June 15, 2022, 3:44 p.m. | Smiral Rashinkar

Towards AI - Medium pub.towardsai.net

Vendor-agnostic Setup for Running ML & DL Experiments With GPU Support

Photo by Tom Winckels on Unsplash

With many emerging solutions like AWS Sagemaker, Microsoft Azure Machine Learning Studio, Google Cloud AI Platform, etc, It can be overwhelming to choose a solution given the cost constraint and use case.

There are some pros and cons when it comes to using a cloud vendor solution:
Pros:
- A tightly integrated platform for data access, IAM, training, testing, and deploying ML & …

deep learning dl docker gpu machine learning ml ml-engineering mlops setup support vendor

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Business Data Scientist, gTech Ads

@ Google | Mexico City, CDMX, Mexico

Lead, Data Analytics Operations

@ Zocdoc | Pune, Maharashtra, India