SnapLogic Data Science

Bringing self-service to machine learning

SnapLogic Data Science accelerates the development and deployment of machine learning (ML) models, so you can reap the benefits of AI faster.

The future belongs to companies that excel in data-based decision making. Machine learning (ML) is becoming the best vehicle for achieving this data supremacy.

Traditional approaches to machine learning are code-intensive, redundant, and fragmented. SnapLogic Data Science offers a self-service alternative to machine learning that accelerates and increases the success of your ML projects. Now, data engineers, data scientists, DevOps, and anyone else in the ML lifecycle can take a visual drag-and-drop approach to collecting and preparing data, developing ML models, and deploying those models. And this all happens inside one unified platform.

SnapLogic supports the entire machine learning lifecycle

SnapLogic Data Science, in combination with the Enterprise Integration Cloud, is a self-service platform for end-to-end ML that offers a low-code approach to data acquisition, data exploration and preparation, model training and validation, and model deployment. The solution includes three sets of pre-built pipeline components (Snap Packs):

  • ML Data Preparation Snap Pack – Perform preparatory operations on datasets such as data type transformation, data cleanup, sampling, shuffling, and scaling.
  • ML Core Snap Pack – Perform operations on machine learning data sets such as model training, cross-validation, and model-based predictions.
  • ML Analytics Snap Pack – Perform analytic operations such as data profiling and data type inspection.

Synthesize data from multiple sources to train your model

  • Rapidly integrate a variety of endpoints – cloud applications, data warehouses, IoT, etc. – to create quality training datasets
  • Prepare and transform data for developing your model using a low-code approach
  • Manage big data for improved model training without incurring high costs or needing deep technical expertise

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SnapLogic Data Science Overview

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Automate machine learning model development

  • Leverage a visual drag-and-drop interface to spend less time cleaning data and more time analyzing it
  • Prepare data, create multiple models, validate them, and perform advanced analytics all in one platform
  • Develop and deploy models in SnapLogic’s end-to-end machine learning solution to gain rich insights and improve decision-making

Data Sheet

SnapLogic Data Science

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Enjoy easy, secure model building and serverless deployments

  • Access state-of-the-art algorithms for testing your model and select the most accurate predictor(s)
  • Keep your training data secure and private by not having to send it to cloud services for model training
  • Pick from an ever-expanding list of supported algorithms: Decision Tree, K-Nearest Neighbors, Logistic Regression, Naïve Bayes, Support Vector Machines, Decision Stump, Random Forests, Multi-Layer Perception, Linear Regression, and more
  • Deploy your model in the form of an API that does not require a server in order to operate

AI and ML innovations

The SnapLogic Machine Learning Showcase

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Collect data and build and deploy machine learning models all in one platform

SnapLogic Data Science leverages the power of the SnapLogic Enterprise Integration Cloud, an integration platform as a service (iPaaS). This allows you to integrate data, build machine learning models, and deploy them all in one place.

SnapLogic Data Science provides a low-code interface that works with all data regardless of its location (on-premises, cloud, hybrid), format (structured, semi-structured, unstructured), speed (real-time, event-based, batch), or source type (application, device/machine, data, APIs, big data). It brings speed, flexibility, and power to model development.

Data Sheet

SnapLogic Enterprise Integration Cloud

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Try self-service machine learning today.

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“Our team at Boston University is looking forward to leveraging … the new Machine Learning Snap Pack[s].”

-Carmine A. Granucci, Assistant Director of Integration, Boston University

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