True AutoML

Dec 11, 2019 10:18:36 AM / by Aible

 

The trouble with AutoML today is that it’s not very automated. Sometimes, not even close. As a result, business users, data scientists and developers waste an incredible amount of time waiting for AI to happen, rather than collaborating on AI that results in what everyone wants – business-changing impact.

 

Aible is the only AutoML AI platform that fully automates the entire end-to-end machine learning life cycle, from requirement gathering to monitoring.
Aible’s AutoML consists of 10 essential steps – most so-called AutoML solutions cover just a handful of these steps, and then only partially.

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  1. Requirement Gathering: Aible automatically asks business-relevant questions to understand your unique cost-benefit tradeoffs and operational constraints. Most AutoML vendors charge extra consulting fees for this function.

  2. Use Case Blueprints: Aible AutoML is powered by blueprints, which bring together the best in data science, domain expertise and modeling optimized for business impact. Blueprints help you get started by recommending and calculating variables that are typically good predictors for your specific use case. Typically, with other AutoML offerings, the term blueprints is used for analysis settings, not domain knowledge.

  3. Data Recipe: Connects to enterprise applications and data sources to access training data (recommended by a blueprint). While some offerings enable connecting to data sources, many don’t. None have Aible’s domain knowledge.

  4. Data Enhancement: Auto-creates data cleansing and augmentation code to make real-world data useful for Machine Learning. This same data cleansing code is deployed with the final model. Some feature creation is offered by other offerings, but typically these enhancements must be manually replicated at deployment.

  5. Model Customization: Enhances leading model frameworks such as TensorFlow to optimize for your unique definition of business impact instead of simple accuracy. With other offerings, model customization is rarely done, and even then only through expert consulting for a fee.

  6. Hyperparameter Tuning: In minutes, trains many different model types and tries different options for each model type to create the best possible model. Most AutoML solutions tune models only for accuracy, not business impact.

  7. Model Selection: Recommends the model that maximizes business impact. You can review what drove model predictions and conduct “what-if” scenarios and sensitivity analyses. Some solutions provide model explanations and “what-if” analysis. None offer sensitivity or scenario analysis that optimizes prediction, and takes into account cost-benefit trade-offs and business constraints.

  8. Model Deployment: Runs the model in serverless form so that many different models can be run at low cost. With other solutions, the user often must handle the deployment.

  9. Prediction Integration: Writes predictions back to your enterprise applications so actionable insights are available right where you work. Prediction integration is typically not within the scope of other AutoML offerings.

  10. Monitoring: Monitors the actual business outcomes and compares them to what was predicted. Highlights where model decay has occurred and recommends model retraining.

    Other solutions only monitor tactical information, not business impact delivered. No other solution enables “one-click” retraining and model splitting.

 

Aible is the only AutoML AI platform that fully automates all 10 essential steps of the machine learning life cycle. Anything less isn’t real AutoML.

 

 

Tags: AI for Business Impact

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