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Making long-term forecasts at Lyft
At Lyft, like many other companies, we need to make accurate short and long-term forecasts. Some of the metrics that we need to accurately predict are number of driver hours provided by drivers in different regions — i.e our supply side of the business — and also number of rides taken by riders in different regions, i.e. our demand. We have several internal tools that we use to make forecasts.
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The Effects of Mixing Machine Learning and Human Judgment
In 1997 IBM’s Deep Blue software beat the World Chess Champion Garry Kasparov in a series of six matches. Since then, other programs have beaten human players in games ranging from Jeopardy to Go. Inspired by his loss, Kasparov decided in 2005 to test the success of Human+AI pairs in an online chess tournament.2
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Addressing Multi-Cloud Automation, Hashicorp Releases Terraform Cloud
In a recent blog post, HashiCorp announced the full release of Terraform Cloud, an open-source SaaS platform for teams to manage their infrastructure-as-code workflows. This orchestration takes place through cloud-agnostic tools that allow teams to improve their productivity through repeatable automation. This announcement follows their May 2019 announcement of Remote State Management.
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