2011, ML and Python - Data Science Current

Improving air quality with generative AI

AWS Machine Learning Blog

JUNE 18, 2024

The solution harnesses the capabilities of generative AI, specifically Large Language Models (LLMs), to address the challenges posed by diverse sensor data and automatically generate Python functions based on various data formats. The solution only invokes the LLM for new device data file type (code has not yet been generated).

AWS

AWS AI AI Python

Streamlining ETL data processing at Talent.com with Amazon SageMaker

AWS Machine Learning Blog

DECEMBER 14, 2023

Established in 2011, Talent.com aggregates paid job listings from their clients and public job listings, and has created a unified, easily searchable platform. The system includes feature engineering, deep learning model architecture design, hyperparameter optimization, and model evaluation, where all modules are run using Python.

ETL

ETL AWS ML ML

Michael I. Jordan of Berkeley on Learning-Aware Mechanism Design

ODSC - Open Data Science

FEBRUARY 20, 2023

He gave the Inaugural IMS Grace Wahba Lecture in 2022, the IMS Neyman Lecture in 2011, and an IMS Medallion Lecture in 2004. He received the Ulf Grenander Prize from the American Mathematical Society in 2021, the IEEE John von Neumann Medal in 2020, the IJCAI Research Excellence Award in 2016, the David E.

Machine Learning

Machine Learning Machine Learning Data Science Python

Reinventing a cloud-native federated learning architecture on AWS

AWS Machine Learning Blog

OCTOBER 10, 2023

Machine learning (ML), especially deep learning, requires a large amount of data for improving model performance. It is challenging to centralize such data for ML due to privacy requirements, high cost of data transfer, or operational complexity. The ML framework used at FL clients is TensorFlow.

AWS

AWS ML ML Algorithm

A Practical Guide for identifying important features using Python

Mlearning.ai

JULY 30, 2023

Identifying important features using Python Introduction Features are the foundation on which every machine-learning model is built. Nonetheless, features are an essential ingredient in building an ML model. This covers unsupervised, supervised, self-supervised, decision-making, and even graph ML. 2825–2830, 2011.

Python

Python Machine Learning Machine Learning Algorithm

Top 10 Deep Learning Platforms in 2024

DagsHub

JULY 25, 2024

This guarantees businesses can fully utilize deep learning in their AI and ML initiatives. You can make more informed judgments about your AI and ML initiatives if you know these platforms' features, applications, and use cases. Integration: Strong integration with Python, supporting popular libraries such as NumPy and SciPy.

Deep Learning

Deep Learning Deep Learning Machine Learning Machine Learning

From text to dream job: Building an NLP-based job recommender at Talent.com with Amazon SageMaker

AWS Machine Learning Blog

OCTOBER 23, 2023

Founded in 2011, Talent.com is one of the world’s largest sources of employment. The system is developed by a team of dedicated applied machine learning (ML) scientists, ML engineers, and subject matter experts in collaboration between AWS and Talent.com. The recommendation system has driven an 8.6%

AWS

AWS Deep Learning Deep Learning Machine Learning

Time Series Forecasting with XGBoost and LightGBM: Predicting Energy Consumption with Lag Features

Mlearning.ai

AUGUST 6, 2023

We can plot these with the help of the `plot_pacf` function of the statsmodels Python package: [link] Partial autocorrelation plot for 12 lag features We can clearly see that the first 9 lags possibly contain valuable information since they’re out of the bluish area.

Python

Python Algorithm AI AI

Optimized Deep Learning Pipelines: A Deep Dive into TFRecords and Protobufs (Part 2)

Heartbeat

JULY 27, 2023

If you’ve only been programming in Python land your whole life, and have no clue what I mean when I say map, you can think of it as no different than a Python dictionary. For example, “Features” would have to become: Again, if you’ve only ever programmed in Python, or something of the sort, this might seem strange to you.

Deep Learning

Deep Learning Deep Learning Python ML

Use streaming ingestion with Amazon SageMaker Feature Store and Amazon MSK to make ML-backed decisions in near-real time

AWS Machine Learning Blog

APRIL 19, 2023

Businesses are increasingly using machine learning (ML) to make near-real-time decisions, such as placing an ad, assigning a driver, recommending a product, or even dynamically pricing products and services. As a result, some enterprises have spent millions of dollars inventing their own proprietary infrastructure for feature management.

ML

ML ML Apache Kafka SQL

Efficiently train, tune, and deploy custom ensembles using Amazon SageMaker

AWS Machine Learning Blog

JULY 20, 2023

As AI has evolved, we have seen different types of machine learning (ML) models emerge. Detailed deployment patterns for this kind of settings can be found in Model hosting patterns in Amazon SageMaker, Part 1: Common design patterns for building ML applications on Amazon SageMaker.

ML

ML ML Cross Validation AWS

How to See Like a Machine

Mlearning.ai

JUNE 5, 2023

Note : This blog is more biased towards python as it is the language most developers use to get started in computer vision. Python / C++ The programming language to compose our solution and make it work. Why Python? Easy to Use: Python is easy to read and write, which makes it suitable for beginners and experts alike.

Deep Learning

Deep Learning Deep Learning Python Machine Learning

Announcing new Jupyter contributions by AWS to democratize generative AI and scale ML workloads

AWS Machine Learning Blog

MAY 10, 2023

Project Jupyter is a multi-stakeholder, open-source project that builds applications, open standards, and tools for data science, machine learning (ML), and computational science. Given the importance of Jupyter to data scientists and ML developers, AWS is an active sponsor and contributor to Project Jupyter.

ML

ML AWS ML AI

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

AWS Machine Learning Blog

NOVEMBER 11, 2024

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). We then also cover how to fine-tune the model using SageMaker Python SDK. You can access the Meta Llama 3.2

AI

AI AI ML ML

Data Science Current

Improving air quality with generative AI

Streamlining ETL data processing at Talent.com with Amazon SageMaker

Trending Sources

Michael I. Jordan of Berkeley on Learning-Aware Mechanism Design

Reinventing a cloud-native federated learning architecture on AWS

A Practical Guide for identifying important features using Python

Top 10 Deep Learning Platforms in 2024

From text to dream job: Building an NLP-based job recommender at Talent.com with Amazon SageMaker

Time Series Forecasting with XGBoost and LightGBM: Predicting Energy Consumption with Lag Features

Optimized Deep Learning Pipelines: A Deep Dive into TFRecords and Protobufs (Part 2)

Use streaming ingestion with Amazon SageMaker Feature Store and Amazon MSK to make ML-backed decisions in near-real time

Efficiently train, tune, and deploy custom ensembles using Amazon SageMaker

How to See Like a Machine

Announcing new Jupyter contributions by AWS to democratize generative AI and scale ML workloads

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

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