Mlops

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Maya Andersson

Jul 1

I reviewed six "operator-ready" checklists for AI agents. None of them define the problem correctly.

#agents #llmevaluation #mlops #agentreliability

5 min read

zhongqiyue

Jun 30

Why Your AI Observability Stack Is Missing the Most Important Metric

#observability #ai #mlops #monitoring

3 min read

Jubin Soni

Jun 28

MLOps: Building a CI/CD Pipeline for ML Models on Azure Databricks

#azure #databricks #mlops #cicd

7 min read

Sahajmeet Kaur

Jun 27

What Is an Agent Registry? (And What We Broke Before We Had One)

#ai #agents #mlops #devops

7 min read

Sahajmeet Kaur

Jun 26

LiteLLM vs OpenRouter: I Used Both. Here's Where Each One Actually Broke.

#llm #ai #devops #mlops

8 min read

Saad Alkentar

Jun 25

MLOps for LLM: A Case Study on Dresscode

#mlops #llm #gemma #ai

21 min read

Renato Marinho

Jun 24

Stop building custom wrappers for your ML models.

#ai #mlops #python #productivity

4 min read

Elise Moreau

Jun 24

Using the channels-last memory format reduced the latency of our conversation backbone by 22%

#pytorch #computervision #machinelearning #mlops

4 min read

Mridul Nagpal

Jun 24

Machine learning in production: the model is the easy part

#ai #machinelearning #mlops

3 min read

Fernando Azevedo

Jun 23

ML Observability on EKS: Logs, Metrics and Tracing Head-to-Head

#dataplatforms #eks #mlops #observability

11 min read

Marcus Chen

Jun 23

Benchmarking 5 LLM providers on one eval set, no SDK per vendor

#machinelearning #llm #mlops #devops

4 min read

Jubin Soni

Jun 28

Azure Databricks for MLOps and Feature Engineering at Scale with Apache Spark, Delta Lake, and MLflow

#azure #databricks #spark #mlops

6 min read

SHIVAM UPADHYAY

Jun 23

Building a Self-Hosted MLOps Platform from Scratch with FastAPI, PostgreSQL, GCS, and Docker

#ai #devops #python #mlops

4 min read

Marcus Chen

Jun 23

temperature=0 didn't make our LLM evals reproducible

#machinelearning #llm #mlops #infrastructure

4 min read

Elise Moreau

Jun 23

The SDXL VAE overflow that decoded black images in fp16

#pytorch #computervision #machinelearning #mlops

4 min read

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DEV Community

# mlops

I reviewed six "operator-ready" checklists for AI agents. None of them define the problem correctly.

Why Your AI Observability Stack Is Missing the Most Important Metric

MLOps: Building a CI/CD Pipeline for ML Models on Azure Databricks

What Is an Agent Registry? (And What We Broke Before We Had One)

LiteLLM vs OpenRouter: I Used Both. Here's Where Each One Actually Broke.

MLOps for LLM: A Case Study on Dresscode

Stop building custom wrappers for your ML models.

Using the channels-last memory format reduced the latency of our conversation backbone by 22%

Machine learning in production: the model is the easy part

ML Observability on EKS: Logs, Metrics and Tracing Head-to-Head

Benchmarking 5 LLM providers on one eval set, no SDK per vendor

Azure Databricks for MLOps and Feature Engineering at Scale with Apache Spark, Delta Lake, and MLflow

Building a Self-Hosted MLOps Platform from Scratch with FastAPI, PostgreSQL, GCS, and Docker

temperature=0 didn't make our LLM evals reproducible

The SDXL VAE overflow that decoded black images in fp16