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January 2021

Dissecting the need for ethical AI

Until recently, topics like data ethics and ethics in AI were limited to academic circles and non-profit organizations rallying for citizen data rights. Fast forward to 2020, and the scenario is very different; AI ethics has become a mainstream topic that's a top priority for big organizations. With data collection and processing capabilities growing by the day, it's become easier than ever to train machine learning (ML) models on this collected data. However, organizations have come to realize that, without building transparency, explainability, and impartiality into their AI models, they're likely to do more harm than good to their business. This podcast will explore why ethical AI is the need of the hour, and what key factors AI leaders should consider before implementing AI in their organization's ecosystem.

Machine Learning Guide: Choosing the Right Workflow

Machine learning (ML) and analytics make data actionable. Without it, data remains an untapped resource until a person (or an intelligent algorithm) analyzes that data to find insights relevant to addressing a business problem. For example, amidst a network outage crisis a historical database of network log records is useless without analysis. Resolving the issue requires an analyst to search the database, apply application logic, and manually identify the triggering series of events.

Algorithmia ML Model Performance Visualization Made Easy with This InfluxDB Template

Measuring your machine learning model will help you understand how well your model is doing, how useful it is, and whether your model can perform better with more data. This is what Algorithmia Insights — a feature of Algorithmia Enterprise MLOps platform — does. Algorithmia platform accelerates your time to value for ML by delivering more models quickly and securely, as it is estimated that 85% of machine learning models never make it to production.