If you want to learn more, check out our performance given by our team during the Big Data Technology Warsaw Summit: https://www.youtube.com/watch?v=gdinM_9R2GA&t=22s Operationalizing Machine Learning operations (features delivery, model training, deployment and serving) is nowadays one of the most challenging areas in fast-growing data-driven companies. Variety of open source components (Kubeflow, Mlflow, Kedro to name a few) and set of specialized managed services provided by every major cloud provider drive solution architects nuts. In GetInData we have a solution for it - we used to call it GetInData MLOps Platform. Set of reusable components, following the Unix toolset pattern ("do one thing and be best at it") and portable to any environment. Also - thanks to loose coupling - adjustable to current and future clients' ML-related challenges, like a candy shop where the first person needs super-fast online predictions, the second one requires robust hyperparameter tuning for best possible models and the third person aims for scalable collaboration on features extraction within many data science teams. During the presentation we will show you two components we're really excited for - Kedro-Kubeflow integration and Feast-based feature store - how we implement these and what clients' use them for. Welcome to our MLOps candy shop that no pandemic can close Watch our webinar here: https://www.youtube.com/watch?v=gdinM_9R2GA&t=10s Speakers: Mateusz Pytel - Google Certified Professional: https://www.linkedin.com/in/em-pe/ Mariusz Strzelecki - Senior Machine Learning Engineer: https://www.linkedin.com/in/mariusz-strzelecki/ ___ Company: Getindata is a company founded in 2014 by ex-Spotify data engineers. From day one our focus has been on Big Data projects. We bring together a group of best and most experienced experts in Poland, working with cloud and open-source Big Data technologies to help companies build scalable data architectures and implement advanced analytics over large data sets. Our experts have vast production experience in implementing Big Data projects for Polish as well as foreign companies including i.a. Spotify, Play, Truecaller, Kcell, Acast, Allegro, ING, Agora, Synerise, StepStone, iZettle and many others from the pharmaceutical, media, finance and FMCG industries. https://getindata.com​

1. Welcome to the MLOps candy shop
and choose your flavor
MARIUSZ STRZELECKI
MATEUSZ PYTEL

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● “DevOps-like” mindset, but for Data Scientists
● Tools, techniques, automation
● Goals:
○ Shorten time-to-market for ML
○ Automate boring things
○ Use the power of containers and Cloud
● Why the hell candy shop then?
What is MLOps?

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Exploration: ML Lab
● data catalog
● feature store
● variety of ML techniques and libraries
● variety of resources needed (GPU, TPU, high-RAM)

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Training: ML Gym
● pipelines
● hyperparameter tuning
● data/code versioning
● artifacts

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Serving: ML Bar
● models versioning
● a/b tests, multi-armed-bandit
● shadow testing, canary
● autoscaling

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Monitoring: ML Stock market
● logging the requests
● data quality measurement (train/predict)
● models performance

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Let’s get to the ground

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Deep dive
1. ML Lab and Feature Store
2. ML Gym and Kedro-Kubeflow

12. ML Lab: Feature Store

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What Feature Store is?
● A place to store ML models training data, structurized
● And predictions!
● A place to collaborate on how the features should be
structured
● 360° view of the domain
Feature
table 1
ML model
1
Predictions
of model 1
ML model
2
Predictions
of model 2

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Key aspects
● Fast on-line serving and efficient batch serving
● Versioning
● Historical data management
● Built-in data statistics
● API for the code, UI for the analysts

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Meet Feast

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Feast User Interface

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Use today!
https://github.com/amundsen-io/amundsendatabuilder/pull/414

18. ML Gym: kedro-kubeflow

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Kedro - ML project framework
● Common structure for all projects
● Customizable starters
● Per environment config
● Data abstraction

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My ML recipe is ready, what now?

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Kubeflow
● Scheduling and orchestration
● Containerized
● Flexible robust runtime
● Monitoring & metadata collection

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Kedro + Kubeflow
● kedro pipeline -> kubeflow pipeline
● Support for on-demand run, upload & schedule
● Deployment automation via kfp API
● Auth support (currently IAP & GCP credentials)
● MLFlow support (with kedro-mlflow plugin)
kedro-kubeflow.readthedocs.io
github.com/getindata/kedro-kubeflow

23. Summary
● 4 areas: exploration, training, serving, monitoring.
● Many flavors to choose from in each area.
● Highlights:
○ Feature discovery - FEAST & Amundsen
○ ML Framework and training runtime - Kedro & Kubeflow

24. Thank You!