Remove 2019 Remove Data-driven Remove Experimentation Remove Machine Learning
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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages.

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6 trends framing the state of AI and ML

O'Reilly on Data

O’Reilly online learning is a trove of information about the trends, topics, and issues tech leaders need to know about to do their jobs. Our analysis of ML- and AI-related data from the O’Reilly online learning platform indicates: Unsupervised learning surged in 2019, with usage up by 172%.

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What you need to know about product management for AI

O'Reilly on Data

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. Why AI software development is different.

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The DataOps Vendor Landscape, 2021

DataKitchen

This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. Companies that implement DataOps find that they are able to reduce cycle times from weeks (or months) to days, virtually eliminate data errors, increase collaboration, and dramatically improve productivity.

Testing 300
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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

Are you seeing currently any specific issues in the Insurance industry that should concern Chief Data & Analytics Officers? Lack of clear, unified, and scaled data engineering expertise to enable the power of AI at enterprise scale. The data will enable companies to provide more personalized services and product choices.

Insurance 250
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HPE Looks to Edge-to-Cloud Strategy for Growth in 2018/2019

Hurwitz & Associates

Edge-to-cloud is the central focus of Hewlett Packard Enterprise (HPE) marketing and go-to-market efforts in 2018/2019. Edge solutions keep large and growing data sets close to where the data is generated, and faster networks facilitate data transfer from edge systems to the cloud.

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Bridging the Gap Between Analytics Expectations and Reality

Sisense

Companies surveyed by Harvard Business Review Analytic Services (HBR) report that two of the most important strategic benefits of using data analytics are (1) identifying new revenue and business models and (2) becoming more innovative. 39% of companies want to identify new revenue and business opportunities with data analytics.