ODSC - Open Data Science

FEBRUARY 20, 2023

I spent a day a week at Amazon, and they’ve been doing machine learning going back to the early 90s to find patterns and also make logistics decisions. Whereas the kind of current machine learning style thinking that federated learning, the ChatGPT do, is they don’t consider these issues.

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AWS Machine Learning Blog

APRIL 6, 2023

& AWS Machine Learning Solutions Lab (MLSL) Machine learning (ML) is being used across a wide range of industries to extract actionable insights from data to streamline processes and improve revenue generation. We trained three models using data from 2011–2018 and predicted the sales values until 2021.

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AWS Machine Learning Blog

JUNE 18, 2024

More than 170 tech teams used the latest cloud, machine learning and artificial intelligence technologies to build 33 solutions. The attempt is disadvantaged by the current focus on data cleaning, diverting valuable skills away from building ML models for sensor calibration.

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AWS Machine Learning Blog

OCTOBER 23, 2023

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. It’s designed to significantly speed up deep learning model training.

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DataRobot Blog

MAY 10, 2022

Addressing the Key Mandates of a Modern Model Risk Management Framework (MRM) When Leveraging Machine Learning . Given this context, how can financial institutions reap the benefits of modern machine learning approaches, while still being compliant to their MRM framework?

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DataRobot Blog

APRIL 29, 2022

The stakes in managing model risk are at an all-time high, but luckily automated machine learning provides an effective way to reduce these risks. As machine learning advances globally, we can only expect the focus on model risk to continue to increase. The Framework for ML Governance. More on this topic.

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AWS Machine Learning Blog

JULY 15, 2024

If you want to learn more about this use case or have a consultative session with the Mission team to review your specific generative AI use case, feel free to request one through AWS Marketplace. She specializes in leveraging AI and ML to drive innovation and develop solutions on AWS. She received her Ph.D. Cristian Torres is a Sr.

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AWS Machine Learning Blog

MAY 2, 2023

JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. There are a few limitations of using off-the-shelf pre-trained LLMs: They’re usually trained offline, making the model agnostic to the latest information (for example, a chatbot trained from 2011–2018 has no information about COVID-19).

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Mlearning.ai

JULY 30, 2023

Identifying important features using Python Introduction Features are the foundation on which every machine-learning model is built. Different machine-learning paradigms use different terminologies for features such as annotations, attributes, auxiliary information, etc. What is feature importance? XGBoost, LightGBM).

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DataRobot Blog

MAY 26, 2022

Validating Modern Machine Learning (ML) Methods Prior to Productionization. Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. Validating Machine Learning Models. Conceptual Soundness of the Model.

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IBM Journey to AI blog

NOVEMBER 20, 2023

It was introduced in 2011 as an alternative to the SATA and Serial Attached SCSI (SAS) protocols that were the industry standard at the time, and it conveys better throughput than its predecessors. Since 2011, NVMe technology has distinguished itself through its high bandwidth and blazing-fast data transfer speeds. What is NVMe?

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IBM Journey to AI blog

NOVEMBER 3, 2023

NVMe storage technology was designed to replace Serial Advanced Technology Attachment (SATA) and Serial Attached SCSI (SAS) protocols that were the industry standard until NVMe’s introduction in 2011. NVMe also works seamlessly with all modern operating systems, including mobile phones, laptops and gaming consoles.

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IBM Journey to AI blog

NOVEMBER 13, 2023

In 2011, NVMe storage technology was introduced as an alternative to SATA and Serial Attached SCSI (SAS) protocols, which had been the industry standard for several years. Peripheral Component Interconnect Express (PCIe) bus One of the most important differentiators of NVMe SSDs is the way it accesses flash storage.

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Heartbeat

OCTOBER 9, 2023

Artificial Intelligence (AI) Integration: AI techniques, including machine learning and deep learning, will be combined with computer vision to improve the protection and understanding of cultural assets. Preservation of cultural heritage and natural history through game-based learning. Ahmad, M., & Selviandro, N.

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Mlearning.ai

FEBRUARY 27, 2023

For the purposes of this tutorial, I’ve chosen the London Energy Dataset which contains the energy consumption of 5,567 randomly selected households in the city of London, UK for the time period of November 2011 to February 2014. In particular, in the XGBoost scenario the MAE is reduced by almost 44%, while the MAPE moved from 19% to 16%.

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Mlearning.ai

AUGUST 6, 2023

As described in the previous article , we want to forecast the energy consumption from August of 2013 to March of 2014 by training on data from November of 2011 to July of 2013. Experiments Before moving on to the experiments, let’s quickly remember what’s our task.

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Heartbeat

JULY 27, 2023

jpg': {'class': 111, 'label': 'Ford Ranger SuperCab 2011'}, '00236.jpg': Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments.

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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). Choose Submit to start the training job on a SageMaker ML instance. You can access the Meta Llama 3.2

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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.

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Towards AI

NOVEMBER 8, 2024

Edited Photo by Taylor Vick on Unsplash In ML engineering, data quality isn’t just critical — it’s foundational. Since 2011, Peter Norvig’s words underscore the power of a data-centric approach in machine learning. Yet, this perspective often gets sidelined and there was never a consensus in the ML community about it.

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Mlearning.ai

JUNE 5, 2023

It is a fork of the Python Imaging Library (PIL), which was discontinued in 2011. Machine Learning in Health Care Advancing tools Deep learning frameworks are software libraries that provide tools and functionalities for developing and deploying deep learning models. It was developed by Google and released in 2015.

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ML Review

MAY 3, 2018

Source : Hassanat (2011) [13] These approaches obtained impressive results (over 70% word accuracy) for tests performed with classifiers trained on the same speaker they were tested on. Decoding visemes: Improving machine lip-reading. Accelerating Machine Learning with Open Source Warp-CTC. Online] arXiv: 1710.01288.

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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.

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Alation

FEBRUARY 20, 2020

While this requires technology – AI, machine learning, log parsing, natural language processing,metadata management, this technology must be surfaced in a form accessible to business users – the data catalog. The Forrester Wave : Machine Learning Data Catalogs, Q2 2018.

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