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 also demonstrate the performance of our state-of-the-art point cloud-based product lifecycle prediction algorithm.

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DataRobot Blog

MAY 10, 2022

It has been over a decade since the Federal Reserve Board (FRB) and the Office of the Comptroller of the Currency (OCC) published its seminal guidance focused on Model Risk Management ( SR 11-7 & OCC Bulletin 2011-12 , respectively). The Framework for ML Governance. Conclusion. Connect with Harsh on Linkedin. Download Now.

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AWS Machine Learning Blog

OCTOBER 23, 2023

Founded in 2011, Talent.com is one of the world’s largest sources of employment. The performance of Talent.com’s matching algorithm is paramount to the success of the business and a key contributor to their users’ experience. The recommendation system has driven an 8.6%

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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. In finance, it's applied for fraud detection and algorithmic trading. In 2011, H2O.ai

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Mlearning.ai

JULY 30, 2023

Nonetheless, features are an essential ingredient in building an ML model. This covers unsupervised, supervised, self-supervised, decision-making, and even graph ML. What we are looking for in these algorithms is to output a list of features along with corresponding importance values. However, they struggle with sparse features.

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Mlearning.ai

JULY 22, 2023

This approach allows Apple to effectively remove a number of privacy concerns by eliminating the need for extensive data sharing and having the algorithms operate locally on users’ devices. Siri launched back in 2011 and became the first modern virtual assistant of its kind.

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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. Conceptual Soundness of the Model.

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Heartbeat

OCTOBER 9, 2023

Computer vision algorithms can reconstruct a highly detailed 3D model by photographing objects from different perspectives. But computer vision algorithms can assist us in digitally scanning and preserving these priceless manuscripts. These ground-breaking areas redefine how we connect with and learn from our collective past.

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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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AWS Machine Learning Blog

JULY 20, 2023

As AI has evolved, we have seen different types of machine learning (ML) models emerge. Ensemble learning refers to the use of multiple learning models and algorithms to gain more accurate predictions than any single, individual learning algorithm.

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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. 17] “ LipNet ” introduces the first approach for an end-to-end lip reading algorithm at sentence level. Thus the algorithm is alignment-free.

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Mlearning.ai

JUNE 5, 2023

C++ also provides direct access to low-level features like pointers and bitwise operations, which can improve the efficiency of algorithms and data structures. It is a fork of the Python Imaging Library (PIL), which was discontinued in 2011. This is critical for computer vision jobs that need real-time processing and precision.

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Towards AI

NOVEMBER 8, 2024

We don’t have better algorithms; we just have more data. 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. Because of how ML practitioners were initially trained.

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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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