Making MLOps Real

We've got another feel good story about someone getting a job from the community. This one is near and dear to my heart because as some of you know I hva recently become an angel investor in ZenML. Our story today is how ZenML was able to hire Alex Strick van Linschoten as their newest ML Engineer.

Alex doesn't come from a technical background. Instead, he completely changed his career from working as a historian and researcher. His academic background was originally in languages (Arabic and Persian) but then he ended up living in Afghanistan for 10 years, writing books there and even getting his PhD in War Studies from King's College London.

Throughout all this, Alex retained an interest in projects that required complex data analysis and this was part of his impetus to switch careers to something more technical. He retrained himself as a web engineer over a period of three years following Launch School's intensive back- and front-end programme, but was happy to get connected to ZenML through the Community Slack.

Alex mentioned to me, "I saw the post here in the 'jobs' section, and it seemed interesting so I reached out saying something like 'hey, I like what you're doing. I don't think I'm experienced enough for what you need, but I'm really interested in MLOps and I can learn what you need me to learn. can I interview with you?'" Bam! that's all it takes sometimes. Don't be afraid to put yourself out there!

"Filling in dozens of automated applications on LinkedIn is so impersonal," Remarked Alex. "Getting to interact with the real people behind the application process on Slack was a really pleasant shift, and I'm super happy with how it all worked out in the end!"

This week, we had a special success story come to us from Brazil. Matheus Frata and Breno Costa joined us to talk about some of the lessons from a very successful MLOps scale up at the Brazilian data insights company Neoway.

I spend a lot of time consuming MLOps content. With this in mind, the depth, clarity, and insights offered by Matheus and Breno (along with a forthcoming blog post they are authoring) really blew me away. They are a shining example of what our friend Jacopo Tagliabue calls “MLOps at Reasonable Scale”.

The most impressive part of this whole conversation was learning from Breno and Matheus about the thoughtful organizational design Neoway put in place around machine learning. It’s honestly one of the best, most thorough explanations around how to structure and interface platform and consumer teams we’ve had on the podcast. Furthermore, Neoway has a coherent technical MLOps platform that accelerates through adoption of the Google Cloud ecosystem. Matheus and Breno took us through how and why they shifted from a legacy architecture to newer tools. Underpinning this entire conversation was a strong fundamental theme of viewing data as a core strategic business asset.

Neoway depends on effectively translation data into insights, which pushes technical teams to be outcomes-oriented and rigorous about the structure and tools they adopt to produce such outcomes. I’d argue that not all businesses have reached Neoway’s level of strategic clarity about the importance of data. If you feel similarly about your organization, I highly recommend listening to this convo to understand tactical ways you can advance your company’s MLOps and data platform efforts across cultural, technical, and organizational fronts.
- Vishnu

Our guest this week Andy McMahon is prolific to say the least. Apart from writing an incredible book "ML Engineering with Python", he has a youtube channel, podcast, and can be seen in various conferences giving talks. Did I mention he has a full time job as a MLE at Natwest group?

In this session, we reflect on many of the learnings Andy has gained through the past few years working in successful data science and machine learning engineering teams building operational products that create millions of dollars of value.

In particular, Andy discusses how he thinks we can 'bootstrap' ML Engineering (MLEng) and MLOps practices in your organization.

In detail Andy will cover topics like:

Where do you start? How do you know what to go after first when it comes to operationalizing your ML models?
What comes first - process or tooling? If you're trying to make ML Engineering and MLOps real, do you need to work out good processes first or do you need to invest in the right tools first?
Who do I need? If you're building out an MLEng/MLOps team, what mix of skills and capabilities should I have? How should I organize them?
How do we do it? On the ground, what do we need to bear in mind as we develop our solutions? How do we set ourselves up for success?

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