AWS Machine Learning Blog

MAY 9, 2024

The rapid advancements in artificial intelligence and machine learning (AI/ML) have made these technologies a transformative force across industries. An effective approach that addresses a wide range of observed issues is the establishment of an AI/ML center of excellence (CoE). What is an AI/ML CoE?

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Towards AI

NOVEMBER 8, 2024

We don’t have better algorithms; we just have more data. Edited Photo by Taylor Vick on Unsplash In ML engineering, data quality isn’t just critical — it’s foundational. Yet, this perspective often gets sidelined and there was never a consensus in the ML community about it. That early obsession with algorithms was vital.

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IBM Journey to AI blog

AUGUST 12, 2024

AIOPs refers to the application of artificial intelligence (AI) and machine learning (ML) techniques to enhance and automate various aspects of IT operations (ITOps). ML technologies help computers achieve artificial intelligence. However, they differ fundamentally in their purpose and level of specialization in AI and ML environments.

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AWS Machine Learning Blog

JANUARY 28, 2025

FMEval is an open source LLM evaluation library, designed to provide data scientists and machine learning (ML) engineers with a code-first experience to evaluate LLMs for various aspects, including accuracy, toxicity, fairness, robustness, and efficiency. Evaluation algorithm Computes evaluation metrics to model outputs.

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AWS Machine Learning Blog

FEBRUARY 12, 2024

The Ranking team at Booking.com plays a pivotal role in ensuring that the search and recommendation algorithms are optimized to deliver the best results for their users. Essential ML capabilities such as hyperparameter tuning and model explainability were lacking on premises.

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AWS Machine Learning Blog

APRIL 8, 2025

With the support of AWS, iFood has developed a robust machine learning (ML) inference infrastructure, using services such as Amazon SageMaker to efficiently create and deploy ML models. In this post, we show how iFood uses SageMaker to revolutionize its ML operations.

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IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. What is MLOps?

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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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Unite.AI

JUNE 19, 2024

In world of Artificial Intelligence (AI) and Machine Learning (ML), a new professionals has emerged, bridging the gap between cutting-edge algorithms and real-world deployment. As businesses across industries increasingly embrace AI and ML to gain a competitive edge, the demand for MLOps Engineers has skyrocketed.

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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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Unite.AI

FEBRUARY 22, 2024

By mitigating biases and fostering an inclusive approach, organizations can avoid pitfalls such as discriminatory algorithms in areas like recruiting. ” Integrity: Upholding Reliability and Ethical Accountability AI/ML integrity stands as a pivotal pillar for responsible AI.

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Marktechpost

OCTOBER 10, 2024

Machine learning (ML) is a subset of artificial intelligence (AI) that builds algorithms capable of learning from data. Unlike traditional programming, where rules are explicitly defined, ML models learn patterns from data and make predictions or decisions autonomously. What is Machine Learning? fraud detection). clustering).

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AWS Machine Learning Blog

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts.

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Towards AI

MARCH 14, 2024

I mean, ML engineers often spend most of their time handling and understanding data. So, how is a data scientist different from an ML engineer? Well, there are three main reasons for this confusing overlap between the role of a data scientist and the role of an ML engineer.

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How to Learn Machine Learning

APRIL 10, 2025

Introduction Machine learning (ML) in 2025 will be continuously evolving because businesses from all industries will utilize artificial intelligence to achieve market superiority. The decision you must now make concerns whether to choose AWS SageMaker, a managed service platform or develop an ML solution exclusively.

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Marktechpost

NOVEMBER 16, 2024

Machine learning (ML) engineers face many challenges while working on end-to-end ML projects. The typical workflow involves repetitive and time-consuming tasks like data cleaning, feature engineering, model tuning, and eventually deploying models into production.

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Towards AI

FEBRUARY 11, 2025

🚀 MLflow Experiment Tracking: The Ultimate Beginners Guide to Streamlining ML Workflows Photo by Alvaro Reyes on Unsplash Introduction Have you ever felt that you were losing command over your machine-learning projects? The algorithm in question? Sound familiar? If you facing this problem, then you are not alone. Time to begin!

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Marktechpost

JANUARY 31, 2024

Data scientists and ML engineers often need help to build full-stack applications. These professionals typically have a firm grasp of data and AI algorithms. Still, they may need more skills or time to learn new languages or frameworks to create user-friendly web applications.

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Towards AI

NOVEMBER 8, 2024

Data preparation isn’t just a part of the ML engineering process — it’s the heart of it. Data is a key differentiator in ML projects (more on this in my blog post below). We don’t have better algorithms; we just have more data. This post dives into key steps for preparing data to build real-world ML systems.

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AWS Machine Learning Blog

JUNE 9, 2023

a low-code enterprise graph machine learning (ML) framework to build, train, and deploy graph ML solutions on complex enterprise-scale graphs in days instead of months. With GraphStorm, we release the tools that Amazon uses internally to bring large-scale graph ML solutions to production. license on GitHub. GraphStorm 0.1

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Bugra Akyildiz

JULY 20, 2024

Second-Order Optimization The authors report the first known large-scale deployment of a second-order optimizer in a production neural network: Distributed Shampoo algorithm used for training dense network weights Improved convergence speed and final model quality 3. Together, they contain about 3.5B

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O'Reilly Media

JANUARY 17, 2023

This mindset has followed me into my work in ML/AI. Because if companies use code to automate business rules, they use ML/AI to automate decisions. Given that, what would you say is the job of a data scientist (or ML engineer, or any other such title)? But first, let’s talk about the typical ML workflow.

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AWS Machine Learning Blog

DECEMBER 10, 2024

You can try this model with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms and models that can be deployed with one click for running inference. In this post, we walk through how to discover, deploy, and use the Pixtral 12B model for a variety of real-world vision use cases.

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AWS Machine Learning Blog

JUNE 25, 2024

This solution simplifies the integration of advanced monitoring tools such as Prometheus and Grafana, enabling you to set up and manage your machine learning (ML) workflows with AWS AI Chips. By deploying the Neuron Monitor DaemonSet across EKS nodes, developers can collect and analyze performance metrics from ML workload pods.

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AWS Machine Learning Blog

NOVEMBER 21, 2023

By taking care of the undifferentiated heavy lifting, SageMaker allows you to focus on working on your machine learning (ML) models, and not worry about things such as infrastructure. Prior to working at Amazon Music, Siddharth was working at companies like Meta, Walmart Labs, Rakuten on E-Commerce centric ML Problems.

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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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Towards AI

JANUARY 30, 2024

ML Engineers(LLM), Tech Enthusiasts, VCs, etc. Anybody previously acquainted with ML terms should be able to follow along. The algorithm works by optimizing the reward policy and then fine-tuning the unsupervised LLM according to reward maximization of such human-reinforced preference feedback. How advanced is this post?

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Towards AI

FEBRUARY 18, 2025

In this post, I want to shift the conversation to how Deepseek is redefining the future of machine learning engineering. It has already inspired me to set new goals for 2025, and I hope it can do the same for other ML engineers. It is fascinating what Deepseek has achieved with their top noche engineering skill.

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Unite.AI

JUNE 28, 2023

It became apparent to both Razi and me that we had the opportunity to make a significant impact by radically simplifying the feature engineering process and providing data scientists and ML engineers with the right tools and user experience for seamless feature experimentation and feature serving.

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Towards AI

FEBRUARY 15, 2024

AI Engineers: Your Definitive Career Roadmap Become a professional certified AI engineer by enrolling in the best AI ML Engineer certifications that help you earn skills to get the highest-paying job. Skills Needed as an AI Engineer As an AI engineer, you require a blend of soft and hard skills.

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ODSC - Open Data Science

SEPTEMBER 21, 2023

Secondly, to be a successful ML engineer in the real world, you cannot just understand the technology; you must understand the business. Some typical examples are given in the following table, along with some discussion as to whether or not ML would be an appropriate tool for solving the problem: Figure 1.1:

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Unite.AI

NOVEMBER 10, 2023

It is key to remember that AI is an algorithm, which analyzes and adjusts to the data we provide. Leverage the solutions that already exist Many companies aim to, right away, design their own machine learning algorithms. The basic scenario for data collection is as follows: Understand what data we might need to implement AI.

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Towards AI

AUGUST 24, 2023

Common mistakes and misconceptions about learning AI/ML Markus Spiske on Unsplash A common misconception of beginners is that they can learn AI/ML from a few tutorials that implement the latest algorithms, so I thought I would share some notes and advice on learning AI. Trying to code ML algorithms from scratch.

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Towards AI

AUGUST 25, 2023

As a reminder, I highly recommend that you refer to more than one resource (other than documentation) when learning ML, preferably a textbook geared toward your learning level (beginner/intermediate / advanced). In a nutshell, AI Engineering is the application of software engineering best practices to the field of AI.

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Marktechpost

JANUARY 10, 2024

Rather than relying on groundbreaking algorithmic changes, the emphasis is on continual learning and iterative improvements. It also automates feature engineering, a task traditionally handled solely by ML engineers, saving time and reducing the risk of errors. Check out the Reference Article.

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ODSC - Open Data Science

FEBRUARY 24, 2023

Apache Superset GitHub | Website Apache Superset is a must-try project for any ML engineer, data scientist, or data analyst. Algorithm-visualizer GitHub | Website Algorithm Visualizer is an interactive online platform that visualizes algorithms from code. This tool automatically detects problems in an ML dataset.

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ODSC - Open Data Science

APRIL 1, 2025

Building Multimodal AI Agents: Agentic RAG with Image, Text, and Audio Inputs Suman Debnath, Principal AI/ML Advocate at Amazon Web Services Discover the transformative potential of Multimodal Agentic RAG systems that integrate image, audio, and text to power intelligent, real-world applications.

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