Mikiko Bazeley
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who talks about the same things
- Joe Reis16 talks
- Ben Lorica15 talks
- Bas Geerdink13 talks
- Patrick Deziel4 talks
- Alejandro Saucedo33 talks
- Alessandra Bilardi5 talks
| title | conference | date |
|---|---|---|
| DataEngBytes 2023 - MEL-T2-01 - Mikiko Bazeley | DataEngBytes Melbourne 2023 | 2023-08-31 |
Claims that 80% of a data scientist’s time is spent on data and that 1 in 5 companies build a feature store drive the talk, backed by concrete patterns… | ||
| Panel Discussion | DataEngBytes Melbourne 2023 | 2023-08-31 |
Panelists argue that tooling is transient so prioritize the data and model life cycle over specific libraries; treat LLM deployments as ML problems with… | ||
| DataEngBytes 2023 - BRI-T1-02 - Mikiko Bazeley | DataEngBytes Brisbane 2023 | 2023-08-29 |
Argues that the full-stack data scientist remains essential because MLOps is fragmented and one-size-fits-all stacks fail in practice, with concrete patterns… | ||
| Panel Discussion | DataEngBytes Brisbane 2023 | 2023-08-29 |
Argues data engineering expands beyond pipelines into data modeling and ongoing quality work, with concrete ML pitfalls like target leakage and distribution… | ||
| DataEngBytes 2023 - SYD-T1-02 - Mikiko Bazeley | DataEngBytes Sydney 2023 | 2023-08-25 |
Argues companies should stop chasing Google scale and extend existing ML platforms to host LLM use cases using RAG and vector DBs rather than ripping… | ||
| Panel Discussion | DataEngBytes Sydney 2023 | 2023-08-25 |
Panelists argue MLOps differs from DevOps because models are probabilistic, tightly coupled to data quality and versioning, and can produce different outputs… | ||
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