SnapLogic Data Science Resources
This category is for all things related to SnapLogic Data Science and it’s machine learning capabilities. If you are participating in the SnapLogic Free Trial, see the SnapLogic Trial category. Here are some resources to get you started: Machine Learning Showcase In the Machine Learning Showcase, you will find various machine learning applications that were developed and deployed entirely in SnapLogic Data Science, an extension of SnapLogic’s Intelligent Integration Platform. Customer Churn Prediction Natural Language Processing The Decision Tree Image Recognition (Inception-v3) (This site will request access to your computer’s camera.) Handwritten Digit Recognition Diabetes Progression Prediction Iris Flower Classification ML API Tester Loan Repayment Prediction Product Documentation Documentation of the Snap Pack can be found at SnapLogic Data Science (Machine Learning) SnapLogic Website Information about the Machine Learning Snap Packs can also be found on our website: ML Data Preparation Snap Pack ML Core Snap Pack ML Analytics Pipeline Patterns If you are in the SnapLogic Free Trial, you will find a set of Patterns under the Patterns tab in the SnapLogic Designer. Patterns are pre-built, reusable integration pipelines that can be configured through a step-by-step wizard. These patterns match the use cases described in the documentation and Machine Learning Showcase. Non-trial users can download the same pipelines from the documentation. Videos Data collection and preparation Model building and validation
"Fail safe upon execution" checkbox in the Type Converter
Hello, I noted checkbox “Fail safe upon execution” in Type Converter. Looks like this was added recently. I cannot find this checkbox in the documentation. Documentation of the Type Converter contain screenshot without this checkbox. Is this new thing? Thanks, Vlad[Webcast] No data scientist? No problem: Self-service machine learning
The McKinsey Global Institute predicts that the U.S. will be short 250,000 data scientists by 2024. By 2021, insights-driven business will steal $1.8 trillion a year in revenue from competitors that are not insights-driven, according to Forrester. Don’t let the building talent shortage and access to relevant data stop your organization from using machine learning to build powerful models that help you perform predictive and eventually, prescriptive analytics. SnapLogic allows you to leverage its machine learning capabilities on your data , without the need for specialized training in data science. View this webinar on demand! You can expect to learn: The key differences between AI and ML algorithms, Deep Learning, Image Recognition, Handwriting Recognition, Natural Language Processing techniques, etc. The additional types of insights you can expect to obtain from machine learning The machine learning process How SnapLogic’s machine learning solution helps get you started on your machine learning/data scientist project! SPEAKERS Dr. Greg Benson Chief Scientist at SnapLogic Craig Stewart SVP of Product Management and Product Marketing at SnapLogicMachine Learning Showcase: Loan Repayment Prediction
Added with the 4.16 release, the Loan Repayment Prediction demo train a machine learning model to predict whether a loan will be fully paid off or “charged off” (never fully paid). The AutoML Snap automates the process of exploring and tuning machine learning models for a given dataset within the resource limit. See the Machine Learning Showcase for a demo and visit the documentation to learn more about the demo and the AutoML Snap.Machine Learning Showcase: Customer Churn Prediction
Recently added to the Machine Learning Showcase, the Customer Churn Prediction demo trains a machine learning model to predict customer churn for a telecommunications company. See the Machine Learning Showcase for a demo and visit the documentation to learn more about the demo.Data At Scale For AI At Scale: How To Think About Data Readiness
This week's episode of the Enterprise Alchemists is another live recording from Integreat 2024 in London! This week we have Maks Shah of Syngenta; we had a fascinating conversation during the after-event cocktail party — which is why this episode is a bit shorter than normal. Maks's key takeaway was that "there was a common theme throughout all the presentations this afternoon, and that was that your data has to be fit for it". There is no hashtag#AI success without the data to feed it with. Establishing a solid data foundation is step zero on your journey to hashtag#GenAI.
