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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Tableau

MAY 6, 2021

Let AI do the heavy lifting . Einstein Discovery in Tableau uses machine learning (ML) to create models and deliver predictions and recommendations within the analytics workflow. No code or algorithms needed. Einstein sifted through the data, discovered patterns, and surfaced recommendations in natural language.

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Iguazio

FEBRUARY 8, 2024

Data engineers, data scientists and other data professional leaders have been racing to implement gen AI into their engineering efforts. LLMOps (Large Language Model Operations), is a specialized domain within the broader field of machine learning operations (MLOps). LLMOps is MLOps for LLMs. How Does LLMOps Work?

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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. MLOps helps these organizations to continuously monitor the systems for accuracy and fairness, with automated processes for model retraining and deployment as new data becomes available.

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ODSC - Open Data Science

MAY 10, 2023

Unfortunately, even the data science industry — which should recognize tabular data’s true value — often underestimates its relevance in AI. Many mistakenly equate tabular data with business intelligence rather than AI, leading to a dismissive attitude toward its sophistication.

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Mlearning.ai

NOVEMBER 29, 2023

All the previously, recently, and currently collected data is used as input for time series forecasting where future trends, seasonal changes, irregularities, and such are elaborated based on complex math-driven algorithms. This results in quite efficient sales data predictions. In its core, lie gradient-boosted decision trees.

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The MLOps Blog

JUNE 27, 2023

Learn more The Best Tools, Libraries, Frameworks and Methodologies that ML Teams Actually Use – Things We Learned from 41 ML Startups [ROUNDUP] Key use cases and/or user journeys Identify the main business problems and the data scientist’s needs that you want to solve with ML, and choose a tool that can handle them effectively.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

An evaluation is a task used to measure the quality and responsibility of output of an LLM or generative AI service. Based on this tenet, we can classify generative AI users who need LLM evaluation capabilities into 3 groups as shown in the following figure: model providers, fine-tuners, and consumers.

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The MLOps Blog

MAY 9, 2023

An ML platform is an environment that standardizes the technology stack for your ML/AI team and provides tools, libraries, and infrastructure for developing, deploying, and operationalizing machine learning applications. They include: 1 Data (or input) pipeline. 2 Model (or training) pipeline. Data preprocessing.

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