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. Inadequate data security and DevOps support The previous solution lacked robust security measures, and there was limited support for development and operations of the data science work.

Data Science 71

Data Science Data Scientist Data Ingestion DevOps 71

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.

Machine Learning 95

Machine Learning ML Auto-complete Data Scientist 95

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. Your client applications invoke this endpoint to get inferences from the model.

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ML Metadata Data Scientist Machine Learning 105

AWS Machine Learning Blog

SEPTEMBER 4, 2024

Its scalability and load-balancing capabilities make it ideal for handling the variable workloads typical of machine learning (ML) applications. Amazon SageMaker provides capabilities to remove the undifferentiated heavy lifting of building and deploying ML models. This entire workflow is shown in the following solution diagram.

DevOps 101

DevOps ML Engineer ML Metadata 101

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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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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Machine Learning ML Engineer DevOps ML 128

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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ML Data Scientist ML Engineer Machine Learning 78

The MLOps Blog

JANUARY 23, 2023

Lived through the DevOps revolution. Came to ML from software. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”. Most of our customers are doing ML/MLOps at a reasonable scale, NOT at the hyperscale of big-tech FAANG companies. Probably sooner than you think.

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

AWS Machine Learning Blog

JULY 8, 2024

As industries begin adopting processes dependent on machine learning (ML) technologies, it is critical to establish machine learning operations (MLOps) that scale to support growth and utilization of this technology. There were noticeable challenges when running ML workflows in the cloud.

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Data Scientist ML Engineer Data Science Machine Learning 115

AWS Machine Learning Blog

SEPTEMBER 23, 2024

Machine learning (ML) projects are inherently complex, involving multiple intricate steps—from data collection and preprocessing to model building, deployment, and maintenance. To start our ML project predicting the probability of readmission for diabetes patients, you need to download the Diabetes 130-US hospitals dataset.

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ML Computer Vision Data Scientist Machine Learning 84

AWS Machine Learning Blog

JANUARY 5, 2024

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.

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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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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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Machine Learning DevOps Data Scientist Data Quality 134

AWS Machine Learning Blog

JUNE 27, 2023

When machine learning (ML) models are deployed into production and employed to drive business decisions, the challenge often lies in the operation and management of multiple models. That is where Provectus , an AWS Premier Consulting Partner with competencies in Machine Learning, Data & Analytics, and DevOps, stepped in.

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DevOps ML Machine Learning ML Engineer 116

AWS Machine Learning Blog

JANUARY 13, 2023

Since then, TR has achieved many more milestones as its AI products and services are continuously growing in number and variety, supporting legal, tax, accounting, compliance, and news service professionals worldwide, with billions of machine learning (ML) insights generated every year. The challenges. Solution overview.

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ML Data Scientist Machine Learning Metadata 90

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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ML Data Scientist ML Engineer Data Science 65

Heartbeat

FEBRUARY 1, 2023

Machine learning has become an essential part of our lives because we interact with various applications of ML models, whether consciously or unconsciously. Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models. What is MLOps?

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DevOps Machine Learning Data Scientist Software Engineer 96

AWS Machine Learning Blog

DECEMBER 1, 2023

Launched in 2019, Amazon SageMaker Studio provides one place for all end-to-end machine learning (ML) workflows, from data preparation, building and experimentation, training, hosting, and monitoring. About the Authors Mair Hasco is an AI/ML Specialist for Amazon SageMaker Studio. Get started on SageMaker Studio here.

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Machine Learning ML DevOps Data Science 121

AWS Machine Learning Blog

AUGUST 22, 2024

You can use Amazon SageMaker Model Building Pipelines to collaborate between multiple AI/ML teams. SageMaker Pipelines You can use SageMaker Pipelines to define and orchestrate the various steps involved in the ML lifecycle, such as data preprocessing, model training, evaluation, and deployment.

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AWS Machine Learning Blog

OCTOBER 2, 2023

A successful deployment of a machine learning (ML) model in a production environment heavily relies on an end-to-end ML pipeline. Although developing such a pipeline can be challenging, it becomes even more complex when dealing with an edge ML use case. This helps avoid costly defects at later stages of the production process.

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DevOps Automation ML Computer Vision 116

Pickl AI

JANUARY 30, 2023

It’s a universal programming language that finds application in different technologies like AI, ML, Big Data and others. An Artificial Intelligence/Machine Learning (AI/ML) Engineer uses Python For: Data Pre-processing : Before coding and creating an algorithm, it is important to clean and filter the data.

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Python DevOps Machine Learning Data Science 97

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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Generative AI Prompt Engineering Prompt Engineer ML 126

AWS Machine Learning Blog

FEBRUARY 29, 2024

Amazon SageMaker Studio offers a comprehensive set of capabilities for machine learning (ML) practitioners and data scientists. These include a fully managed AI development environment with an integrated development environment (IDE), simplifying the end-to-end ML workflow.

Data Scientist 124

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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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ML Data Scientist Machine Learning Data Science 116

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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Machine Learning Metadata Data Scientist Data Quality 134

AWS Machine Learning Blog

DECEMBER 13, 2023

Machine learning (ML) models do not operate in isolation. To deliver value, they must integrate into existing production systems and infrastructure, which necessitates considering the entire ML lifecycle during design and development. GitHub serves as a centralized location to store, version, and manage your ML code base.

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AWS Machine Learning Blog

OCTOBER 22, 2024

Data scientists and machine learning (ML) engineers use pipelines for tasks such as continuous fine-tuning of large language models (LLMs) and scheduled notebook job workflows. Create a complete AI/ML pipeline for fine-tuning an LLM using drag-and-drop functionality. Brock Wade is a Software Engineer for Amazon SageMaker.

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Automation LLM DevOps Python 84

The MLOps Blog

MARCH 21, 2023

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. In this comprehensive guide, we’ll explore everything you need to know about machine learning platforms, including: Components that make up an ML platform.

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Machine Learning Data Scientist ML Metadata 69

Mlearning.ai

JULY 29, 2023

INTRODUCTION Recently I had the pleasure to explore different options to automate a daily ML inference job, which reads raw data from a database table and write the inference results to another table. This approach is heavily inspired by the book Designing Machine Learning Systems by Chip Huyen , a go-to resource for any ML Engineer.

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Machine Learning DevOps ML ML Engineer 52

AWS Machine Learning Blog

AUGUST 16, 2023

Thomson Reuters (TR), a global content and technology-driven company, has been using artificial intelligence (AI) and machine learning (ML) in its professional information products for decades. This post is cowritten by Shirsha Ray Chaudhuri, Harpreet Singh Baath, Rashmi B Pawar, and Palvika Bansal from Thomson Reuters.

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Large Language Models Machine Learning Generative AI ML 89

Mlearning.ai

AUGUST 18, 2023

My interpretation to MLOps is similar to my interpretation of DevOps. As a software engineer your role is to write code for a certain cause. DevOps cover all of the rest, like deployment, scheduling of automatic tests on code change, scaling machines to demanding load, cloud permissions, db configuration and much more.

DevOps 52

DevOps Python ML ML Engineer 52

Flipboard

MARCH 7, 2023

Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. A public GitHub repo provides hands-on examples for each of the presented approaches.

Python 123

Python Data Science Data Scientist ML 123

The MLOps Blog

AUGUST 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. Stefan: Yeah.

ML 52

ML Data Scientist Software Engineer Machine Learning 52

The MLOps Blog

SEPTEMBER 7, 2023

This situation is not different in the ML world. Data Scientists and ML Engineers typically write lots and lots of code. Related post MLOps Is an Extension of DevOps. Cost to refactor, performance, reusability, legibility, or, more simply put, technical debt can hinder a company’s capacity to grow in a sustainable way.

Software Engineer 52

Software Engineer Machine Learning ETL Data Scientist 52

ODSC - Open Data Science

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52

Chatbots Life

MAY 16, 2023

10Clouds is a software consultancy, development, ML, and design house based in Warsaw, Poland. 10Clouds is a software consultancy, development, ML, and design house based in Warsaw, Poland. Services : AI Solution Development, ML Engineering, Data Science Consulting, NLP, AI Model Development, AI Strategic Consulting, Computer Vision.

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Generative AI Chatbots Conversational AI Software Development 92

AWS Machine Learning Blog

NOVEMBER 9, 2023

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

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Data Drift Auto-complete ML Automation 122