How to Organize Production-Ready Data Science/Engineering Projects
Mlearning.ai
APRIL 27, 2023
Tips from seniors and my own learning points Continue reading on MLearning.ai »
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How Vericast optimized feature engineering using Amazon SageMaker Processing
AWS Machine Learning Blog
MAY 3, 2023
For any machine learning (ML) problem, the data scientist begins by working with data. This includes gathering, exploring, and understanding the business and technical aspects of the data, along with evaluation of any manipulations that may be needed for the model building process.
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How CCC Intelligent Solutions created a custom approach for hosting complex AI models using Amazon SageMaker
AWS Machine Learning Blog
JANUARY 20, 2023
As the company continues to evolve to integrate AI into its existing and new product catalog, this requires sophisticated approaches to train and deploy multi-modal machine learning (ML) ensemble models for solving complex business needs. Daniel Suarez is a Data Science Engineer at CCC Intelligent Solutions.
This Is Where JavaScript Beats Python For Machine Learning
Dlabs.ai
DECEMBER 10, 2020
Truth be told, Python outperforms JavaScript on many fronts when it comes to machine learning, especially in terms of the availability and maturity of ML libraries. And no data science engineer worth their salt should ignore the chance to reach so many users — or work with the gargantuan dataset that web browsers can provide.
Building Visual Search Engines with Kuba Cie?lik
The MLOps Blog
JANUARY 5, 2023
This article was originally an episode of the MLOps Live , an interactive Q&A session where ML practitioners answer questions from other ML practitioners. Every episode is focused on one specific ML topic, and during this one, we talked to Kuba Cieślik, founder and AI Engineer at tuul.ai , about building visual search engines.
Building Generative AI and ML solutions faster with AI apps from AWS partners using Amazon SageMaker
AWS Machine Learning Blog
DECEMBER 4, 2024
This increases the time it takes for customers to go from data to insights. Our customers want a simple and secure way to find the best applications, integrate the selected applications into their machine learning (ML) and generative AI development environment, manage and scale their AI projects.
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