Artificial Inteligence Data Engineering Machine Learning Scalability 
Altexsoft
JUNE 29, 2021
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.
InnovationM
APRIL 25, 2024
Deploying a Large Language Model (LLM) on RunPod Leveraging the prowess of RunPod for deploying Large Language Models (LLMs) unveils a realm of possibilities in distributed environments. Model Selection: Choose the specific LLM model you want to deploy. How to approach it?
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d2iq
JANUARY 18, 2022
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.
Confluent
FEBRUARY 6, 2019
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.
CIO
DECEMBER 11, 2023
Both healthcare payers and providers remain cautious about how to use this latest version of artificial intelligence, and rightfully so. You have to balance the potential benefits of generative AI with significant, important operational issues, such as ensuring patient data privacy and complying with regulatory requirements.
d2iq
FEBRUARY 19, 2021
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.
CIO
FEBRUARY 14, 2023
By George Trujillo, Principal Data Strategist, DataStax Increased operational efficiencies at airports. Investments in artificial intelligence are helping businesses to reduce costs, better serve customers, and gain competitive advantage in rapidly evolving markets. Instant reactions to fraudulent activities at banks.
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