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Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. Impedance mismatch between data scientists, data engineers and production engineers.

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AI Chihuahua! Part I: Why Machine Learning is Dogged by Failure and Delays

d2iq

Going from a prototype to production is perilous when it comes to machine learning: most initiatives fail , and for the few models that are ever deployed, it takes many months to do so. As little as 5% of the code of production machine learning systems is the model itself. Adapted from Sculley et al.

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The 10 most in-demand IT jobs in finance

CIO

In the finance industry, software engineers are often tasked with assisting in the technical front-end strategy, writing code, contributing to open-source projects, and helping the company deliver customer-facing services. Data engineer.

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The 10 most in-demand IT jobs in finance

CIO

In the finance industry, software engineers are often tasked with assisting in the technical front-end strategy, writing code, contributing to open-source projects, and helping the company deliver customer-facing services. Data engineer.

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Building a Machine Learning Application With Cloudera Data Science Workbench And Operational Database, Part 3: Productionization of ML models

Cloudera

Machine learning is now being used to solve many real-time problems. One big use case is with sensor data. Corporations now use this type of data to notify consumers and employees in real-time. With this example as inspiration, I decided to build off of sensor data and serve results from a model in real-time.

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Supporting Diverse ML Systems at Netflix

Netflix Tech

Berg , Romain Cledat , Kayla Seeley , Shashank Srikanth , Chaoying Wang , Darin Yu Netflix uses data science and machine learning across all facets of the company, powering a wide range of business applications from our internal infrastructure and content demand modeling to media understanding.

System 90
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AI in the Cloud: What Are The Go-To Options?

Exadel

The technological landscape has evolved to include AI assistants, self-driving cars, and machine learning solutions that process data in a blink of an eye. Major Players for AI in the Cloud For the scope of this article, AI is defined as machine learning, since ML is the biggest constituent of the technology.