis' background The pros and cons of working for a startup Useful skills for working at a startup and the Lean way to work How Antonis joined the DataTalks.Club community Suggestions for students joining the MLOps course Antonis contributing to Evidently AI How Antonis started freelancing Getting your first clients on Upwork Pricing your work as a freelancer The process after getting approved by a client Wearing many hats as a freelancer and while working at a startup Other suggestions for getting clients as a freelancer Antonis' thoughts on the Data Engineering course Antonis' resource recommendations

Links:

Lean Startup by Eric Ries: https://theleanstartup.com/ Lean Analytics: https://leananalyticsbook.com/ Designing Machine Learning Systems by Chip Huyen: https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/ Kafka Streaming with python by Khris Jenkins tutorial video: https://youtu.be/jItIQ-UvFI4

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about:

CJ’s background Evolutionary biology Learning machine learning Learning on the job and being honest with what you don’t know Convincing that you will be useful CJ’s first interview Transitioning to industry Tailoring your CV Data science courses Moving to Berlin Being selective vs ‘spray and pray’ Moving on to new jobs Plan for transitioning to industry Requirements for getting hired Publications, portfolios and pet projects Adjusting to industry Bad habits from academia Topics with long-term value CJ’s textbook

Links:

CJ's LinkedIn: https://www.linkedin.com/in/christina-jenkins/ Positions for master students: one two

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

Highlights  In Part 3 of the music "trigger cities" mini-series, we explore the music tastes of Mexico City, São Paulo, Buenos Aires, Rio de Janiero, Bogotá, Lima and Santiago.Mission   Good morning, it’s Jason here at Chartmetric with your 3-minute Data Dump where we upload charts, artists and playlists into your brain so you can stay up on the latest in the music data world.We’re on the socials at “chartmetric”, that’s Chartmetric, no “S ”- follow us on Instagram, Twitter, Facebook or LinkedIn, and talk to us! We’d love to hear from you.DateThis is your Data Dump for Wednesday, July 17th, 2019.Latin America "Trigger" CitiesIn case you missed them, we have been working on a written mini-series called “trigger cities”, it’s a concept that Chartmetric’s Partner and Advisor, Chaz Jenkins, an international marketing guru coined many years ago.It’s the idea that in the streaming environment, our algorithms on YouTube, Spotify and all platforms are connected with the tastes of huge cities around the world who also love the same apps.Lauv, the uber-successful independent artist first saw playlist success with his 2017 hit “I Like Me Better” in Southeast Asia! Lauv...is not Asian, but SE Asians adore great pop love songs.Reggaeton from the likes of huge superstars like Colombia’s J Balvin and Puerto Rico’s Bad Bunny are now on top playlists like Spotify’s Today’s Top Hits, a primarily English-language playlist...but their come-up was based on Latin American listeners supporting them more than any other region.So in the interest of knowing what the local markets are like, we wrote about  seven different metropolitan areas in Latin America: Mexico City, São Paulo, Buenos Aires, Rio de Janiero, Bogotá, Lima and Santiago.Five speak Spanish, two speak Brazilian Portuguese, and all love the YouTube.It’s a known fact that Latin America turns to the Google platform more than anything else to listen to music, and the numbers are quite impressive: Bogotá, despite having less than half (10.7M) of Mexico City’s population, took the #1 spot in YouTube views in one week last month with 26.5M views across 1.6M+ artists. The Mexican capital, however, was not far behind with 24.8M, and the two cities seem to be leading YouTube’s consumption in the region, with Lima a distant #3 with 17.1M views.On Spotify, Mexico City-as Spotify’s proclaimed “World’s Music-Streaming Mecca”-took the top spot in the same week with 2.3B non-unique monthly listeners (and this is admittedly odd metric, check the show notes for a link to the explanation), far outstripping Santiago in the #2 spot with 1.5B non-unique monthly listeners (MLs).When it comes to genres, we compiled genre tags on Shazam chart occurrences in these seven cities and found what sounds each city was most curious about when they flipped out their phones.“Urbano latino”-which is primarily reggaeton and Latin trap and the most popular in Santiago, Lima and Bogotá-didn’t show up at all in Brazil, with Brazilian-native genres such as “Sertanejo” (Brazilian country music) asserting their unique identity in the region, with Pop/Rock/Dance all showing strongly in the past month for both cities.This is contrary to the idea that all of Latin America loves reggaeton...just not true.On Instagram, who do you think are the ten most followed artists in the region?Well there’s Selena Gomez, Justin Bieber, Ariana Grande and Beyoncé…...there’s also Maluma and Daddy Yankee...But do you know pop queen Anitta, local icon Ivete Sangalo, comedian-entertainer Whindersson Nunes or the Beyoncé-inspired Ludmilla? They’re all Brazilian, showing how much Brazilians love IG, and also how much they love their own country’s artists.So there’s a taste of Part 3 of our trigger cities mini-series, please do check it out on Medium or LinkedIn and let us know what you think! If you’re into Southeast Asia, we wrote about that too (Medium or LinkedIn). We hope they’re useful insights as you target social media campaigns, forge international collaborations or plan out a tour!Outro That’s it for your Daily Data Dump for Wednesday, July 17th 2019. This is Jason from Chartmetric.Free accounts are at chartmetric.comAnd article links and show notes are at: podcast.chartmetric.comHappy Wednesday, and we’ll see you Friday! 

Summary Machine learning is a class of technologies that promise to revolutionize business. Unfortunately, it can be difficult to identify and execute on ways that it can be used in large companies. Kevin Dewalt founded Prolego to help Fortune 500 companies build, launch, and maintain their first machine learning projects so that they can remain competitive in our landscape of constant change. In this episode he discusses why machine learning projects require a new set of capabilities, how to build a team from internal and external candidates, and how an example project progressed through each phase of maturity. This was a great conversation for anyone who wants to understand the benefits and tradeoffs of machine learning for their own projects and how to put it into practice.

Introduction

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Kevin Dewalt about his experiences at Prolego, building machine learning projects for Fortune 500 companies

Interview

Introduction How did you get involved in the area of data management? For the benefit of software engineers and team leaders who are new to machine learning, can you briefly describe what machine learning is and why is it relevant to them? What is your primary mission at Prolego and how did you identify, execute on, and establish a presence in your particular market?

How much of your sales process is spent on educating your clients about what AI or ML are and the benefits that these technologies can provide?

What have you found to be the technical skills and capacity necessary for being successful in building and deploying a machine learning project?

When engaging with a client, what have you found to be the most common areas of technical capacity or knowledge that are needed?

Everyone talks about a talent shortage in machine learning. Can you suggest a recruiting or skills development process for companies which need to build out their data engineering practice? What challenges will teams typically encounter when creating an efficient working relationship between data scientists and data engineers? Can you briefly describe a successful project of developing a first ML model and putting it into production?

What is the breakdown of how much time was spent on different activities such as data wrangling, model development, and data engineering pipeline development? When releasing to production, can you share the types of metrics that you track to ensure the health and proper functioning of the models? What does a deployable artifact for a machine learning/deep learning application look like?

What basic technology stack is necessary for putting the first ML models into production?

How does the build vs. buy debate break down in this space and what products do you typically recommend to your clients?

What are the major risks associated with deploying ML models and how can a team mitigate them? Suppose a software engineer wants to break into ML. What data engineering skills would you suggest they learn? How should they position themselves for the right opportunity?

Contact Info

Email: Kevin Dewalt [email protected] and Russ Rands [email protected] Connect on LinkedIn: Kevin Dewalt and Russ Rands Twitter: @kevindewalt

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Prolego Download our book: Become an AI Company in 90 Days Google Rules Of ML AI Winter Machine Learning Supervised Learning O’Reilly Strata Conference GE Rebranding Commercials Jez Humble: Stop Hiring Devops Experts (And Start Growing Them) SQL ORM Django RoR Tensorflow PyTorch Keras Data Engineering Podcast Episode About Data Teams DevOps For Data Teams – DevOps Days Boston Presentation by Tobias Jupyter Notebook Data Engineering Podcast: Notebooks at Netflix Pandas

Podcast Interview

Joel Grus

JupyterCon Presentation Data Science From Scratch

Expensify Airflow

James Meickle Interview

Git Jenkins Continuous Integration Practical Deep Learning For Coders Course by Jeremy Howard Data Carpentry

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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In this episode, our guest is Dan Kahan about his research into how people consume and interpret science news. In an era of fake news, motivated reasoning, and alternative facts, important questions need to be asked about how people understand new information. Dan is a member of the Cultural Cognition Project at Yale University, a group of scholars interested in studying how cultural values shape public risk perceptions and related policy beliefs. In a paper titled Cultural cognition of scientific consensus, Dan and co-authors Hank Jenkins‐Smith and Donald Braman discuss the "cultural cognition of risk" and establish experimentally that individuals tend to update their beliefs about scientific information through a context of their pre-existing cultural beliefs. In this way, topics such as climate change, nuclear power, and conceal-carry handgun permits often result in people. The findings of this and other studies tell us that on topics such as these, even when people are given proper information about a scientific consensus, individuals still interpret those results through the lens of their pre-existing cultural beliefs. The 'cultural cognition of risk' refers to the tendency of individuals to form risk perceptions that are congenial to their values. The study presents both correlational and experimental evidence confirming that cultural cognition shapes individuals' beliefs about the existence of scientific consensus, and the process by which they form such beliefs, relating to climate change, the disposal of nuclear wastes, and the effect of permitting concealed possession of handguns. The implications of this dynamic for science communication and public policy‐making are discussed.