(翻訳) Einstein Studioのモデルビルダーを使用して予測(Forecasting)モデルを構築する
この技術記事では、Data Cloudのモデルビルダーを使用して予測 (Forecasting) ユース ケース向けの予測モデル (Predictive model )を構築する プロセスを順を追って 説明します 。
Beyond Binary: Unlocking Business Insights with the Power of Multinomial Classifiers: “The Lab” – Part 3
This is a continuation of the previous blog post Beyond Binary: Unlocking Business Insights with the Power of Multinomial Classifiers: “The Lab”- Part 2. In this blog, we’ll apply One-versus-Rest approach to train five binary models in EInstein Studio and combine their results for probabilistic consistency. Part 3: Training the Binary Model Ensemble How to ...
(翻訳) データを使って意思決定：ビジネスワークフローに予測AIを統合
企業内に蓄積されたデータを有効に活用することが企業の命運を分ます。今後AIを使った意思決定がますます重要になってくるでしょう。今回は予測AIをビジネスフローに簡単に組み込む方法を紹介します。
Build Forecasting models using Einstein Studio’s Model Builder
This guide will walk you through the process of building a predictive model for a forecasting use case using the Model builder in Data Cloud. If you’re new to building predictive models and exploring the No-code model builder, make sure to check out the excellent Salesforce Blog Build an AI Model With Clicks In Data ...
From Data to Decisions: Integrating Predictive AI for Key Business Workflows
We’re in the midst of an AI and data revolution–and here with Data Cloud, we’re innovating quickly with Model Builder to help you take advantage of your Salesforce Data. Model Builder transforms raw data into actionable predictions, helping you make smarter decisions and drive better results. This innovative tool empowers you to create and deploy ...
Build an AI Model With Clicks In Data Cloud
The all new Einstein Studio tab in Data Cloud is now GA and will allow users with access to Data Cloud to build, connect, and manage their predictive and generative models. Models which can be seamlessly integrated into any Salesforce Customer 360 application for intelligent decision making. Previously Einstein Studio allowed users to connect to ...
Einstein Discovery – Live Predictions with Snowflake
Training a machine learning model in Einstein Discovery relies upon data in CRM Analytics (CRMA) datasets. The tight coupling with CRMA datasets provides many benefits to customers in terms of data availability, feature transformations/calculations, a rich ETL layer, connections to dozens of common data sources, and so on. Once deployed to Salesforce, Einstein Discovery models ...
Einstein Discovery – Bring Your Own Model Deployment
Welcome to part two of the BYOM deep dive. In this post, we will take you through the process of deploying the model we generated in part one of this blog. Similar to part one, the overall structure of this walk-through is contained within two primary phases; some of which also have multiple steps: Deploying ...
Einstein Discovery – Bring Your Own Model Deep Dive
Einstein Discovery machine learning helps you build powerful predictive models on your data using clicks, not code. With a simple, wizard-driven interface, you have the ability to rapidly create insights and predictive models. Einstein Discovery utilizes a number of industry-standard algorithms to facilitate building models, these include: GLM (linear and logistic regression) GBM (Gradient Boost ...
Text Clustering in Einstein Discovery
It is common to build and deploy supervised machine learning models that are generally comprised of tabular datasets with numerical, categorical, and temporal (date/time) variables. Often though, there may be additional value to be gained by augmenting the model with insights derived from unstructured data (text). Some common examples of unstructured text in this context ...