Amazon SageMaker vs. Weights & Biases

Amazon SageMaker is a great tool for developing machine learning models that take more effort than just point-and-click type of analyses. The software works well with the other tools in the Amazon ecosystem, so if you use Amazon Web Services or are thinking about it, SageMaker would be a great addition. SageMaker is great for consumer insights, predictive analytics, and looking for gems of insight in the massive amounts of data we create. SageMaker is less suitable for analysts who do generally "small" data analyses, and "small" data analyses in today's world can be billions of records.

Incentivized

No brainer to use it when doing ML experiments as it is very easy compared to any other open source tool. You don't have to host anything like in Tensorboard.

Experiment details can be shared very easily with public using the reports

Incentivized

• Provides enough freedom for experienced data scientists and also for those who just need things done without going much deeper into building models.
• Customization and easy to alter and change.
• If you already are an Amazon user, you do not need to transition over to another software.

Incentivized

• Metrics Logging
• Hyperparmeters Sweeps
• Model Artifcats

Incentivized

• The UI can be eased up a bit for use by business analysts and non technical users
• For huge amount of data pull from legacy solutions, the platform lags a bit
• Considering ML is an emerging topic and would be used by most of the organizations in future, the pipeline integrations can be optimized

Incentivized

• Dashboard lags when we log a lot of metrics
• Improved support for matplotlib charts and documentation of wandb custom charts is not straghtforward

Incentivized

Amazon SageMaker comes with other supportive services like S3, SQS, and a vast variety of servers on EC2. It's very comfortable to manage the process and also support the end application by one click hosting option. Also, it charges on the base of what you use and how long you use it, so it becomes less costly compared to others.

Incentivized

• We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
• We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
• For our use-cases, serving models is less expensive with SageMaker than bespoke servers.

Incentivized

• Made it very easy to track experiments
• Track ML and Business Metrics improvements across experiments
• Reproduce runs which is essential in ML modelling

Incentivized