Mikiko Bazeley
Head of MLOps at Featureform
Actions
Mikiko Bazeley is Head of MLOps at Featureform, a Virtual Feature Store.
She's worked as an MLOps engineer, data scientist, and data analyst for companies like Mailchimp (Intuit), Teladoc, Sunrun, Autodesk as well as a handful of early stage startups.
Mikiko leverages her knowledge and experiences as a practitioner, mentor, and strategist to contribute MLOps & production ML content through LinkedIn, Youtube, & Substack, as well as partnering with companies in the ML ecosystem like Nvidia.
Her main goals are to help:
- data scientists deploy better models faster;
- ML platform engineers develop robust & scalable ML systems & stacks without breaking the bank; &
- bring the delight back into building ML products.
Links
Sydney MLOps Panel Discussion
Hear from the greatest minds locally and internationally in the MLOps space!
MLOps Beyond LLMs
Companies & organizations know they shouldn’t build for Google but they also don’t know how NOT to build for Google scale.
The MLOps tooling ecosystem is fragmented and companies that are just starting on their journeys to becoming ML-native or ML-fluent are confused by the ML Ops maturity models that don't account for their particular organizational goals or trajectory, especially if they're not "on the road" to Google maturity.
Toss in the the emergence and (seemingly widespread) adoption of LLM’s and companies (and teams) are lost and looking for clarity on:
- How can existing ML platforms be extended to account for new uses cases involving LLMs?
- Does the team composition change? Do we now need to start hiring “prompt engineers”?
- Should we stop existing initiatives? Do we need to pivot?
My goal in this session is to help cut through the noise and cover:
- What are the main problems MLOps tries to solve?
- What does the archetypal MLOps platform look like?
- What are the most common components of an MLOps platform?
- Where do LLM based applications fit in?
Melbourne MLOps Panel
Hear from the brightest minds locally and internationally in the field of MLOps!
How The Full-Stack Data Scientist Is STILL The Sexiest Job
When MLOps first began to emerge, the promise was to increase innovation by minimizing or automating the toilsome work of data scientists.
And yet, the MLOps tooling ecosystem remains fragmented.
Companies just starting on their journeys to becoming ML-native or ML-fluent plan their roadmaps based on ill-fitting MLOps maturity models that don't account for their particular organizational goals or trajectory, while leaving data scientists out of the loop.
Toss in the the hype and ("seemingly" widespread) adoption of LLM’s and GenAI applications as well as debates about the existence of "prompt engineers" and it would seem like the data scientist role is headed for an extinction event.
My goals for this talk are to:
- Illustrate the current state of the MLOps landscape;
- Share my experiences from both sides of the fence as a former data scientist and a MLOps engineer working for companies like Autodesk, Teladoc, and Mailchimp;
- Illustrate the common pain-points by companies around production ML;
- Show how the future will favor the builders, regardless of whether they're "AI Engineers", "full-stack data scientists", or "ML Engineers".
Featurization & Feature Stores: A Crash Course In The ML Lifecycle & MLOps
DataOps, MLOps, Data Engineering.... what's the big difference?
Squint at the job descriptions and they'd seem to be the same person, especially around featurization. Can't DataOps tools be used for MLOps? Why is 80% of a data scientist's time stuck with data? Isn't a feature store just an expensive, overly specialized database where machine learning features get parked (only to be forgotten until a pipeline breaks)?
Much like how humans share 70% of their DNA with slugs (and 50% with bananas)* the differences, while minute, are significant.
My goal in this session is to help illuminate the challenges and vagaries of developing ML models from scratch (for production) and in the process answer the following questions:
- What are the main problems MlOps tries to solve?
- What does the process look like for developing a model from scratch? And why is feature engineering tricky to automate?
- What is a Feature Store? What are the pain-points a feature store is meant to solve?
- What are the different types of feature store or platforms that exist and which archetypes are seeing the most adoption? And why?
Brisbane Panel Discussion
Hear from some of the brightest data engineering minds both locally and internationally for this thrilling panel discussion!
DataEngBytes Melbourne 2023 Sessionize Event
DataEngBytes Brisbane 2023 Sessionize Event
DataEngBytes Sydney 2023 Sessionize Event
Please note that Sessionize is not responsible for the accuracy or validity of the data provided by speakers. If you suspect this profile to be fake or spam, please let us know.
Jump to top