AWS Machine Learning Blog

JUNE 22, 2023

New machines are added continuously to the system, so we had to make sure our model can handle prediction on new machines that have never been seen in training. Data preprocessing and feature engineering In this section, we discuss our methods for data preparation and feature engineering.

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Pickl AI

NOVEMBER 28, 2024

For example, in neural networks, data is represented as matrices, and operations like matrix multiplication transform inputs through layers, adjusting weights during training. Without linear algebra, understanding the mechanics of Deep Learning and optimisation would be nearly impossible.

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Heartbeat

NOVEMBER 28, 2023

Introduction Welcome to the step-by-step guide on efficiently managing TensorFlow/Keras model development with Comet. TensorFlow and Keras have emerged as powerful frameworks for building and training deep learning models. MLOps encompasses the entire ML lifecycle, from data preparation to model deployment and monitoring.

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Iguazio

FEBRUARY 8, 2024

LLMOps focuses specifically on the operational aspects of large language models (LLMs). LLM models are large deep learning models that are trained on vast datasets, are adaptable to various tasks and specialize in NLP tasks. Data Pipeline - Manages and processes various data sources.

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Mlearning.ai

NOVEMBER 29, 2023

Scientific studies forecasting  — Machine Learning and deep learning for time series forecasting accelerate the rates of polishing up and introducing scientific innovations dramatically. 19 Time Series Forecasting Machine Learning Methods How exactly does time series forecasting machine learning work in practice?

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DagsHub

APRIL 21, 2024

You need to make that model available to the end users, monitor it, and retrain it for better performance if needed. Source: Author A machine learning engineering team is responsible for working on the first four stages of the ML pipeline, while the last two stages fall under the responsibilities of the operations team. What is MLOps?

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phData

AUGUST 24, 2023

Various machine learning algorithms can be used for credit scoring and decisioning, including logistic regression, decision trees, random forests, support vector machines, and neural networks. Data Preparation The first step in the process is data collection and preparation. loan default or not).

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The MLOps Blog

NOVEMBER 30, 2022

These days enterprises are sitting on a pool of data and increasingly employing machine learning and deep learning algorithms to forecast sales, predict customer churn and fraud detection, etc., Most of its products use machine learning or deep learning models for some or all of their features.

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