Pattern®

Pattern is the biggest seller on Amazon.com besides Amazon themselves. 

We operate on Amazon, Shopify, Temu, Target.com, and many other e-commerce platforms. 

And we have 7 data science teams that cover 70+ data science use cases to help us succeed on them (advertising, market intelligence, demand forecasting, demand generation, content, operations, consulting).

DS use our internal ML Platform to self serve development and deployment of their own products end to end.

I direct an MLE and a platform team who build/maintain/evangelize the ML Platform used by DS.

Director of ML Platform, Staff Engineer

MLOps Club

Building a micro-degree to bring you from "Coder" → "Software Engineer" → "Cloud/DevOps Engineer" → "MLOps Engineer". Visit mlops-club.org!

Course Creator, Cloud & MLOps

BENlabs

Lead in the end-to-end design, planning, and implementation of company-wide AI initiatives.

Staff MLOps Engineer

The ML Platform team exists to empower data scientists to rapidly develop and own AI services end to end by making the DevOps and software aspects trivial.

I am accountable for 
• the ML Platform team meeting our quarterly and annual OKRs
• having clear ML Platform architecture and ownership/support model
• platform outages
• interviewing; writing job descriptions
• acting as an individual contributor--but as a secondary priority to managing

ML Platform Team Lead

Some projects:

• Championing a company-wide "constructs library". Like https://constructs.dev/, but just for BENlabs. 

This is a great balance of deploying infrastructure to AWS in a consistent, secure way, but still gives individuals and teams freedom to deviate for new architectures.

Sidenote: we set a goal to have all of our engineers and many data scientists become "IaC-literate". It turned out to be doable. Great boost in speed & quality. Very exciting. Exploring LocalStack to make testing of IaC faster and easier. That's been a pitfall of developing IaC.

• Championing a project, boilerplate code tool (with projen, https://github.com/projen/projen). Goal: data scientists and engineers should be able to "launch" new experiments and products in minutes instead of weeks. 

I made these projects as open-source POCs before continuing them closed-source at BEN and digging deeper. POC for creating repos with IaC: https://www.youtube.com/watch?v=7TDrysCv5xg; POC of Python-projen library https://github.com/phitoduck/phito-projen

• Doing a POC of BentoML as opposed to FastAPI, Seldon Core, etc. for model serving. Gave a meetup talk on it at Utah's MLOps.community chapter. Being active in the BentoML community. 

• Technically, I'm a co-organizer of the MLOps Utah meetup, but I need to contribute more to claim credit for that.

• I'm one of the "local experts" on Auth0, NewRelic, DNS, Pulumi, projen, AWS CDK, packaging, and BentoML.

• Supporting an MLFlow POC, mostly led by Jon Jones.

• Pushing hard for support plans: AWS Support, BentoML dedicated Slack channel, NewRelic support. 

• Seeking ways to form relationships with AI teams at "sister tech companies", maybe with a mutual-NDA. 

• Organized a hackathon with some co-workers and friends outside of work. We made a self-hostable, serverless Minecraft Platform-as-a-Service using AWS CDK. ("pip install awscdk-minecraft") https://github.com/mlops-club/awscdk-minecraft

Senior MLOps Engineer

• Write/extend tools to make DS models trivially easy to "deploy" as REST APIs and in batch jobs on AWS. 

• Lead "hand-offs" of AI products from DS to DE.

• Research/implement MLOps practices: experiment tracking, shadow deployments, catching model drift, etc

• Worked with Steve Jackson and Daniel Clark to build an important JSON:API (web service) whose purpose I probably shouldn't say here. We've continued to use the framework we established here for new AI services. Now, heavily researching observability vendors for correlated metrics, logs, and traces (which trigger alerts). 

• Established patterns for testing applications in docker. Currently developing CI framework to run a battery of tests against services *after* they have been deployed to the cloud, and then on a schedule thereafter.

• Working on a modeling project with DS. I enjoy this and the learnings should feed back into the ML platform tools.

• Did/doing heavy research into no-downtime deployments of REST APIs--particularly when the underlying transactional database schema needs to change with the REST API version.

• Write loads of infrastructure as code. Heavily researching pro's/con's of AWS CDK vs CDKtf vs Pulumi. Also, establishing standards for those.

• Wrote a CLI-tool wrapping Sphinx to generate versioned documentation for all DE/DS repos. The docs build and are published in CI. Docs can be viewed in PRs as well :)

• Extended a project template framework: DE/DS engineers can generate boilerplate Python projects, ready with CI, docs, Dockerfiles, packaging configuration, tests, semantic versioning for internal PyPI server, and other nice things. Every team should have this!

• Actively mentor.

• Actively participate in recruiting and interviewing. Went to career fairs at BYU and UVU. Holding an info session at BYU promoting BEN and offering career advice.

• Participate in our internal AI paper club. Presented on the transformer architecture.

MLOps Engineer

Elevated Analytics

We offered Salesforce analytics via SaaS--as well as mentorship and training on best practices for tracking your sales process. This is ideal for groups who have not hired a full-time analyst but want affordable, high-quality visibility into their sales funnel.

Platform Engineer, Cofounder

rootski

Building an AI startup “in the open”, using the project as a mentorship platform for people wanting to get experience in all sorts of engineering and engineering-adjacent roles.

See the explainer video at https://www.rootski.io (looks awful on mobile 🙂)

Learn about the project and how to contribute here: https://github.com/rootski-io/rootski. Hope to see you on Slack!

Mentor/founder

Karat

Conduct technical interviews on behalf of client organizations.  My goal is to make interviews an uplifting experience for the candidates－whatever the outcome.

Interview Engineer

XANT

• Wrote many data pipelines and support tools using Apache Airflow. I also "Docker-ized" Apache Airflow for our team, managed its deployment in Kubernetes, and worked with the AWS support team to resolve mysterious Airflow/Kubernetes issues.

• Researched "Infrastructure as Code", then led an initiative to standardize deployments of all new services using AWS CloudFormation templates; later leveraged that work to spin up all data engineering services in a staging environment (Terraform, CloudFormation, pretty diagrams)

• Led migration and rearchitecting of a REST API for machine learning inferences, which was consumed by our core product. I used my data science background to reduce the HTTP request latency from 2+ minutes to <20 seconds in the worst case (Flask, python, Airflow, DynamoDB)

• Led an initiative to create and standardize unit testing and CI/CD pipelines for all Python projects (Bitbucket/Bamboo, AWS Container Registry)

• Assisted in maintaining massive data ingest pipelines using Kafka and other tools to land data in S3, Snowflake, and curated relational/non-relational databases

• Made lifelong friends and awesome software with brilliant people

Data Engineer

Data Engineer Intern

InsideSales.com

Wrote code to develop data infrastructure including SQL and NoSQL databases, Kafka, RabbitMQ, Airflow, and many services within AWS

• Led trainings 2x/week for analytics team on Python, Docker, Linux, and AWS which enabled multiple team members to independently write and deploy scripts to production.

• Developed a customer churn model from Salesforce and customer usage data. Gathered data SQL and a 3rd-party JavaScript-based query language (JQL)

• Deployed Airflow on a VM to run data pipelines daily. Helped another engineer dockerize and deploy an R script with Airflow that sent alerted account managers of accounts they hadn't reached out to in over a week.

• Learned SQL and DOMO. Also learned to make BI dashboards with a lot of handholding from my manager.

Lessons, looking back years later:

(1) After I left, Airflow fell over and the analysts stopped using it. It was unrealistic to expect a team of traditional B.I. analysts to maintain such a software-engineering-oriented tool. 

Positive note: some Airflow pipelines proved valuable, and were reimplemented in simpler tools.

(2) The customer churn model was not useful so it was abandoned. I think the sad reality is most A.I. projects are not worth building, and it's easy to get distracted to complexity.

Analytics Intern

InnovAsia, Inc.

- Worked with strategy team to identify and implement software solutions that eliminate bottlenecks in 2 million parts per month global supply chain
- Developed application for systems administration of U.S. and Chinese accounts (Python, Bash)

Contract Full-stack Developer

Brigham Young University

Started a rewrite of the lab's landing page in AngularJS. Never finished, but was a valuable learning experience.

Research Assistant in IDeA Labs

Helped a few hundred beginner CS students with clean code and OOP fundamentals in C++

Teaching Assistant (Intro. to Programming)

I'm an ML Platform engineer! What is that?

Yes, Platform Engineering is rebranded DevOps. But the rebrand is trying to get back what DevOps was originally meant to be. And I'm trying to do that for MLOps and Data Science!

It's a super challenging problem because:

• SWE's speak a different language: often, the "best practices" for handling data defy the conventional wisdom in "traditional software engineering".

• Simplicity: MLOps--like DevOps--is a practice, not a person. MLOps is best practiced by the people creating inference pipelines. For DevOps, that's SWE's, but for MLOps, it's... statisticians, academics, domain experts. These are generally people with weak SWE skills. The demand for simplicity is high.

• Risk: ML/AI is about making inferences (guesses). By definition, when you're guessing, it's impossible to write tests that guarantee if your thing works before deploying. You *have* to test in prod. (they call it A/B testing).

ML platform engineering is a new field. And it's awesome.

Well friends, my current role is ending.

Been waiting for a while to hear about possibly creating a new role internally. But it's time to start looking in the meantime.

I've got zero regrets spending the last 13 months at Pattern®.

The team is awesome, and we did some cutting edge MLOps stuff like: 

- modernized the ML Platform; happy users all around
- saved 2 days/week/scientist in their work on pipelines that handle $100,000s/day
- all but eliminated ML outages by setting up data tests on EVERYTHING
- automated 100% cost tracking for all DS (compute, Snowflake, AWS... everything)
- learned how to set up company-wide data and ML lineage
- coached 2 team members to promotion; more on the way
- simplified: removed MLflow, Dockerfiles, IaC, and reams of config files from DS' lives

Looking for

- somewhere to be all in for 5+ years
- chance to gather in person at least a few times/year
- full-time remote; I'll likely move to Oklahoma in 2027
- everything ML/AI, senior/staff level

It'd be fun to

- help build data platform infra (file formats, query engines, pipelines, lineage)
- enable DS to self serve and own models in prod
- try out the kubernetes world and its blossoming OSS AI/ML ecosystem
- work with DS and DE on end-to-end projects
- go deeper on LLM evals and GenAI serving
- scale GenAI POCs (with shared ownership from the original creators so they don't dump an infeasible project and then disappear 🤣)

One thing I'll do between jobs is put some work into our end-to-end MLOps course with MLOps Club.

Thanks for reading as much of this as you did!

Things are a bit hectic so... sorry in advance if it takes me a sec to respond 😅

Eric Riddoch

Hi! My favorite area in tech is building data-powered apps. I do a lot of personal projects and focus my work experience to learn about all the buzzword aspects of that process: 

- services in the cloud
- AI models and their lifecycle
- databases, warehouses, lakes, pipelines for making data consumable
- websites, apps, etc. for users to interface with the data

Director of ML Platform @ Pattern

Daniel Jimenez

Career/family question: Been thinking a lot about remote work. It's a high-stakes question for me atm.

If anyone has experience with two working spouses and kids, I'd love your advice.

===

I got married in 2018. My wife wanted to be a doctor. I knew that if I married her, I'd be committing to support her in that. Aaaand of course, I want to work. And we want 4 kids. So... it's fair to say we've got some heavy constraints 🤣.

I've been going super hard at work for the last 8 years because I knew I'd need flexibility. Sometimes being a part-time stay-at-home dad, other times, full-time remote.

Our current plan: Move from Utah to Oklahoma to be closer to my mom when my wife's pediatrics residency ends in July 2027. Mom is excited to help with the kids :)

Oklahoma doesn't have many growth-stage tech companies, so I'll need to be remote.

Maybe there's another way to do this. Maybe I need to invest in a remote job now.

I know I need to compromise. But I wonder how I should invest my time now to make it graceful.

taplio

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