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What is Machine Learning Engineer: Responsibilities, Skills, and Value Brought

Altexsoft

In a world fueled by disruptive technologies, no wonder businesses heavily rely on machine learning. Google, in turn, uses the Google Neural Machine Translation (GNMT) system, powered by ML, reducing error rates by up to 60 percent. The role of a machine learning engineer in the data science team.

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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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Key Data Engineer responsibilities

Apiumhub

Data engineer roles have gained significant popularity in recent years. Number of studies show that the number of data engineering job listings has increased by 50% over the year. And data science provides us with methods to make use of this data. Who are data engineers?

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Managing Machine Learning Workloads Using Kubeflow on AWS with D2iQ Kaptain

d2iq

Security: Data privacy and security are often afterthoughts during the process of model creation but are critical in production. D2iQ is an AWS Containers Competency Partner , and D2iQ Kaptain is an enterprise Kubeflow product that enables organizations to develop and deploy machine learning workloads at scale.

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Unlocking the Power of AI with a Real-Time Data Strategy

CIO

To succeed with real-time AI, data ecosystems need to excel at handling fast-moving streams of events, operational data, and machine learning models to leverage insights and automate decision-making. It’s also used to deploy machine learning models, data streaming platforms, and databases.

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Dataiku and Snowflake Bring New Capabilities to Data Engineers, Data Scientists, & Developers

Dataiku

One key to more efficient, effective AI model and application development is executing workloads on compute platforms that offer high scalability, performance, and concurrency.

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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.