IBM Journey to AI blog

AUGUST 12, 2024

MLOps is a set of practices that combines machine learning (ML) with traditional data engineering and DevOps to create an assembly line for building and running reliable, scalable, efficient ML models. AIOPs enables ITOPs personnel to implement predictive alert handling, strengthen data security and support DevOps processes.

Big Data 278

Big Data DevOps Automation Machine Learning 278

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.

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DevOps Software Engineer 224

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.

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DevOps Metadata Software Engineer Data Scientist 59

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

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 188

Data Science DevOps Machine Learning Data Scientist 188

Unite.AI

NOVEMBER 20, 2024

These agents perform tasks ranging from customer support to software engineering, navigating intricate workflows that combine reasoning, tool use, and memory. This is where AgentOps comes in; a concept modeled after DevOps and MLOps but tailored for managing the lifecycle of FM-based agents.

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LLM AI AI DevOps 147

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.

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DevOps Machine Learning Data Scientist Software Engineer 96

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 129

Machine Learning ML Engineer DevOps ML 129

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 64

Data Science Machine Learning Software Engineer Data Analysis 64

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.

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DevOps Machine Learning Software Development ML 89

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.

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Machine Learning DevOps ML Software Engineer 128

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

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 122

DevOps Machine Learning LLM Software Engineer 122

Marktechpost

OCTOBER 27, 2024

Best Use Cases: Game development, Windows applications, web development (ASP.NET), and enterprise software. It’s used in system programming, network programming, cloud computing, and DevOps. Best Use Cases: System programming, network programming, cloud computing, and DevOps 7.

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Data Analysis Big Data Natural Language Processing Machine Learning 135

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 104

Data Scientist Software Development Data Science Machine Learning 104

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 82

Automation LLM DevOps Python 82

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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Data Drift Machine Learning Data Scientist DevOps 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.

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Chatbots Automation Machine Learning DevOps 127

Viso.ai

SEPTEMBER 2, 2024

Image fusion job example – Source Software Engineer Besides specialized CV engineers – computer vision companies often employ regular software engineers who possess the following skills: Being proficient in programming languages, e.g. C++, Python, Java, etc.

Computer Vision 52

Computer Vision Software Engineer Convolutional Neural Networks Neural Network 52

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

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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Generative AI Prompt Engineer Prompt Engineering AI 134

AWS Machine Learning Blog

NOVEMBER 7, 2024

Ravi Sharma is a Senior Software Engineer at Zalando’s Pricing Platform, bringing experience across diverse domains such as football betting, radio astronomy, healthcare, and ecommerce. His work encompasses the development of business-relevant and scalable forecasting models, stretching from prototyping to deployment.

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ML Machine Learning Automation Data Scientist 111

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.

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Python Data Scientist DevOps ML 100

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.

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Machine Learning Convolutional Neural Networks Neural Network Computer Vision 94

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 82

Machine Learning Software Development Data Drift DevOps 82

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.

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ML Machine Learning Data Science Data Drift 116

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 123

Deep Learning ML Automation Auto-complete 123

ODSC - Open Data Science

JULY 19, 2023

Security: We have included steps and best practices from GitHub’s advanced security scanning and credential scanning (also available in Azure DevOps) that can be incorporated into the workflow. So, we’ve created quick deploy examples with Azure DevOps (ADO), GitHub, and Microsoft Learn to help you get your ideas tested in no time.

Machine Learning 52

Machine Learning Data Drift Data Science ML Engineer 52

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.

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Conversational AI Machine Learning AI AI 130

The MLOps Blog

AUGUST 3, 2023

Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. The idea is we want to help you enable a junior team of data scientists to not trip up over the software engineering aspects of maintaining the code within the macro tasks of something such as Airflow.

ML 52

ML Data Scientist Software Engineer Python 52

ODSC - Open Data Science

DECEMBER 11, 2023

This tool is designed to assist development and DevOps teams in the detection and mitigation of security vulnerabilities, with a focus on detecting sensitive secrets and vital credentials that may be exposed inadvertently. She is excited to use her writing skills to help businesses grow and succeed online in the competitive market.

Software Development 52

Software Development Data Science DevOps Software Engineer 52

AWS Machine Learning Blog

FEBRUARY 2, 2024

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. Randy has held a variety of positions in the technology space, ranging from software engineering to product management.

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ETL DevOps LLM Generative AI 118

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.

Machine Learning 52

Machine Learning ML Automation Data Scientist 52

AWS Machine Learning Blog

JULY 8, 2024

He currently serves media and entertainment customers, and has expertise in software engineering, DevOps, security, and AI/ML. To explore more AI use cases, visit the AI Use Case Explorer. About the Author Alen Zograbyan is a Sr. Solutions Architect at Amazon Web Services.

Automation 124

Automation ML DevOps Software Engineer 124

AWS Machine Learning Blog

NOVEMBER 7, 2023

With a background in software development and nine AWS certifications, Jin brings a wealth of experience to assist AWS customers in materializing their AI/ML and generative AI visions using the AWS platform. He holds a master’s degree in Computer Science & Software Engineering from the University of Syracuse.

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Large Language Models Generative AI LLM AI 121

Viso.ai

MARCH 1, 2023

However, Artificial Intelligence is a narrow high-tech niche of software engineering, requiring in-depth knowledge of coding and technology. Hence, AI development is traditionally associated with advanced software engineering skills and significant development experience.

Computer Vision 52

Computer Vision Software Development AI AI 52

Heartbeat

NOVEMBER 28, 2023

MLOps is a set of principles and practices that combine software engineering, data science, and DevOps to ensure that ML models are deployed and managed effectively in production. However, deploying ML models in production can be complex and challenging. This is where MLOps comes in.

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Machine Learning ML Deep Learning Neural Network 52