Mlearning.ai

AUGUST 28, 2023

There are six main files/folders that need to be modified with your resource names, data files, python script files, and yml files defining the virtual machine environment: config-infra-prod.yml: this file contains the name of the service principle name. Resources include the: Resource group, Azure ML studio, Azure Compute Cluster.

Azure 52

Azure ML ML Data Science 52

Heartbeat

JANUARY 18, 2024

The function makes API calls using the Replicate library and manages time intervals using the time module. env file, add the Replicate token and model endpoints in the following format: # ?.env The function accepts several API call-related parameters, including model, prompt, maximum length, temperature, and API token.

Python 52

Python Deep Learning Deep Learning Natural Language Processing 52

Towards AI

AUGUST 17, 2023

Kustomize is used for managing configuration differences across environments. ├── infra │ ├── charts/ └── overlays ├── dev │ ├── patch-image.yaml └── production ├── patch-image.yaml └── patch-replicas.yaml Jenkins is used to continuously build new images in the development environment.

AWS 98

AWS Clustering AI AI 98

AWS Machine Learning Blog

NOVEMBER 9, 2023

The repo has the following folder structure: /environments – Configuration script for prod environment /mlops-infra – Code for deploying AWS services using Terraform code /pipelines – Code for SageMaker pipeline components Jenkinsfile – Script to deploy through Jenkins CI/CD pipeline setup.py – Needed to install the required Python modules and create (..)

AWS 102

AWS ML ML Machine Learning 102