InfoQ Homepage SaaS Content on InfoQ
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Metrics-Driven Machine Learning Development at Salesforce Einstein
Eric Wayman discusses how Salesforce tracks data and modeling metrics in the pipeline to identify data and modeling issues and to raise alerts for issues affecting models running in production.
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Building a Voice Assistant for Enterprise
Manju Vijayakumar talks about Einstein Assistant - an AI Voice assistant for enterprises that enables users to "Talk to Salesforce".
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Designing Automated Pipelines for Unseen Custom Data
Kevin Moore discusses some challenges in designing automated machine learning pipelines that can deal with custom user data that it has never seen before, as well as some of Salesforce’s solutions.
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Implementing AutoML Techniques at Salesforce Scale
Matthew Tovbin shows how to build ML models using AutoML (Salesforce), including techniques for automatic data processing, feature generation, model selection, hyperparameter tuning and evaluation.
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Models in Minutes not Months: AI as Microservices
Sarah Aerni talks about how Salesforce built an AI platform that scales to thousands of customers.
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The Black Swan of Perfectly Interpretable Models
Mayukh Bhaowal, Leah McGuire discuss how Salesforce Einstein made ML more transparent and less of a black box, and how they managed to drive wider adoption of ML.
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Tech Modernization: A Cloud Migration
John Berry and Henri van den Bulk share lessons learned and guidance on building scalable cloud-based SaaS applications.
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Managing Thousands of Data Services @Heroku
Gabriel Enslein discusses the evolution of fleet orchestration, immutable infrastructure, security auditing for managing data services for many Salesforce customers.
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A Practical Road to SaaS in Python
Armin Ronacher discusses his experiences building SaaS businesses on a Python technology stack from a security and scalability point of view, and what other technologies work well with Python.
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Serverless Meets SaaS: The Ultimate Match
Tod Golding discusses the architecture and design strategies associated with building and delivering SaaS solutions in a serverless model.
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The Lego Model for Machine Learning Pipelines
Leah McGuire describes the machine learning platform Salesforce wrote on top of Spark to modularize data cleaning and feature engineering.
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Can Agile Work for Off-the-shelf Software?
Ceri Shaw, Adrian Banks discuss the challenges and rewards of Agile when working on an enterprise software product and contrast them with working in a more traditionally Agile SaaS setting.
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