The MLOps Blog

JANUARY 23, 2023

Just so you know where I am coming from: I have a heavy software development background (15+ years in software). Lived through the DevOps revolution. Came to ML from software. Founded two successful software services companies. If you’d like a TLDR, here it is: MLOps is an extension of DevOps.

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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.

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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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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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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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AWS Machine Learning Blog

DECEMBER 12, 2023

This new workforce requires rapid reskilling and understanding of disruptive services such as artificial intelligence (AI) and machine learning (ML) to drive meaningful outcomes. In this post, we share how Vodafone is advancing its ML skills using AWS DeepRacer and Accenture. Why is machine learning important to Vodafone?

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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.

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Unite.AI

JUNE 24, 2024

It combines principles from DevOps, such as continuous integration, continuous delivery, and continuous monitoring, with the unique challenges of managing machine learning models and datasets. As the adoption of machine learning in various industries continues to grow, the demand for robust MLOps tools has also increased. What is MLOps?

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Towards AI

AUGUST 16, 2023

Pietro Jeng on Unsplash MLOps is a set of methods and techniques to deploy and maintain machine learning (ML) models in production reliably and efficiently. Thus, MLOps is the intersection of Machine Learning, DevOps, and Data Engineering (Figure 1). Projects: a standard format for packaging reusable ML code.

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The MLOps Blog

OCTOBER 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. It was two or three times more.

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Mlearning.ai

APRIL 24, 2023

Learning about the framework of a service cloud platform is time consuming and frustrating because there is a lot of new information from many different computing fields (computer science/database, software engineering/developers, data science/scientific engineering & computing/research).

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Marktechpost

JULY 5, 2024

It covers advanced topics, including scikit-learn for machine learning, statistical modeling, software engineering practices, and data engineering with ETL and NLP pipelines. The program culminates in a capstone project where learners apply their skills to solve a real-world data science challenge.

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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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AWS Machine Learning Blog

JUNE 25, 2024

She is currently focusing on combining her DevOps and ML background into the domain of MLOps to help customers deliver and manage ML workloads at scale. He is currently focused on combining his background in software engineering, DevOps, and machine learning to help customers deliver machine learning workflows at scale.

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AWS Machine Learning Blog

MAY 1, 2024

The Inf1 and Trn1 instances deliver high performance inference using dedicated ML chips like Inferentia and Trainium at lower costs compared to GPU-based instances.According to the latest information, AWS Inferentia and Trainium instance prices range from $0.228 per hour for an inf1.xlarge He holds a Masters degree in Software Engineering.

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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.

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AWS Machine Learning Blog

JUNE 21, 2023

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). Studio provides a single web-based visual interface where you can perform all ML development steps required to prepare data, as well as build, train, and deploy models. For more information, visit Amazon SageMaker Studio.

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AWS Machine Learning Blog

JUNE 11, 2024

This allows machine learning (ML) practitioners to rapidly launch an Amazon Elastic Compute Cloud (Amazon EC2) instance with a ready-to-use deep learning environment, without having to spend time manually installing and configuring the required packages. You also need the ML job scripts ready with a command to invoke them.

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AWS Machine Learning Blog

FEBRUARY 1, 2024

She has a diverse background, having worked in many technical disciplines, including software development, agile leadership, and DevOps, and is an advocate for women in tech. Randy has held a variety of positions in the technology space, ranging from software engineering to product management.

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AWS Machine Learning Blog

MARCH 30, 2023

In this post, we introduce a new interactive tool called Snapper, powered by a machine learning (ML) model that reduces the effort required of annotators. ML model optimized for annotators A tremendous number of high-performing object detection models have been proposed by the computer vision community in recent years.

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Viso.ai

SEPTEMBER 2, 2024

Our analysis will explore these career opportunities in computer vision, that are not strictly CV engineer, or AI/ML specialist. Problem-solving and debugging skills, and some experience with DevOps, or SaaS environments will be beneficial. Customer-oriented with a real striving for quality and engineering excellence.

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AWS Machine Learning Blog

JULY 25, 2024

About the Authors Sharon Li is an AI/ML Specialist Solutions Architect at Amazon Web Services (AWS) based in Boston, Massachusetts. He is currently focused on combining his background in software engineering, DevOps, and machine learning to help customers deliver machine learning workflows at scale.

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Towards AI

AUGUST 16, 2023

The MLOps Process We can see some of the differences with MLOps which is a set of methods and techniques to deploy and maintain machine learning (ML) models in production reliably and efficiently. MLOps is the intersection of Machine Learning, DevOps, and Data Engineering.

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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.

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Viso.ai

MARCH 29, 2024

MLflow is an open-source platform designed to manage the entire machine learning lifecycle, making it easier for ML Engineers, Data Scientists, Software Developers, and everyone involved in the process. MLflow can be seen as a tool that fits within the MLOps (synonymous with DevOps) framework. To learn more, book a demo.

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AWS Machine Learning Blog

JULY 8, 2024

The workflow to create the training container consists of the following services: SageMaker uses Docker containers throughout the ML lifecycle. When the training process is complete, the output model that resides in the /opt/ml/model directory is automatically uploaded to the S3 bucket specified in the training job configuration.

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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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AWS Machine Learning Blog

FEBRUARY 2, 2024

This notebook pulls the models from the SageMaker JumpStart ML hub and deploys them to two separate SageMaker real-time endpoints. Abdullahi holds a MSC in Computer Networking from Wichita State University and is a published author that has held roles across various technology domains such as DevOps, infrastructure modernization and AI.

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AWS Machine Learning Blog

NOVEMBER 7, 2023

Amazon Kendra with foundational LLM Amazon Kendra is an advanced enterprise search service enhanced by machine learning (ML) that provides out-of-the-box semantic search capabilities. Semantic understanding – The ML capabilities of Amazon Kendra ensure that retrieval is based on deep semantic understanding and not just keyword matches.

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Heartbeat

NOVEMBER 28, 2023

Introducing MLOps Machine learning (ML) is an essential tool for businesses of all sizes. However, deploying ML models in production can be complex and challenging. MLOps encompasses the entire ML lifecycle, from data preparation to model deployment and monitoring. Second, ML models are constantly evolving.

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ODSC - Open Data Science

DECEMBER 11, 2023

Piiano Flows focuses on PII and other sensitive fields used within the code and uses AI and ML to provide full and greater coverage. GitGurdian GitGuardian is a strong tool that maintains the safety of software development by keeping confidential data in code repositories.

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AWS Machine Learning Blog

AUGUST 29, 2023

The presented MLOps workflow provides a reusable template for managing the ML lifecycle through automation, monitoring, auditability, and scalability, thereby reducing the complexities and costs of maintaining batch inference workloads in production. SageMaker Pipelines serves as the orchestrator for ML model training and inference workflows.

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The MLOps Blog

MAY 9, 2023

One of the most prevalent complaints we hear from ML engineers in the community is how costly and error-prone it is to manually go through the ML workflow of building and deploying models. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times.

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Explosion

JUNE 18, 2021

How implement models ML fundamentals training and evaluation improve accuracy use library APIs Python and DevOps What when to use ML decide what models and components to train understand what application will use outputs for find best trade-offs select resources and libraries The “how” is everything that helps you execute the plan.

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AWS Machine Learning Blog

OCTOBER 30, 2024

About the Authors Jundong Qiao is a Machine Learning Engineer at AWS Professional Service, where he specializes in implementing and enhancing AI/ML capabilities across various sectors. Praveen Kumar Jeyarajan is a Principal DevOps Consultant at AWS, supporting Enterprise customers and their journey to the cloud.

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AWS Machine Learning Blog

AUGUST 15, 2023

The decision handler determines the moderation action and provides reasons for its decision based on the ML models’ output, thereby deciding whether the image required a further review by a human moderator or could be automatically approved or rejected.

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