See why teams choose Pecan.ai over AWS SageMaker and Databricks
AWS SageMaker and Databricks AutoML provide tools for skilled users who can manage ML complexity. Pecan, built for data analysts and business teams, delivers an end-to-end experience from business question to prediction – powered by the Pecan Agent, with no data science expertise required.
Approach
How each solution is fundamentally designed to work.
Turns business questions into predictions and actions, powered by the Pecan Agent
Model-first ML platform focused on building, training, and deploying models
Data-first platform built around large-scale data processing and ML workflows
Who it’s for
Who can successfully use the product.
Data analysts and business teams
Data scientists and ML engineers
Data engineers and ML teams
Framing the Business Question
Predictive use cases aren’t as simple as they sound. Poorly defined use cases can lead to misleading predictions.
Guided by the Pecan Agent to define the right predictive goals
Assumes you've already defined the problem clearly.
Assumes users already know what to predict and how to structure it
Picking the Right Data
Selecting the correct data is critical because your predictions rely on it.
Automatically identifies and prepares the right data from raw sources
Assumes you already know what data to use.
Requires users to select and prepare the right data
Creating the Training Set
A good training set isn't just about putting tables together—it’s easy to get this step wrong.
Start with raw data, no training dataset needed. The Pecan Agent builds a complete training set based on the predictive question, including all required aggregations and feature engineering
Requires a clean, pre-built training dataset before modeling can begin
Requires users to prepare and structure training datasets in advance
Enhancing the training set
Simply using raw data isn't enough. Additional insights significantly improve predictive accuracy.
Pecan automatically extracts behavioral patterns and key insights from the historical data, improving model's accuracy
Provides manual tools without guidance or automation
Supports feature engineering, but requires manual setup and iteration
Protecting Against ML Pitfalls
Issues like data leakage and overfitting can make models look accurate but fail in production.
Built-in safeguards maintain reliable, production-ready predictions that stay that way over time, by proactively identifying data leakage, overfitting, and data drift
User is responsible for identifying and preventing these issues
User is responsible for identifying and preventing these issues
Evaluating Your Model's Performance
Evaluating an ML model goes beyond statistical scores—you need to understand its real-world impact.
The Pecan Agent evaluates predictions and provides clear guidance with actionable insights
Offers basic evaluation metrics but leaves interpretation up to you.
Provides metrics and tooling, but interpretation is left to the user
From Prediction to Action (Operationalization)
Predictions only create value when used in workflows.
Predictions are deployed directly into business systems and workflows
Requires engineering effort to deploy and integrate models
Requires engineering effort to deploy and integrate models
Pricing
Multiple iterations may be required to reach production quality, making cost efficiency essential.
Built for cost-effective experimentation, allowing quicker iterations toward production
Can become expensive quickly with frequent iterations or large-scale projects
Flexible but can require significant infrastructure and ongoing resources
Training and support
Predictive modeling isn’t just about the tech. There is know how on how to take predictive models and drive actual impact
Dedicated success teams, with vast experience and domain expertise.
No dedicated training or support
Documentation and platform support, but no guided predictive modeling workflow
ML platforms are designed to help teams build and manage models. That means defining the problem, preparing the data, creating training sets, and handling deployment. Pecan takes care of that entire process – from raw data to predictions in your workflows, without requiring ML expertise. You start with a business question, Pecan delivers predictions you can act on.
