AWS Machine Learning Blog

OCTOBER 16, 2024

Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. Identify areas of interest We begin by illustrating how SageMaker can be applied to analyze geospatial data at a global scale.

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Towards AI

APRIL 10, 2024

How much machine learning really is in ML Engineering? There are so many different data- and machine-learning-related jobs. But what actually are the differences between a Data Engineer, Data Scientist, ML Engineer, Research Engineer, Research Scientist, or an Applied Scientist?!

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AWS Machine Learning Blog

OCTOBER 20, 2023

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.

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Flipboard

NOVEMBER 24, 2023

In an increasingly digital and rapidly changing world, BMW Group’s business and product development strategies rely heavily on data-driven decision-making. With that, the need for data scientists and machine learning (ML) engineers has grown significantly.

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AWS Machine Learning Blog

OCTOBER 24, 2024

Machine learning (ML) helps organizations to increase revenue, drive business growth, and reduce costs by optimizing core business functions such as supply and demand forecasting, customer churn prediction, credit risk scoring, pricing, predicting late shipments, and many others. Let’s learn about the services we will use to make this happen.

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Eugene Yan

JANUARY 16, 2019

What's the difference between a data scientist, data engineer, and ML engineer? A panel at Google.

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AWS Machine Learning Blog

DECEMBER 4, 2024

Businesses are under pressure to show return on investment (ROI) from AI use cases, whether predictive machine learning (ML) or generative AI. Only 54% of ML prototypes make it to production, and only 5% of generative AI use cases make it to production. Using SageMaker, you can build, train and deploy ML models.

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IBM Data Science in Practice

MARCH 8, 2023

The growth of the AI and Machine Learning (ML) industry has continued to grow at a rapid rate over recent years. Hidden Technical Debt in Machine Learning Systems More money, more problems — Rise of too many ML tools 2012 vs 2023 — Source: Matt Turck People often believe that money is the solution to a problem.

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AWS Machine Learning Blog

AUGUST 12, 2024

Instead, organizations are increasingly looking to take advantage of transformative technologies like machine learning (ML) and artificial intelligence (AI) to deliver innovative products, improve outcomes, and gain operational efficiencies at scale. Data is presented to the personas that need access using a unified interface.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

Data exploration and model development were conducted using well-known machine learning (ML) tools such as Jupyter or Apache Zeppelin notebooks. Apache Hive was used to provide a tabular interface to data stored in HDFS, and to integrate with Apache Spark SQL. This also led to a backlog of data that needed to be ingested.

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Data Science Dojo

FEBRUARY 20, 2023

Machine learning (ML) is the technology that automates tasks and provides insights. It allows data scientists to build models that can automate specific tasks. It comes in many forms, with a range of tools and platforms designed to make working with ML more efficient. It also has ML algorithms built into the platform.

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AWS Machine Learning Blog

APRIL 26, 2023

Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? Most executives think ML can apply to any business decision, but on average only half of the ML projects make it to production. Challenges Customers may face several challenges when implementing machine learning (ML) solutions.

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Towards AI

JULY 1, 2024

Learn the basics of data engineering to improve your ML modelsPhoto by Mike Benna on Unsplash It is not news that developing Machine Learning algorithms requires data, often a lot of data. Collecting this data is not trivial, in fact, it is one of the most relevant and difficult parts of the entire workflow.

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AWS Machine Learning Blog

SEPTEMBER 20, 2023

In these scenarios, as you start to embrace generative AI, large language models (LLMs) and machine learning (ML) technologies as a core part of your business, you may be looking for options to take advantage of AWS AI and ML capabilities outside of AWS in a multicloud environment.

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How to Learn Machine Learning

APRIL 26, 2025

The field of data science is now one of the most preferred and lucrative career options available in the area of data because of the increasing dependence on data for decision-making in businesses, which makes the demand for data science hires peak. And Why did it happen?). or What might be the best course of action?

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The MLOps Blog

DECEMBER 28, 2022

The onset of the pandemic has triggered a rapid increase in the demand and adoption of ML technology. Building ML team Following the surge in ML use cases that have the potential to transform business, the leaders are making a significant investment in ML collaboration, building teams that can deliver the promise of machine learning.

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Applied Data Science

AUGUST 2, 2021

Team Building the right data science team is complex. With a range of role types available, how do you find the perfect balance of Data Scientists , Data Engineers and Data Analysts to include in your team? The Data Engineer Not everyone working on a data science project is a data scientist.

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Dataconomy

MAY 2, 2023

The machine learning systems developed by Machine Learning Engineers are crucial components used across various big data jobs in the data processing pipeline. Additionally, Machine Learning Engineers are proficient in implementing AI or ML algorithms. Is ML engineering a stressful job?

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Hacker News

JULY 18, 2024

ABOUT EVENTUAL Eventual is a data platform that helps data scientists and engineers build data applications across ETL, analytics and ML/AI. OUR PRODUCT IS OPEN-SOURCE AND USED AT ENTERPRISE SCALE Our distributed data engine Daft [link] is open-sourced and runs on 800k CPU cores daily.

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AWS Machine Learning Blog

SEPTEMBER 19, 2023

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. SageMaker Studio set up.

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Data Science Dojo

APRIL 16, 2024

Machine learning (ML) engineer Potential pay range – US$82,000 to 160,000/yr Machine learning engineers are the bridge between data science and engineering. Integrating the knowledge of data science with engineering skills, they can design, build, and deploy machine learning (ML) models.

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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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AWS Machine Learning Blog

APRIL 19, 2023

Businesses are increasingly using machine learning (ML) to make near-real-time decisions, such as placing an ad, assigning a driver, recommending a product, or even dynamically pricing products and services. As a result, some enterprises have spent millions of dollars inventing their own proprietary infrastructure for feature management.

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ODSC - Open Data Science

FEBRUARY 2, 2023

The role of a data scientist is in demand and 2023 will be no exception. To get a better grip on those changes we reviewed over 25,000 data scientist job descriptions from that past year to find out what employers are looking for in 2023. Data Science Of course, a data scientist should know data science!

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Dataconomy

MAY 8, 2025

As organizations increasingly rely on ML for decision-making, the need for robust monitoring practices has never been more significant. Open-source machine learning monitoring (OSMLM) encompasses the systems and methods aimed at overseeing and optimizing ML models that have been deployed.

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AWS Machine Learning Blog

JANUARY 10, 2024

Specialist Data Engineering at Merck, and Prabakaran Mathaiyan, Sr. ML Engineer at Tiger Analytics. The large machine learning (ML) model development lifecycle requires a scalable model release process similar to that of software development. This post is co-written with Jayadeep Pabbisetty, Sr.

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phData

JUNE 25, 2024

Snowflake Summit 2024 launched numerous features and enhancements targeted at data scientists’ workflows and developer experience. By adopting more of Snowflake’s functionality for data science, organizations have an opportunity to greatly accelerate AI/ML application development.

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The MLOps Blog

JANUARY 26, 2024

TL;DR Feedback integration is crucial for ML models to meet user needs. A robust ML infrastructure gives teams a competitive advantage. I started my ML journey as an analyst back in 2016. Mailchimp’s ML Platform: genesis, challenges, and objectives Mailchimp is a 20-year-old bootstrapped email marketing company.

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Mlearning.ai

FEBRUARY 9, 2023

Working as a Data Scientist — Expectation versus Reality! 11 key differences in 2023 Photo by Jan Tinneberg on Unsplash Working in Data Science and Machine Learning (ML) professions can be a lot different from the expectation of it. In courses/projects, it is common to have data available.

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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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Pickl AI

DECEMBER 25, 2024

Key Takeaways Business Analytics targets historical insights; Data Science excels in prediction and automation. Business Analytics requires business acumen; Data Science demands technical expertise in coding and ML. With added skills, professionals can shift between Business Analytics and Data Science. Masters or Ph.D.

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Mlearning.ai

MAY 27, 2023

Why data scientists and analysts ought to have a working knowledge of software engineering in Python? There are several good reasons why data scientists and analysts, particularly Python, need a solid grounding in software engineering ideas and techniques.

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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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Flipboard

DECEMBER 11, 2024

Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. Data engineers use data warehouses, data lakes, and analytics tools to load, transform, clean, and aggregate data.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

The ZMP analyzes billions of structured and unstructured data points to predict consumer intent by using sophisticated artificial intelligence (AI) to personalize experiences at scale. Hosted on Amazon ECS with tasks run on Fargate, this platform streamlines the end-to-end ML workflow, from data ingestion to model deployment.

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AWS Machine Learning Blog

AUGUST 31, 2023

As Artificial Intelligence (AI) and Machine Learning (ML) technologies have become mainstream, many enterprises have been successful in building critical business applications powered by ML models at scale in production.

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AWS Machine Learning Blog

JUNE 16, 2023

SambaSafety’s team of data scientists has developed complex and propriety modeling solutions designed to accurately quantify this risk profile. With AWS CI/CD and AI/ML products, SambaSafety’s data science team didn’t have to change their existing development workflow to take advantage of continuous model training and inference.

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