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

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

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 109

Machine Learning ML DevOps Software Engineer 109

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

O'Reilly Media

OCTOBER 19, 2021

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. Can’t we just fold it into existing DevOps best practices?

DevOps 138

DevOps Software Engineer Data Scientist ML 138

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 AI 98

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 ML Engineer 52

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 87

Machine Learning Convolutional Neural Networks Neural Network Computer Vision 87

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 97

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.

Large Language Models 100

Large Language Models Generative AI LLM AI 100

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 102

Chatbots Automation Machine Learning DevOps 102

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

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

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 119

Generative AI Prompt Engineer Prompt Engineering AI 119

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 Machine Learning 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

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 85

Python Data Scientist ML DevOps 85

AWS Machine Learning Blog

AUGUST 15, 2023

This approach has saved them months of DevOps effort per year, which means they can now allocate their time to developing innovative features instead of spending it on operational tasks. Combined with the DevOps efficiency gains, the Amazon Shopping team achieved significant cost savings.

DevOps 80

DevOps Computer Vision Automation ML 80

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 94

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

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

Marktechpost

MARCH 21, 2024

Software Engineering teams encounter significant challenges in managing observability costs and incident handling, particularly when development pace is rapid. Effective code instrumentation is difficult for engineers, leading to costly errors. Meet OneGrep , an AI-driven DevOps Copilot that solves these problems quickly.

DevOps 89

DevOps Software Engineer Machine Learning AI 89

The MLOps Blog

OCTOBER 3, 2023

I started from tech, my first job was an internship at Google as a software engineer. I’m from Poland, and I remember when I got an offer from Google to join as a regular software engineer. I switched from analytics to data science, then to machine learning, then to data engineering, then to MLOps.

ML 52

ML Data Scientist Machine Learning Data Science 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.

Machine Learning 52

Machine Learning ML Deep Learning Neural Network 52

IBM Journey to AI blog

MARCH 6, 2024

DevOps Research and Assessment metrics (DORA), encompassing metrics like deployment frequency, lead time and mean time to recover , serve as yardsticks for evaluating the efficiency of software delivery. A burned-out developer is usually an unproductive one.

Generative AI 289

Generative AI DevOps Software Development Auto-complete 289

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

AWS Machine Learning Blog

AUGUST 29, 2023

Then, inference pipelines are deployed to staging and production accounts using automation from DevOps tools such as GitLab CI/CD. Vivek Lakshmanan is a Machine Learning Engineer at Amazon. He has a Master’s degree in Software Engineering with specialization in Data Science and several years of experience as an MLE.

Data Scientist 76

Data Scientist Data Quality Python ML 76

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.

NLP 52

NLP Machine Learning Natural Language Processing DevOps 52

The MLOps Blog

JUNE 27, 2023

This article by Samhita Alla, a software engineer and tech evangelist at Union.ai, provides a simplified walkthrough of the applications of Flyte in MLOps. MLOps tools and platforms FAQ What devops tools are used in machine learning in 20233? Check out the documentation to get started.

Machine Learning 134

Machine Learning Metadata Data Quality Data Scientist 134

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 108

LLM Deep Learning Machine Learning Large Language Models 108

Mlearning.ai

JUNE 16, 2023

Game changer ChatGPT in Software Engineering: A Glimpse Into the Future | HackerNoon Generative AI for DevOps: A Practical View - DZone ChatGPT for DevOps: Best Practices, Use Cases, and Warnings.

Software Development 52

Software Development Generative AI ChatGPT Prompt Engineer 52

The MLOps Blog

DECEMBER 28, 2022

Hence, the ML teams must have a mix of strong data architects and engineering experts that can successfully operationalize the ML model. MLOps cycle | Source How to organize ML team Centralized ML team People from different fields like engineering, product, DevOps, and ML all come together under one big team.

ML 78

ML Data Scientist ML Engineer Machine Learning 78

The MLOps Blog

MAY 9, 2023

Model deployment You can deploy the packaged and registered model to a staging environment (as traditional software with DevOps) or the production environment. It is most common to use containers for machine learning pipelines. The staging environment is for integration testing. The architecture of the Flyte platform.

ML 98

ML Machine Learning Metadata Automation 98

The MLOps Blog

MARCH 21, 2023

It would make sure that all development and deployment workflows use good software engineering practices. CI/CD lets your engineers add code and data to start automated development, testing, and deployment, depending on how your organization is set up. . My Story DevOps Engineers Who they are?

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

The MLOps Blog

JUNE 5, 2023

One of the hardest things about MLOps today is that a lot of data scientists aren’t native software engineers, but it may be possible to lower the bar to software engineering. Learning software engineering best practices and understanding how ML systems get built and productionized.

Large Language Models 52

Large Language Models Machine Learning Software Engineer ML 52