AWS Machine Learning Blog

APRIL 29, 2024

In 2024, however, organizations are using large language models (LLMs), which require relatively little focus on NLP, shifting research and development from modeling to the infrastructure needed to support LLM workflows. Metaflow’s coherent APIs simplify the process of building real-world ML/AI systems in teams.

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Women in Big Data

APRIL 4, 2025

During the Keynote talk Responsible AI @ Kumo AI , Hema Raghavan (Kumo AI Co-Founder & Head of Engineering) showcased platform solutions that make machine learning on relational data simple, performant, and scalable.

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Mlearning.ai

NOVEMBER 29, 2023

How to Use Machine Learning (ML) for Time Series Forecasting — NIX United The modern market pace calls for a respective competitive edge. Data forecasting has come a long way since formidable data processing-boosting technologies such as machine learning were introduced.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

AutoML allows you to derive rapid, general insights from your data right at the beginning of a machine learning (ML) project lifecycle. Understanding up front which preprocessing techniques and algorithm types provide best results reduces the time to develop, train, and deploy the right model. replace("_", "-").replace("script",

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AWS Machine Learning Blog

NOVEMBER 10, 2023

Creating high-performance machine learning (ML) solutions relies on exploring and optimizing training parameters, also known as hyperparameters. It provides key functionality that allows you to focus on the ML problem at hand while automatically keeping track of the trials and results. We use a Random Forest from SkLearn.

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Alation

NOVEMBER 4, 2021

However, I paused and asked myself: What is the value that customers of Alation actually got for non-compliance or data security use cases? The value of the data catalog depends on the audience. For data modelers, value arose from spending less time finding data and more time modeling data.

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DataRobot Blog

OCTOBER 11, 2022

Want to learn how AI/ML can be so effective in this space? So how can AI/ML help McLaren Formula 1 Team, one of the sports oldest and most successful teams, in this space? The How – Data, Modeling, and Predictions! Racing Data Summary. Are you new to Formula 1? Let’s begin! And what are the stakes? Learn More.

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Tableau

OCTOBER 8, 2021

With its LookML modeling language, Looker provides a unique, modern approach to define governed and reusable data models to build a trusted foundation for analytics. Additionally, Tableau allows customers using BigQuery ML to easily visualize the results of predictive machine learning models run on data stored in BigQuery.

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AWS Machine Learning Blog

JUNE 5, 2023

Hyperparameter overview When training any machine learning (ML) model, you are generally dealing with three types of data: input data (also called the training data), model parameters, and hyperparameters. You use the input data to train your model, which in effect learns your model parameters.

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AWS Machine Learning Blog

MARCH 18, 2025

Embedding is usually performed by a machine learning (ML) model. The language model then generates a SQL query that incorporates the enterprise knowledge. Streamlit This open source Python library makes it straightforward to create and share beautiful, custom web apps for ML and data science.

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The MLOps Blog

JANUARY 23, 2023

Machine Learning Operations (MLOps): Overview, Definition, and Architecture” By Dominik Kreuzberger, Niklas Kühl, Sebastian Hirschl Great stuff. If you haven’t read it yet, definitely do so. Came to ML from software. I don’t see what special role ML and MLOps engineers would play here. –

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ML @ CMU

MAY 12, 2023

Graphs generally complicate DP : we are often used to ML settings where we can clearly define the privacy granularity and how it relates to an actual individual (e.g. Privacy definition: There are a small number of clients, but each holds many data subjects, and client-level DP isn’t suitable. medical images of patients).

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Snorkel AI

MARCH 16, 2023

Today, I will be introducing you to LandingLens, our main product, and taking you through the journey we went through in developing that product and the data-centric approaches we’ve incorporated into the platform. The third objective is to help our customers get the most from their existing ML platforms.

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Snorkel AI

MARCH 16, 2023

Today, I will be introducing you to LandingLens, our main product, and taking you through the journey we went through in developing that product and the data-centric approaches we’ve incorporated into the platform. The third objective is to help our customers get the most from their existing ML platforms.

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Iguazio

JANUARY 22, 2024

Definitions: Foundation Models, Gen AI, and LLMs Before diving into the practice of productizing LLMs, let’s review the basic definitions of GenAI elements: Foundation Models (FMs) - Large deep learning models that are pre-trained with attention mechanisms on massive datasets.

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AWS Machine Learning Blog

AUGUST 21, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. The capabilities of Lake Formation simplify securing and managing distributed data lakes across multiple accounts through a centralized approach, providing fine-grained access control.

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AWS Machine Learning Blog

OCTOBER 16, 2024

With the power of advanced language models and machine learning (ML) algorithms, generative AI can understand the context and intent behind a programmer’s code, offering valuable suggestions, completing code snippets, and even generating entire functions or modules based on high-level descriptions.

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Tableau

OCTOBER 8, 2021

With its LookML modeling language, Looker provides a unique, modern approach to define governed and reusable data models to build a trusted foundation for analytics. Additionally, Tableau allows customers using BigQuery ML to easily visualize the results of predictive machine learning models run on data stored in BigQuery.

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DagsHub

DECEMBER 5, 2023

This is where ML experiment tracking comes into play! What is ML Experiment Tracking? ML experiment tracking is the process of recording, organizing, and analyzing the results of ML experiments. It helps data scientists keep track of their experiments, reproduce their results, and collaborate with others effectively.

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Mlearning.ai

JUNE 16, 2023

Generative AI can be used to automate the data modeling process by generating entity-relationship diagrams or other types of data models and assist in UI design process by generating wireframes or high-fidelity mockups. diagram Using ChatGPT to build system diagrams — Part II Generate C4 diagrams using mermaid.js

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IBM Journey to AI blog

SEPTEMBER 5, 2024

Access to high-quality data can help organizations start successful products, defend against digital attacks, understand failures and pivot toward success. Emerging technologies and trends, such as machine learning (ML), artificial intelligence (AI), automation and generative AI (gen AI), all rely on good data quality.

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Pickl AI

JUNE 11, 2024

They offer a focused selection of data, allowing for faster analysis tailored to departmental goals. Metadata This acts like the data dictionary, providing crucial information about the data itself. Metadata details the source of the data, its definition, and how it relates to other data points within the warehouse.

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The MLOps Blog

APRIL 17, 2023

As an MLOps engineer on your team, you are often tasked with improving the workflow of your data scientists by adding capabilities to your ML platform or by building standalone tools for them to use. And since you are reading this article, the data scientists you support have probably reached out for help.

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DagsHub

OCTOBER 23, 2024

Managing unstructured data is essential for the success of machine learning (ML) projects. Without structure, data is difficult to analyze and extracting meaningful insights and patterns is challenging. This article will discuss managing unstructured data for AI and ML projects. What is Unstructured Data?

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Mlearning.ai

FEBRUARY 13, 2023

where each word represents a key and each definition represents a value. These databases are designed for fast data retrieval and are ideal for applications that require quick data access and low latency, such as caching, session management, and real-time analytics. Cassandra and HBase are widely used with IoT 6.

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Heartbeat

DECEMBER 7, 2023

Data should be designed to be easily accessed, discovered, and consumed by other teams or users without requiring significant support or intervention from the team that created it. Data should be created using standardized data models, definitions, and quality requirements.

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ML Review

FEBRUARY 26, 2018

Since intentions determine the subsequent domain identification flow, the intention stratum is a necessary first step in initiating contextual and domain data model processes. Efforts in gathering data and refining knowledge graphs will contribute to IHCI’s development. AAAI Press, 2014: 1586–1592.

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Heartbeat

MARCH 21, 2023

In the context of time series, model monitoring is particularly important as time series data can be highly dynamic because change is definite over time in ways that can impact the accuracy of the model. We’re committed to supporting and inspiring developers and engineers from all walks of life.

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Pickl AI

OCTOBER 16, 2024

Data Model : RDBMS relies on a structured schema with predefined relationships among tables, whereas NoSQL databases use flexible data models (e.g., key-value pairs, document-based) that accommodate unstructured data. Scalability : RDBMS typically scales vertically by adding more resources to a single server.

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DrivenData Labs

MARCH 30, 2023

The financial crime detection track definitely fell in that category! Summary of approach : In our solution, the financial transaction messaging system and its network of banks jointly extract feature values to improve the utility of a machine learning model for anomalous payment detection.

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phData

JULY 18, 2023

Why Migrate to a Modern Data Stack? Data teams can focus on delivering higher-value data tasks with better organizational visibility. Move Beyond One-off Analytics: The Modern Data Stack empowers you to elevate your data for advanced analytics and integration of AI/ML, enabling faster generation of actionable business insights.

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Explosion

JUNE 18, 2021

How implement models ML fundamentals training and evaluation improve accuracy use library APIs Python and DevOps What when to use ML decide what models and components to train understand what application will use outputs for find best trade-offs select resources and libraries The “how” is everything that helps you execute the plan.

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The MLOps Blog

OCTOBER 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. Nice to have you here, Miki.

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Mlearning.ai

FEBRUARY 24, 2023

.)+ geom_line(aes(x=Datum, y=cum, col="Cumulative Sum"))+ labs(col="Outcome", x="Date", y="Value") Clearly you can see a change in derivatives — the growth never stop but the slope of change definitely differs across time. Lets add temperature as well, but stick with the daily data for now. AIC: 5879909.05

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phData

SEPTEMBER 27, 2024

Introduction: The Customer Data Modeling Dilemma You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? For years, we’ve been obsessed with creating these grand, top-down customer data models. Yeah, that one.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

Amazon SageMaker MLOps lifecycle As the post “ MLOps foundation roadmap for enterprises with Amazon SageMaker ” describes, MLOps is the combination of processes, people, and technology to productionise ML use cases efficiently. Deployment of Amazon SageMaker Pipelines relies on repository interactions and CI/CD pipeline activation.

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