What are the problems with development running on local machines?
The data size and compute requirement can grow exponentially over time, especially with deep learning pipelines. Moreover, managing multiple environments, tracking all the experiments, and managing models and, pipelines become really complex with a local setup. Ultimately the training process has to land in the cloud anyway. The sooner it gets there, the better.
How is Segmind different from Google Colab?
How is Segmind different from KubeFlow?
How is Segmind different from Databricks?
How is Segmind different from AWS, GCP & Azure?
Segmind is a platform built on top of these cloud providers to help you efficiently use the power of the cloud for managing your MLOps. You can install Segmind inside any of these cloud providers to train and manage your models. Segmind maintains a list of official providers and lets you manage infrastructure across clouds.