Remove 2011 Remove Data Preparation Remove Machine Learning
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From text to dream job: Building an NLP-based job recommender at Talent.com with Amazon SageMaker

AWS Machine Learning Blog

This post is co-authored by Anatoly Khomenko, Machine Learning Engineer, and Abdenour Bezzouh, Chief Technology Officer at Talent.com. Founded in 2011, Talent.com is one of the world’s largest sources of employment. Standard feature engineering Our data preparation process begins with standard feature engineering.

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Improving air quality with generative AI

AWS Machine Learning Blog

More than 170 tech teams used the latest cloud, machine learning and artificial intelligence technologies to build 33 solutions. The output data is transformed to a standardized format and stored in a single location in Amazon S3 in Parquet format, a columnar and efficient storage format.

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Top 10 Deep Learning Platforms in 2024

DagsHub

Source: Author Introduction Deep learning, a branch of machine learning inspired by biological neural networks, has become a key technique in artificial intelligence (AI) applications. Deep learning methods use multi-layer artificial neural networks to extract intricate patterns from large data sets.

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IBM Watson Supercomputer

Dataconomy

Through its sophisticated algorithms and machine learning techniques, it brings revolutionary changes to various industries by automating processes and improving decision-making quality. milestone In 2011, Watson demonstrated its capabilities by winning Jeopardy!, showcasing the potential of AI in quizzes and challenges.

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Fine-tune Meta Llama 3.2 text generation models for generative AI inference using Amazon SageMaker JumpStart

AWS Machine Learning Blog

Solution overview SageMaker JumpStart is a robust feature within the SageMaker machine learning (ML) environment, offering practitioners a comprehensive hub of publicly available and proprietary foundation models (FMs). He holds a Master’s degree in Machine Learning and Software Engineering from Syracuse University.

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