Iguazio

DECEMBER 18, 2023

For data science practitioners, productization is key, just like any other AI or ML technology. However, it's important to contextualize generative AI within the broader landscape of AI and ML technologies. By thinking about the ML process in advance: preparing, managing, and versioning data, reusing components, etc.,

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AWS Machine Learning Blog

DECEMBER 18, 2024

With a vision to build a large language model (LLM) trained on Italian data, Fastweb embarked on a journey to make this powerful AI capability available to third parties. To further enrich the dataset, Fastweb generated synthetic Italian data using LLMs.

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DagsHub

AUGUST 29, 2024

Source: Author Introduction Machine learning (ML) models, like other software, are constantly changing and evolving. Version control systems (VCS) play a key role in this area by offering a structured method to track changes made to models and handle versions of data and code used in these ML projects.

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Iguazio

DECEMBER 18, 2023

For data science practitioners, productization is key, just like any other AI or ML technology. However, it's important to contextualize generative AI within the broader landscape of AI and ML technologies. By thinking about the ML process in advance: preparing, managing, and versioning data, reusing components, etc.,

ML 52

ML ML AI AI 52

Dataconomy

MAY 16, 2025

A 2025 landscape analysis shows ApacheKafka , Flink , and Iceberg moving from niche tools to fundamental parts of modern data architecture, underscoring how ubiquitous realtime expectations have become. Tight ML integration. Define behavioral events, latency targets, and compliance guardrails up front. Transformation discipline.

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AWS Machine Learning Blog

OCTOBER 23, 2024

At its core, Amazon Bedrock provides the foundational infrastructure for robust performance, security, and scalability for deploying machine learning (ML) models. The serverless infrastructure of Amazon Bedrock manages the execution of ML models, resulting in a scalable and reliable application.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

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O'Reilly Media

JUNE 14, 2024

It offers a wealth of books, on-demand courses, live events, short-form posts, interactive labs, expert playlists, and more—formed from the proprietary content of thousands of independent authors, industry experts, and several of the largest education publishers in the world.

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AWS Machine Learning Blog

NOVEMBER 1, 2023

Organizations can search for PII using methods such as keyword searches, pattern matching, data loss prevention tools, machine learning (ML), metadata analysis, data classification software, optical character recognition (OCR), document fingerprinting, and encryption.

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AWS Machine Learning Blog

APRIL 12, 2023

The PdMS includes AWS services to securely manage the lifecycle of edge compute devices and BHS assets, cloud data ingestion, storage, machine learning (ML) inference models, and business logic to power proactive equipment maintenance in the cloud. It’s an easy way to run analytics on IoT data to gain accurate insights.

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Flipboard

NOVEMBER 24, 2023

Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline. He is the author of the upcoming book “What’s Your Problem?” Clay Elmore is an AI/ML Specialist Solutions Architect at AWS. aimed at guiding new product managers through the product management career.

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Iguazio

FEBRUARY 8, 2024

To read more about LLMOps and MLOps, checkout the O’Reilly book “Implementing MLOps in the Enterprise” , authored by Iguazio ’s CTO and co-founder Yaron Haviv and by Noah Gift. They are characterized by their enormous size, complexity, and the vast amount of data they process. Continuous monitoring of resources, data, and metrics.

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AWS Machine Learning Blog

JANUARY 25, 2023

Model explainability refers to the process of relating the prediction of a machine learning (ML) model to the input feature values of an instance in humanly understandable terms. Amazon SageMaker Clarify is a feature of Amazon SageMaker that enables data scientists and ML engineers to explain the predictions of their ML models.

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Algorithm Natural Language Processing Machine Learning Machine Learning 94

IBM Journey to AI blog

JULY 27, 2023

A data lakehouse with multiple query engines and storage can allow engineers to share data in open formats. Additionally, engineers can cleanse, transform and standardize data for AI/ML modeling without duplicating or building additional pipelines.

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IBM Journey to AI blog

DECEMBER 16, 2022

You have a specific book in mind, but you have no idea where to find it. You enter the title of the book into the computer and the library’s digital inventory system tells you the exact section and aisle where the book is located.

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IBM Journey to AI blog

AUGUST 23, 2023

Data governance for LLMs The best breakdown of LLM architecture I’ve seen comes from this article by a16z (image below). is an enterprise-ready studio, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models.

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The MLOps Blog

FEBRUARY 27, 2025

TL;DR Bias is inherent to building a ML model. Adhering to data protection laws is not as complex if we focus less on the internal structure of the algorithms and more on the practical contexts of use. Using encryption protocols like HTTPS and TLS to safeguard data in transit. Bias exists on a spectrum.

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Flipboard

APRIL 15, 2025

Incorporate real system constraints: Ground your synthetic data in actual system limitations and data. For example, when testing a scheduling feature, use real availability windows and booking rules. Verify scenario coverage: Ensure your generated data actually triggers the scenarios you want to test.

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phData

SEPTEMBER 27, 2024

Activity Schema Modeling: Capturing the Customer Journey in Action Now that we’ve got our Lego blocks of customer data, let’s talk about another game-changing approach that’s shaking up the world of customer data modeling: Activity Schema Modeling. Your customer data game will never be the same.

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Flipboard

JUNE 16, 2025

If undecided, begin with DSA and a versatile language like Python, which can later transition into ML or web development. Data Tools Excel remains a fundamental tool for basic analysis, while Tableau, Power BI, Qlik Sense and QlikView offer advanced visualisation and business intelligence capabilities. MongoDB, Cassandra) databases.

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