Remove Data Analysis Remove Data Drift Remove Natural Language Processing
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Five open-source AI tools to know

IBM Journey to AI blog

The readily available nature of open-source AI also raises security concerns; malicious actors could leverage the same tools to manipulate outcomes or create harmful content. Biased training data can lead to discriminatory outcomes, while data drift can render models ineffective and labeling errors can lead to unreliable models.

AI Tools 207
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Machine Learning Operations (MLOPs) with Azure Machine Learning

ODSC - Open Data Science

Model Development (Inner Loop): The inner loop element consists of your iterative data science workflow. A typical workflow is illustrated here from data ingestion, EDA (Exploratory Data Analysis), experimentation, model development and evaluation, to the registration of a candidate model for production.

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AI in Time Series Forecasting

Pickl AI

Long Short-Term Memory (LSTM) A type of recurrent neural network (RNN) designed to learn long-term dependencies in sequential data. Facebook Prophet A user-friendly tool that automatically detects seasonality and trends in time series data. Making Data Stationary: Many forecasting models assume stationarity.

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How United Airlines built a cost-efficient Optical Character Recognition active learning pipeline

AWS Machine Learning Blog

This workflow will be foundational to our unstructured data-based machine learning applications as it will enable us to minimize human labeling effort, deliver strong model performance quickly, and adapt to data drift.” – Jon Nelson, Senior Manager of Data Science and Machine Learning at United Airlines.

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Creating An Information Edge With Conversational Access To Data

Topbots

Adaptability over time To use Text2SQL in a durable way, you need to adapt to data drift, i. the changing distribution of the data to which the model is applied. For example, let’s assume that the data used for initial fine-tuning reflects the simple querying behaviour of users when they start using the BI system.