AWS Machine Learning Blog

APRIL 3, 2025

Amazon AppFlow was used to facilitate the smooth and secure transfer of data from various sources into ODAP. Additionally, Amazon Simple Storage Service (Amazon S3) served as the central data lake, providing a scalable and cost-effective storage solution for the diverse data types collected from different systems.

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Flipboard

MAY 14, 2025

Their information is split between two types of data: unstructured data (such as PDFs, HTML pages, and documents) and structured data (such as databases, data lakes, and real-time reports). Different types of data typically require different tools to access them. Traditionally, businesses face a challenge.

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AWS Machine Learning Blog

AUGUST 15, 2024

With over 50 connectors, an intuitive Chat for data prep interface, and petabyte support, SageMaker Canvas provides a scalable, low-code/no-code (LCNC) ML solution for handling real-world, enterprise use cases. Organizations often struggle to extract meaningful insights and value from their ever-growing volume of data.

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AWS Machine Learning Blog

AUGUST 8, 2024

As one of the largest AWS customers, Twilio engages with data, artificial intelligence (AI), and machine learning (ML) services to run their daily workloads. Data is the foundational layer for all generative AI and ML applications. The following diagram illustrates the solution architecture.

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ODSC - Open Data Science

JUNE 7, 2023

To make your data management processes easier, here’s a primer on data lakes, and our picks for a few data lake vendors worth considering. What is a data lake? First, a data lake is a centralized repository that allows users or an organization to store and analyze large volumes of data.

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ODSC - Open Data Science

APRIL 27, 2023

5 Concerns for ML Safety in the Era of LLMs and Generative AI The growth of large language models and generative AI has spurred new concerns for ML safety and cybersecurity. 5 Data Engineering and Data Science Cloud Options for 2023 AI development is incredibly resource intensive.

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AWS Machine Learning Blog

MARCH 1, 2023

This combination of great models and continuous adaptation is what will lead to a successful machine learning (ML) strategy. MLOps focuses on the intersection of data science and data engineering in combination with existing DevOps practices to streamline model delivery across the ML development lifecycle.

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Flipboard

NOVEMBER 17, 2023

Amazon SageMaker enables enterprises to build, train, and deploy machine learning (ML) models. Amazon SageMaker JumpStart provides pre-trained models and data to help you get started with ML. MongoDB vector data store MongoDB Atlas Vector Search is a new feature that allows you to store and search vector data in MongoDB.

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AWS Machine Learning Blog

FEBRUARY 7, 2025

This post, part of the Governing the ML lifecycle at scale series ( Part 1 , Part 2 , Part 3 ), explains how to set up and govern a multi-account ML platform that addresses these challenges. An enterprise might have the following roles involved in the ML lifecycles. This ML platform provides several key benefits.

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Pickl AI

APRIL 6, 2023

Accordingly, one of the most demanding roles is that of Azure Data Engineer Jobs that you might be interested in. The following blog will help you know about the Azure Data Engineering Job Description, salary, and certification course. How to Become an Azure Data Engineer?

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AWS Machine Learning Blog

JUNE 5, 2023

Data is the foundation for machine learning (ML) algorithms. One of the most common formats for storing large amounts of data is Apache Parquet due to its compact and highly efficient format. Athena allows applications to use standard SQL to query massive amounts of data on an S3 data lake.

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Flipboard

DECEMBER 18, 2023

Customers use Amazon Redshift as a key component of their data architecture to drive use cases from typical dashboarding to self-service analytics, real-time analytics, machine learning (ML), data sharing and monetization, and more. Discover how you can use Amazon Redshift to build a data mesh architecture to analyze your data.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

Previously, he was a Data & Machine Learning Engineer at AWS, where he worked closely with customers to develop enterprise-scale data infrastructure, including data lakes, analytics dashboards, and ETL pipelines. He specializes in designing, building, and optimizing large-scale data solutions.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

ML operationalization summary As defined in the post MLOps foundation roadmap for enterprises with Amazon SageMaker , ML and operations (MLOps) is the combination of people, processes, and technology to productionize machine learning (ML) solutions efficiently.

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AWS Machine Learning Blog

JULY 3, 2025

To achieve the desired accuracy in KPI calculations, the data pipeline was refined to achieve consistent and precise performance, which leads to meaningful insights. At this point, it became possible for the calculator agent to forego the Pandas or Spark data processing implementation.

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AWS Machine Learning Blog

FEBRUARY 13, 2024

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. Their task is to construct and oversee efficient data pipelines.

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The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. and Pandas or Apache Spark DataFrames.

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ODSC - Open Data Science

FEBRUARY 2, 2023

Just as a writer needs to know core skills like sentence structure, grammar, and so on, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and so on. They’re looking for people who know all related skills, and have studied computer science and software engineering.

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The MLOps Blog

MAY 17, 2023

From data processing to quick insights, robust pipelines are a must for any ML system. Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. However, efficient use of ETL pipelines in ML can help make their life much easier.

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phData

SEPTEMBER 20, 2023

On the client side, Snowpark consists of libraries, including the DataFrame API and native Snowpark machine learning (ML) APIs for model development (public preview) and deployment (private preview). phData has been working in data engineering since the inception of the company back in 2015.

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The MLOps Blog

JANUARY 23, 2023

However, there are some key differences that we need to consider: Size and complexity of the data In machine learning, we are often working with much larger data. Basically, every machine learning project needs data. Given the range of tools and data types, a separate data versioning logic will be necessary.

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AWS Machine Learning Blog

JUNE 18, 2024

Despite the challenges, Afri-SET, with limited resources, envisions a comprehensive data management solution for stakeholders seeking sensor hosting on their platform, aiming to deliver accurate data from low-cost sensors. This happens only when a new data format is detected to avoid overburdening scarce Afri-SET resources.

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AWS Machine Learning Blog

DECEMBER 18, 2023

By using these capabilities, businesses can efficiently store, manage, and analyze time-series data, enabling data-driven decisions and gaining a competitive edge. If you need an automated workflow or direct ML model integration into apps, Canvas forecasting functions are accessible through APIs.

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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AWS Machine Learning Blog

JUNE 21, 2024

eSentire has over 2 TB of signal data stored in their Amazon Simple Storage Service (Amazon S3) data lake. This further step updates the FM by training with data labeled by security experts (such as Q&A pairs and investigation conclusions).

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ODSC - Open Data Science

MARCH 30, 2023

Using Azure ML to Train a Serengeti Data Model, Fast Option Pricing with DL, and How To Connect a GPU to a Container Using Azure ML to Train a Serengeti Data Model for Animal Identification In this article, we will cover how you can train a model using Notebooks in Azure Machine Learning Studio.

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AWS Machine Learning Blog

DECEMBER 7, 2023

Amazon Forecast is a fully managed service that uses machine learning (ML) algorithms to deliver highly accurate time series forecasts. In this post, we describe how we reduced the modelling time by 70% by doing the feature engineering and modelling using Amazon Forecast. He loves combining open-source projects with cloud services.

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phData

NOVEMBER 1, 2024

Chief Information Officer, Legal Industry Survey respondents noted improved data quality and compliance and risk management as the top two outcomes for organizations with a focus on more standardized data controls when working to implement GenAI and LLM applications.

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AWS Machine Learning Blog

MAY 15, 2023

Getir used Amazon Forecast , a fully managed service that uses machine learning (ML) algorithms to deliver highly accurate time series forecasts, to increase revenue by four percent and reduce waste cost by 50 percent. Mutlu Polatcan is a Staff Data Engineer at Getir, specializing in designing and building cloud-native data platforms.

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IBM Journey to AI blog

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. An AI governance framework ensures the ethical, responsible and transparent use of AI and machine learning (ML). The development and use of these models explain the enormous amount of recent AI breakthroughs.

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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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The MLOps Blog

MARCH 21, 2023

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

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ODSC - Open Data Science

OCTOBER 20, 2023

You’ll use MLRun, Langchain, and Milvus for this exercise and cover topics like the integration of AI/ML applications, leveraging Python SDKs, as well as building, testing, and tuning your work. In this session, we’ll demonstrate how you can fine-tune a Gen AI model, build a Gen AI application, and deploy it in 20 minutes.

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IBM Journey to AI blog

JULY 27, 2023

Powering a knowledge management system with a data lakehouse Organizations need a data lakehouse to target data challenges that come with deploying an AI-powered knowledge management system. It provides the combination of data lake flexibility and data warehouse performance to help to scale AI.

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IBM Journey to AI blog

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. IBM watsonx.ai With watsonx.ai, businesses can effectively train, validate, tune and deploy AI models with confidence and at scale across their enterprise.

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Alation

JUNE 21, 2022

Over time, we called the “thing” a data catalog , blending the Google-style, AI/ML-based relevancy with more Yahoo-style manual curation and wikis. Thus was born the data catalog. In our early days, “people” largely meant data analysts and business analysts. ML and DataOps teams).

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