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            Create Predictive AI Models From Scratch

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            Create Predictive AI Models From Scratch

            Create models to analyze your data and get AI powered predictions based on machine learning. Learn about supported use cases and ethical model building. Then, follow the steps to create a model and evaluate model quality.

            Required Editions

            Available in: All Editions supported by Data 360. See Data 360 edition availability.
            • Supported Predictive Model Types
              Predictive models address common business use cases.
            • Design Ethical AI Models
              Salesforce offers guidance to help you practice ethical use of artificial intelligence (AI) in your business.
            • Prepare Your Training Data
              Effective data preparation is the key to getting great results with your predictive model. Harness the power of data integration in Data 360. Load and transform data from one or more data sources into a data model object (DMO) with optimized training data. You can pull data from Salesforce and external sources. You can also use third-party tools and utilities to further expedite data cleansing and wrangling tasks.
            • Create a Model from Scratch
              Create a model that uses historical data to predict future outcomes. Select and structure your data source and define the desired outcome. Then train the model to reveal predictive inferences and insights by using AI, machine learning, and statistical analysis.
            • Improve Predictive Models
              Model quality is a critical success factor in predictive AI solutions. AI Models (formerly Einstein Studio) supports continuous, iterative improvement for predictive models. Measure model quality in production over time. Use quality alerts to identify and address areas for improvement. Experiment with new model versions. Efforts to improve model quality result in better business outcomes.
            • Evaluate Model Quality
              Use training metrics to evaluate the model’s ability to predict an outcome and determine whether it’s ready to activate. Training metrics provide information about how effectively the model understands patterns and relationships within the training data, and indicates its predictive efficacy.
             
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