Five Essential Python Libraries for Machine Learning Engineers

This article introduces five essential Python libraries—MLflow, Streamlit, FastAPI, XGBoost, and ELI5—that every junior or intermediate machine‑learning engineer and data scientist should master to streamline experiment tracking, build interactive web apps, deploy models efficiently, achieve fast accurate predictions, and improve model interpretability.

ELI5FastAPIPython

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Five Essential Python Libraries for Machine Learning Engineers

dbaplus Community

Jan 8, 2022 · Artificial Intelligence

How Ctrip Streamlined ML Model Development and Deployment with MLOps

This article explains how Ctrip tackled the long, costly ML model development‑to‑deployment pipeline by adopting and extending MLflow for full lifecycle management, covering model persistence, tracking, serving, custom pyfunc models, Dockerized deployment, scaling, and performance monitoring.

DockerFastAPIMLOps

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How Ctrip Streamlined ML Model Development and Deployment with MLOps

Code DAO

Dec 31, 2021 · Cloud Computing

How to Run Distributed PyTorch Training on AzureML with CLI v2

This article walks through the complete workflow for building, testing, and launching a distributed PyTorch training job on AzureML using the CLI v2, covering local script preparation, Accelerate configuration, Docker environment setup, dataset registration, compute target definition, job YAML creation, and job submission with monitoring.

CLIDistributed TrainingDocker

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How to Run Distributed PyTorch Training on AzureML with CLI v2

Liulishuo Tech Team

Jun 12, 2018 · Big Data

Highlights from Spark+AI Summit 2018: Hydrogen, MLflow, Delta, Spark 2.3, and Shuffle Optimization

The 2018 Spark+AI Summit in San Francisco showcased Spark's evolution toward unified AI and big‑data processing, introducing the Hydrogen project with gang scheduling, the open‑source MLflow platform, the Delta unified analytics engine, Spark 2.3 enhancements, and Facebook's shuffle I/O optimizations.

AIDelta LakeHydrogen

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Highlights from Spark+AI Summit 2018: Hydrogen, MLflow, Delta, Spark 2.3, and Shuffle Optimization