Remove Data Engineering Remove Engineering Management Remove Metrics Remove Performance
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Bringing an AI Product to Market

O'Reilly Media - Ideas

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

Marketing 145
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Netflix at AWS re:Invent 2019

Netflix Tech

December 2 1pm-2pm CMP 326-R Capacity Management Made Easy with Amazon EC2 Auto Scaling Vadim Filanovsky , Senior Performance Engineer & Anoop Kapoor, AWS Abstract :Amazon EC2 Auto Scaling offers a hands-free capacity management experience to help customers maintain a healthy fleet, improve application availability, and reduce costs.

AWS 40
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Netflix at AWS re:Invent 2019

Netflix Tech

December 2 1pm-2pm CMP 326-R Capacity Management Made Easy with Amazon EC2 Auto Scaling Vadim Filanovsky , Senior Performance Engineer & Anoop Kapoor, AWS Abstract :Amazon EC2 Auto Scaling offers a hands-free capacity management experience to help customers maintain a healthy fleet, improve application availability, and reduce costs.

AWS 40
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Introducing CDP Data Engineering: Purpose Built Tooling For Accelerating Data Pipelines

Cloudera

With growing disparate data across everything from edge devices to individual lines of business needing to be consolidated, curated, and delivered for downstream consumption, it’s no wonder that data engineering has become the most in-demand role across businesses — growing at an estimated rate of 50% year over year.

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Netflix at AWS re:Invent 2019

Netflix Tech

December 2 1pm-2pm CMP 326-R Capacity Management Made Easy with Amazon EC2 Auto Scaling Vadim Filanovsky , Senior Performance Engineer & Anoop Kapoor, AWS Abstract :Amazon EC2 Auto Scaling offers a hands-free capacity management experience to help customers maintain a healthy fleet, improve application availability, and reduce costs.

AWS 15
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What are model governance and model operations?

O'Reilly Media - Ideas

In a previous post , we noted some key attributes that distinguish a machine learning project: Unlike traditional software where the goal is to meet a functional specification, in ML the goal is to optimize a metric. Quality depends not just on code, but also on data, tuning, regular updates, and retraining.

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Organise your engineering teams around the work by reteaming

Abhishek Tiwari

For instance, if you are fast-growing VC funded e-commerce startup and your number one business priority is multiplying current growth and performing exceptionally well on key financial metrics charted out by your investors. Is it possible to draw inspiration from outside of software engineering? You want to move fast.