On-Prem vs Public Cloud for ML

1. 1Evaluate your ML workflow to determine the best infrastructure fit
2. 2Compare the costs of on-prem vs public cloud for training and inference
3. 3Assess the scalability and flexibility needs of your ML project
4. 4Consider the security and compliance requirements for your ML data
5. 5Test and benchmark your ML models on both on-prem and public cloud infrastructure

Data scientists and engineers can benefit from understanding the tradeoffs between on-prem and public cloud for ML, to make informed decisions about their infrastructure

💡 On-prem and public cloud have different tradeoffs for ML training and inference, and the best choice depends on your specific workflow and requirements

💡 Weigh the pros and cons of on-prem vs public cloud for #MachineLearning at scale