Amazon Forecast vs. Iguazio

Amazon Forecast is well suited when you are a company that's looking for a simple and effective solution in terms of understanding and predicting your resources planning in the AWS. However, it's also good to know that the cost that is incurred is higher and not suited for anything other than the AWS solutions integrations.

Incentivized

With Iguazio we are able to scale up our organisations AI infrastructure which us vital to meet business goals and accelerate time-to-time. We are also able to manage our ML pipeline end-to-end using a full-stack,user-friendly environment, feature-rich integrated feature store and powerful data transformation and real-time feature engineering capabilities.

Incentivized

• Built-in datasets
• Accuracy
• Machine learning

Incentivized

• Dynamic scaling capacity.
• Central Metadata management.
• Data ingestion and preparation.

Incentivized

• Not easy for beginners as it requires insights to set the forecast
• Much more expensive if considered for small businesses

Incentivized

• The user interface is not so much user-friendly, and easy-to-use, navigate.

Incentivized

Cost-effective and user-friendly.

Incentivized

Execution, experiment, data, model tracking, and automated deployment is done automatically through the MLRun serverless runtime engine. MLRun maintains a project hierarchy with strict membership and cross-team collaboration. End-to-end data governance is fully solidified and managed with authentication and identity management. Customers securely share data by providing access directly to it and not to copies.

Incentivized

• Helped in planning the resources required.
• ML and AI delivered near perfect forecasts.
• Features and the details available for the cost is not enough.

Incentivized

• Is a fully integrated solution with a user-friendly portal.
• Manage our ML pipeline end-to-end using Full-stack,user friendly environment.
• Iguazio enables our teams to manage all artefacts throughout their lifecycle.
• Enhance team work and collaboration in our teams.

Incentivized