AWS Machine Learning Blog

DECEMBER 12, 2024

The new SDK is designed with a tiered user experience in mind, where the new lower-level SDK ( SageMaker Core ) provides access to full breadth of SageMaker features and configurations, allowing for greater flexibility and control for ML engineers. In the following example, we show how to fine-tune the latest Meta Llama 3.1

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Flipboard

JANUARY 19, 2025

How to Pick Between Data Science, Data Analytics, Data Engineering, ML Engineering, and SW Engineering How to Pick Between Data Science, Data

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Flipboard

MAY 31, 2023

Learn how to easily build any AI model and customize your own LLM in just a few lines of code with a declarative approach to machine learning.

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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. SageMaker Processing provisions cluster resources for you to run city-, country-, or continent-scale geospatial ML workloads.

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AWS Machine Learning Blog

OCTOBER 22, 2024

Amazon SageMaker is a cloud-based machine learning (ML) platform within the AWS ecosystem that offers developers a seamless and convenient way to build, train, and deploy ML models. He focuses on architecting and implementing large-scale generative AI and classic ML pipeline solutions.

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Towards AI

JULY 2, 2024

What are the most important skills for an ML Engineer? Well, I asked ML engineers at all these companies to share what they consider the top skills… And I’m telling you, there were a lot of answers I received and I bet you didn’t even think of many of them!

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Unite.AI

FEBRUARY 12, 2025

Odoo has been exploring machine learning to enhance its operations for instance, using AI for demand forecasting and intelligent scheduling. AI-Driven Forecasting: Machine learning features for demand forecasting and production optimization, helping predict needs and equipment issues before they arise. Visit Odoo 4.

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AI News

MARCH 8, 2024

Hugging Face , the startup behind the popular open source machine learning codebase and ChatGPT rival Hugging Chat, is venturing into new territory with the launch of an open robotics project. Until now, Hugging Face has primarily focused on software offerings like its machine learning codebase and open-source chatbot.

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Towards AI

APRIL 4, 2023

Last Updated on April 4, 2023 by Editorial Team Introducing a Python SDK that allows enterprises to effortlessly optimize their ML models for edge devices. With their groundbreaking web-based Studio platform, engineers have been able to collect data, develop and tune ML models, and deploy them to devices.

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Towards AI

FEBRUARY 9, 2025

Lets understand the most useful linear feature scaling techniques of Machine Learning (ML) in detail! Since all ML models expect numeric input, it doesnt signify that passing the numeric features as they are fulfills the use case. This member-only story is on us. Upgrade to access all of Medium.

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Towards AI

FEBRUARY 21, 2023

We observe that the main agents at the moment for AI progression are people working in machine learning as engineers and researchers. A sensible proxy sub-question might then be: Can ChatGPT function as a competent machine learning engineer? ChatGPT’s job as our ML engineer […]

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Analytics Vidhya

OCTOBER 6, 2023

Ray streamlines complex tasks for ML engineers, data scientists, and developers. Its versatility spans data processing, model training, hyperparameter tuning, deployment, and reinforcement learning. Python Ray is a dynamic framework revolutionizing distributed computing.

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Analytics Vidhya

FEBRUARY 21, 2022

Image designed by the author – Shanthababu Introduction Every ML Engineer and Data Scientist must understand the significance of “Hyperparameter Tuning (HPs-T)” while selecting your right machine/deep learning model and improving the performance of the model(s). Make it simple, for every […].

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Flipboard

SEPTEMBER 4, 2023

Home Table of Contents Getting Started with Docker for Machine Learning Overview: Why the Need? How Do Containers Differ from Virtual Machines? Finally, we will top it off by installing Docker on our local machine with simple and easy-to-follow steps. How Do Containers Differ from Virtual Machines?

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AWS Machine Learning Blog

DECEMBER 19, 2023

Artificial intelligence (AI) and machine learning (ML) are becoming an integral part of systems and processes, enabling decisions in real time, thereby driving top and bottom-line improvements across organizations. However, putting an ML model into production at scale is challenging and requires a set of best practices.

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Marktechpost

OCTOBER 10, 2024

In today’s tech-driven world, data science and machine learning are often used interchangeably. This article explores the differences between data science vs. machine learning , highlighting their key functions, roles, and applications. What is Machine Learning? However, they represent distinct fields.

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IBM Journey to AI blog

AUGUST 12, 2024

Instead, businesses tend to rely on advanced tools and strategies—namely artificial intelligence for IT operations (AIOps) and machine learning operations (MLOps)—to turn vast quantities of data into actionable insights that can improve IT decision-making and ultimately, the bottom line.

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Flipboard

FEBRUARY 13, 2025

AI/ML engineers would prefer to focus on model training and data engineering, but the reality is that we also need to understand the infrastructure and mechanics […]

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Flipboard

NOVEMBER 24, 2023

With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and ML engineers require capable tooling and sufficient compute for their work. Data scientists and ML engineers require capable tooling and sufficient compute for their work.

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Unite.AI

SEPTEMBER 3, 2024

Computational power has become a critical factor in pushing the boundaries of what's possible in machine learning. As models grow more complex and datasets expand exponentially, traditional CPU-based computing often falls short of meeting the demands of modern machine learning tasks.

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Marktechpost

SEPTEMBER 25, 2024

With the rapid advancement of technology, surpassing human abilities in tasks like image classification and language processing, evaluating the energy impact of ML is essential. Historically, ML projects prioritized accuracy over energy efficiency, contributing to increased energy consumption.

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Marktechpost

OCTOBER 12, 2024

Machine Learning (ML) models have shown promising results in various coding tasks, but there remains a gap in effectively benchmarking AI agents’ capabilities in ML engineering. MLE-bench is a novel benchmark aimed at evaluating how well AI agents can perform end-to-end machine learning engineering.

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AWS Machine Learning Blog

JANUARY 23, 2025

The solution described in this post is geared towards machine learning (ML) engineers and platform teams who are often responsible for managing and standardizing custom environments at scale across an organization. This approach helps you achieve machine learning (ML) governance, scalability, and standardization.

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TheSequence

NOVEMBER 7, 2024

But how good is AI in traditional machine learning(ML) engineering tasks such as training or validation. This is the purpose of a new work proposed by OpenAI with MLE-Bench, a benchmark to evaluate AI agents in ML engineering tasks.

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Towards AI

JANUARY 28, 2025

Photo by Markus Winkler on Unsplash You might have wandered the internet for a complete roadmap to learn ML. You might have been flooded with tons of courses like Learn Machine Learning in 3 monthsMachine Learning SimplifiedLearn ML in 1 weekand there are several others like these.

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Towards AI

AUGUST 7, 2024

From Solo Notebooks to Collaborative Powerhouse: VS Code Extensions for Data Science and ML Teams Photo by Parabol | The Agile Meeting Toolbox on Unsplash In this article, we will explore the essential VS Code extensions that enhance productivity and collaboration for data scientists and machine learning (ML) engineers.

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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

OCTOBER 9, 2023

This lesson is the 2nd of a 3-part series on Docker for Machine Learning : Getting Started with Docker for Machine Learning Getting Used to Docker for Machine Learning (this tutorial) Lesson 3 To learn how to create a Docker Container for Machine Learning, just keep reading.

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AWS Machine Learning Blog

FEBRUARY 27, 2024

In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker. This is a guest post written by Axfood AB.

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Unite.AI

JUNE 29, 2023

Business leaders in today's tech and startup scene know the importance of mastering AI and machine learning. By tapping into AI and machine learning services offered by cloud providers, businesses can unlock fresh growth opportunities, automate their processes, and steer their cost-cutting initiatives.

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ODSC - Open Data Science

SEPTEMBER 21, 2023

The majority of us who work in machine learning, analytics, and related disciplines do so for organizations with a variety of different structures and motives. The following is an extract from Andrew McMahon’s book , Machine Learning Engineering with Python, Second Edition.

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AWS Machine Learning Blog

OCTOBER 28, 2024

Real-world applications vary in inference requirements for their artificial intelligence and machine learning (AI/ML) solutions to optimize performance and reduce costs. SageMaker Model Monitor monitors the quality of SageMaker ML models in production.

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AWS Machine Learning Blog

SEPTEMBER 18, 2023

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model.

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Marktechpost

JANUARY 31, 2024

Data scientists and ML engineers often need help to build full-stack applications. Still, they may need more skills or time to learn new languages or frameworks to create user-friendly web applications. It is a Python-based framework for data scientists and machine learning engineers.

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AWS Machine Learning Blog

FEBRUARY 11, 2025

Get started with SageMaker JumpStart SageMaker JumpStart is a machine learning (ML) hub that can help accelerate your ML journey. Marc Karp is an ML Architect with the Amazon SageMaker Service team. He focuses on helping customers design, deploy, and manage ML workloads at scale.

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Flipboard

JUNE 20, 2023

Machine learning (ML) engineers have traditionally focused on striking a balance between model training and deployment cost vs. performance. This is important because training ML models and then using the trained models to make predictions (inference) can be highly energy-intensive tasks.

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ODSC - Open Data Science

FEBRUARY 9, 2023

That responsibility usually falls in the hands of a role called Machine Learning (ML) Engineer. Having empathy for your ML Engineering colleagues means helping them meet operational constraints. To continue with this analogy, you might think of the ML Engineer as the data scientist’s “editor.”

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