NVIDIA

DECEMBER 3, 2024

The certification exams and recommended training to prepare for them are designed for network and system administrators, DevOps and MLOps engineers, and others who need to understand AI infrastructure and operations. earlier this year.

DevOps 113

DevOps Software Engineer Large Language Models Generative AI 113

Unite.AI

JUNE 19, 2024

If you're fascinated by the intersection of ML and software engineering, and you thrive on tackling complex challenges, a career as an MLOps Engineer might be the perfect fit. Understanding MLOps Before delving into the intricacies of becoming an MLOps Engineer, it's crucial to understand the concept of MLOps itself.

Software Engineer 182

Software Engineer DevOps Machine Learning ML 182

IBM Journey to AI blog

FEBRUARY 22, 2024

They upskilled 3,000 software engineers for DevOps and cloud-native technologies. With a co-created custom curriculum that is designed to supplement their standard DevOps and cloud native offerings to ensure the content aligned with the client’s new technology stack.

DevOps 196

DevOps Software Engineer 196

AWS Machine Learning Blog

DECEMBER 23, 2024

Neel Kapadia is a Senior Software Engineer at AWS where he works on designing and building scalable AI/ML services using Large Language Models and Natural Language Processing. Anand Jumnani is a DevOps Consultant at Amazon Web Services based in United Kingdom. In his spare time, he enjoys cooking, reading, and traveling.

DevOps 119

DevOps Natural Language Processing Software Engineer Large Language Models 119

Artificial Corner

MARCH 6, 2025

Google’s 2024 DORA State of DevOps report found that increased AI adoption modestly improved code quality by 3.4% decline in software delivery stability (such as more incidents or rollbacks) when AI was heavily used. However, not all reports paint a negative picture. among surveyed teams. But even that report highlighted a 7.2%

Software Engineer 82

Software Engineer AI AI DevOps 82

Unite.AI

OCTOBER 18, 2024

Qovery Qovery stands out as a powerful DevOps Automation Platform that aims to streamline the development process and reduce the need for extensive DevOps hiring. This article explores the top internal developer platforms that are improving the way development teams work, deploy applications, and manage their infrastructure.

IDP 162

IDP DevOps Automation Software Development 162

Mlearning.ai

DECEMBER 21, 2023

Design patterns in software engineering are typical solutions to common problems in software design. They represent best practices, evolved over time, and are a toolkit for software developers to solve common problems efficiently. Source: Image by the Author What are Design Patterns? How to Get Started?

Software Engineer 52

Software Engineer Software Development DevOps Algorithm 52

IBM Journey to AI blog

AUGUST 11, 2023

MLOps fosters greater collaboration between data scientists, software engineers and IT staff. The paper suggested creating a systematic “MLOps” process that incorporated CI/CD methodology commonly used in DevOps to essentially create an assembly line for each step.

Data Science 170

Data Science DevOps Machine Learning Data Scientist 170

Heartbeat

FEBRUARY 29, 2024

After closely observing the software engineering landscape for 23 years and engaging in recent conversations with colleagues, I can’t help but feel that a specialized Large Language Model (LLM) is poised to power the following programming language revolution.

Software Engineer 52

Software Engineer Software Development LLM Large Language Models 52

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. Not a fork.

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

AWS Machine Learning Blog

MARCH 10, 2025

The use of multiple external cloud providers complicated DevOps, support, and budgeting. Operational consolidation and reliability Post-migration, our DevOps and SRE teams see 20% less maintenance burden and overheads. These operational inefficiencies meant that we had to revisit our solution architecture.

DevOps 105

DevOps Metadata Auto-complete Automation 105

ODSC - Open Data Science

OCTOBER 9, 2024

The rapid evolution of AI is transforming nearly every industry/domain, and software engineering is no exception. But how so with software engineering you may ask? These technologies are helping engineers accelerate development, improve software quality, and streamline processes, just to name a few.

Software Engineer 40

Software Engineer Software Development DevOps Machine Learning 40

Heartbeat

FEBRUARY 1, 2023

MLOps acts as the link between data scientists and the production team’s operations (a team consisting of machine learning engineers, software engineers, and IT operations professionals) as they work together to develop ML models and supervise the use of ML models in production.

DevOps 96

DevOps Machine Learning Data Scientist Software Engineer 96

The MLOps Blog

SEPTEMBER 7, 2023

Related post MLOps Is an Extension of DevOps. From writing code for doing exploratory analysis, experimentation code for modeling, ETLs for creating training datasets, Airflow (or similar) code to generate DAGs, REST APIs, streaming jobs, monitoring jobs, etc.

Software Engineer 52

Software Engineer Machine Learning ETL Data Scientist 52

AWS Machine Learning Blog

DECEMBER 19, 2023

Machine learning operations (MLOps) applies DevOps principles to ML systems. Just like DevOps combines development and operations for software engineering, MLOps combines ML engineering and IT operations.

Machine Learning 133

Machine Learning ML Engineer DevOps ML 133

Flipboard

FEBRUARY 10, 2025

He is currently focused on combining his background in software engineering, DevOps, and machine learning to help customers deliver machine learning workflows at scale. Bobby Lindsey is a Machine Learning Specialist at Amazon Web Services. In his spare time, he enjoys reading, research, hiking, biking, and trail running.

LLM 161

LLM AI AI Python 161

AWS Machine Learning Blog

APRIL 8, 2025

They also integrated this entire system with the companys existing CI/CD pipeline, making it efficient and also maintaining good DevOps practices used at iFood. Using Amazon SageMaker Pipelines , they were able to build a CI/CD system for ML, to deliver automated retraining and model deployment. It starts with the ML Go!

Machine Learning 103

Machine Learning ML Auto-complete Data Scientist 103

AWS Machine Learning Blog

DECEMBER 12, 2023

To help achieve this ambitious transition, Vodafone has partnered with Accenture and AWS to build a cloud platform that helps its engineers work in flexible, creative, and agile ways by providing them a curated set of managed, security and DevOps-oriented AWS services and application workloads.

Machine Learning 130

Machine Learning ML DevOps Software Engineer 130

AWS Machine Learning Blog

MARCH 18, 2024

Harish Tummalacherla is Software Engineer with Deep Learning Performance team at SageMaker. He works on performance engineering for serving large language models efficiently on SageMaker. In his spare time, he enjoys running, cycling and ski mountaineering.

LLM 130

LLM Deep Learning Machine Learning Large Language Models 130

Towards AI

AUGUST 16, 2023

Thus, MLOps is the intersection of Machine Learning, DevOps, and Data Engineering (Figure 1). Any competent software engineer can learn how to use a particular MLOps platform since it does not require an advanced degree. The ideal MLOps engineer would have some experience with several MLOps and/or DevOps platforms.

Machine Learning 98

Machine Learning DevOps Software Development ML 98

AWS Machine Learning Blog

NOVEMBER 26, 2024

The following architecture diagram captures the main infrastructure that is deployed by the AWS CDK, typically carried out by a DevOps engineer. Cory Hairston is a Software Engineer with AWS Bedrock. He currently works on providing reusable software solutions.

Data Scientist 92

Data Scientist Python Automation ML 92

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.

DevOps 124

DevOps Machine Learning LLM Software Engineer 124

AWS Machine Learning Blog

MAY 1, 2024

Praveen Kumar Jeyarajan is a Principal DevOps Consultant at AWS, supporting Enterprise customers and their journey to the cloud. He has 13+ years of DevOps experience and is skilled in solving myriad technical challenges using the latest technologies. He holds a Masters degree in Software Engineering.

Chatbots 131

Chatbots Automation Machine Learning DevOps 131

Towards AI

FEBRUARY 10, 2024

Definition of a full-stack data scientist The sibling relationship between data science and software development has led to the borrowing of many concepts from the software development domain into data science practice.

Data Scientist 116

Data Scientist Software Development Data Science Machine Learning 116

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?

Data Drift 124

Data Drift Machine Learning Data Scientist ML 124

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.

Generative AI 138

Generative AI Prompt Engineering Prompt Engineer AI 138

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.

Data Science 62

Data Science Machine Learning Data Analysis Software Engineer 62

AWS Machine Learning Blog

JUNE 21, 2023

This enables you to apply DevOps best practices and meet safety, compliance, and configuration standards across all AWS accounts and Regions. About the Authors Cory Hairston is a Software Engineer with the Amazon ML Solutions Lab. He currently works on providing reusable software solutions.

Python 103

Python Data Scientist ML DevOps 103

AWS Machine Learning Blog

OCTOBER 22, 2024

She has a decade of experience in DevOps, infrastructure, and ML. Brock Wade is a Software Engineer for Amazon SageMaker. Brock builds solutions for MLOps, LLMOps, and generative AI, with experience spanning infrastructure, DevOps, cloud services, SDKs, and UIs.

Automation 88

Automation LLM DevOps Python 88

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).

DevOps 52

DevOps ETL Python Machine Learning 52

Towards AI

AUGUST 16, 2023

MLOps is the intersection of Machine Learning, DevOps, and Data Engineering. 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.

Machine Learning 98

Machine Learning Software Development Data Drift Data Science 98

AWS Machine Learning Blog

APRIL 26, 2023

Data science – The heart of ML EBA and focuses on feature engineering, model training, hyperparameter tuning, and model validation. MLOps engineering – Focuses on automating the DevOps pipelines for operationalizing the ML use case. This may often be the same team as cloud engineering. Connect with him on LinkedIn.

ML 117

ML Machine Learning Data Science Data Drift 117

AWS Machine Learning Blog

MARCH 30, 2023

About the authors Jonathan Buck is a Software Engineer at Amazon Web Services working at the intersection of machine learning and distributed systems. His work involves productionizing machine learning models and developing novel software applications powered by machine learning to put the latest capabilities in the hands of customers.

Machine Learning 98

Machine Learning Convolutional Neural Networks Neural Network Computer Vision 98

AWS Machine Learning Blog

JUNE 11, 2024

Anant Sharma is a software engineer at AWS Annapurna Labs specializing in DevOps. His primary focus revolves around building, automating and refining the process of delivering software to AWS Trainium and Inferentia customers.

Deep Learning 126

Deep Learning ML Automation Auto-complete 126

AWS Machine Learning Blog

JULY 25, 2024

He is currently focused on combining his background in software engineering, DevOps, and machine learning to help customers deliver machine learning workflows at scale. Bobby Lindsey is a Machine Learning Specialist at Amazon Web Services. In his spare time, he enjoys reading, research, hiking, biking, and trail running.

Conversational AI 134

Conversational AI Machine Learning AI AI 134

AWS Machine Learning Blog

MARCH 5, 2025

He has touched on most aspects of these projects, from infrastructure and DevOps to software development and AI/ML. After earning his bachelors degree in software engineering and a masters in computer vision and machine learning from Polytechnique Montreal, Philippe joined AWS to put his expertise to work for customers.

Generative AI 80

Generative AI LLM AI AI 80

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