Efficient Data+AI Stack

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Do you really need a Feature Store?
Feature Store — the interface between raw data and ML models
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Yunna Wei
2
What constitutes an efficient development environment for data scientists?
The goal of building an efficient development environment for data scientists is more than assisting them to conduct PoC’s, it is also around making…
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Yunna Wei
1
MLOps Automation — CI/CD/CT for Machine Learning (ML) Pipelines
Scaling the use of AI/ML by building Continuous Integration (CI) / Continuous Delivery (CD) / Continuous Training (CT) pipelines for ML based…
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Yunna Wei
3
ML model registry — the “interface” that binds model experiments and model deployment
MLOps in Practice — A deep- dive into ML model registries, model versioning and model lifecycle management Background
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Yunna Wei
Why Data Scientists Should Adopt Machine Learning (ML) Pipelines
MLOps in Practice — as a data scientist, are you handing over a notebook or an ML pipeline to your ML engineers or DevOps engineers for the ML model to…
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Yunna Wei
MLOps in Practice — Machine Learning (ML) model deployment patterns (Part 1)
Machine Learning (ML) model serving and deployment is one of the most critical components of any solid ML solution architecture. This article…
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Yunna Wei
Build low-latency and scalable ML model prediction pipelines using Spark Structured Streaming and MLflow
MLOps in practice series — sharing design and implementation patterns of critical MLOps component.
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Yunna Wei
MLOps in Practice— Have you ever monitored your ML driven systems?
Monitoring plays a fundamental role in any solid ML solution architecture.
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Yunna Wei
Efficient Data+AI Stack
Efficient Data+AI Stack
Help organizations select and build an efficient Data+AI stack with real-world experiences and insights; Assist individuals to build their data and AI career journey with deep-dive tutorials and guidance;
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