O'Reilly Media

OCTOBER 19, 2021

Much has been written about struggles of deploying machine learning projects to production. This approach has worked well for software development, so it is reasonable to assume that it could address struggles related to deploying machine learning in production too. All ML projects are software projects.

DevOps 139

DevOps Software Engineer Data Scientist ML 139

Unite.AI

JUNE 19, 2024

In world of Artificial Intelligence (AI) and Machine Learning (ML), a new professionals has emerged, bridging the gap between cutting-edge algorithms and real-world deployment. As businesses across industries increasingly embrace AI and ML to gain a competitive edge, the demand for MLOps Engineers has skyrocketed.

Software Engineer 179

Software Engineer DevOps Machine Learning ML 179

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.

Machine Learning 126

Machine Learning DevOps Data Scientist Data Quality 126

Flipboard

NOVEMBER 24, 2023

With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and ML engineers require capable tooling and sufficient compute for their work. Data scientists and ML engineers require capable tooling and sufficient compute for their work.

ML 153

ML DevOps Data Scientist ML Engineer 153

Towards AI

DECEMBER 26, 2024

4 Things to Keep in Mind Before Deploying Your ML Models This member-only story is on us. Source: Image By Author As a Cloud Engineer, Ive recently collaborated with a number of project teams, and my primary contribution to these teams has been to do the DevOps duties required on the GCP Cloud. Upgrade to access all of Medium.

ML 91

ML DevOps Software Development Machine Learning 91

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.

Machine Learning 121

Machine Learning ML Engineer DevOps ML 121

Marktechpost

FEBRUARY 27, 2024

Managing and serving features to real-time models in machine learning poses a significant challenge for ML platform teams. Existing options often involve intricate dataset joining logic and lack the necessary abstraction to decouple machine learning from data infrastructure.

Machine Learning 103

Machine Learning DevOps Data Scientist ML 103

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?

Data Science 170

Data Science DevOps Machine Learning Data Scientist 170

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?

Machine Learning 121

Machine Learning ML DevOps Software Engineer 121

Towards AI

DECEMBER 26, 2024

4 Things to Keep in Mind Before Deploying Your ML Models This member-only story is on us. Source: Image By Author As a Cloud Engineer, Ive recently collaborated with a number of project teams, and my primary contribution to these teams has been to do the DevOps duties required on the GCP Cloud. Upgrade to access all of Medium.

ML 95

ML DevOps Software Development Machine Learning 95

Flipboard

NOVEMBER 22, 2024

This post is part of an ongoing series about governing the machine learning (ML) lifecycle at scale. The data mesh architecture aims to increase the return on investments in data teams, processes, and technology, ultimately driving business value through innovative analytics and ML projects across the enterprise.

ML 132

ML Data Science Metadata DevOps 132

AWS Machine Learning Blog

NOVEMBER 26, 2024

With the current demand for AI and machine learning (AI/ML) solutions, the processes to train and deploy models and scale inference are crucial to business success. Even though AI/ML and especially generative AI progress is rapid, machine learning operations (MLOps) tooling is continuously evolving to keep pace.

ML 108

ML DevOps Data Scientist Automation 108

IBM Journey to AI blog

JANUARY 15, 2024

Digital transformation trends that drive a competitive advantage Trend: Artificial intelligence and machine learning We’re entering year two of widespread adoption of generative AI tools. For example, applied ML will help organizations that depend on the supply chain engage in better decision making, in real time.

Machine Learning 219

Machine Learning Automation DevOps Artificial Intelligence 219

AWS Machine Learning Blog

NOVEMBER 14, 2024

By setting up automated policy enforcement and checks, you can achieve cost optimization across your machine learning (ML) environment. The following table provides examples of a tagging dictionary used for tagging ML resources. This framework considers multiple personas and services to govern the ML lifecycle at scale.

ML 100

ML Metadata Automation Machine Learning 100

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. Ensuring data quality, governance, and security may slow down or stall ML projects.

ML 108

ML Machine Learning Data Science Data Drift 108

IBM Journey to AI blog

FEBRUARY 14, 2024

Using machine learning (ML), AI can understand what customers are saying as well as their tone—and can direct them to customer service agents when needed. When someone asks a question via speech or text, ML searches for the answer or recalls similar questions the person has asked before.

Computer Vision 247

Computer Vision Robotics Automation NLP 247

AWS Machine Learning Blog

OCTOBER 20, 2023

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.

ML 117

ML Data Scientist ML Engineer Data Science 117

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.

Machine Learning 87

Machine Learning Convolutional Neural Networks Neural Network Computer Vision 87

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.

Data Science 126

Data Science Data Drift DevOps Auto-complete 126

IBM Journey to AI blog

FEBRUARY 6, 2024

Microservices have become crucial for DevOps methodologies. Improved application development: Expand adoption of agile and DevOps methodologies, enabling faster application development and time to market. Microservices help teams develop applications once and across all types of clouds.

DevOps 299

DevOps Automation Software Development Big Data 299

Unite.AI

JUNE 24, 2024

Machine Learning Operations (MLOps) is a set of practices and principles that aim to unify the processes of developing, deploying, and maintaining machine learning models in production environments. What is MLOps?

Data Drift 122

Data Drift Machine Learning Data Scientist DevOps 122

AWS Machine Learning Blog

OCTOBER 9, 2023

Machine learning (ML) can analyze large volumes of product reviews and identify patterns, sentiments, and topics discussed. However, implementing ML can be a challenge for companies that lack resources such as ML practitioners, data scientists, or artificial intelligence (AI) developers.

Machine Learning 98

Machine Learning Categorization ML DevOps 98

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?

DevOps 96

DevOps Machine Learning Data Scientist Software Engineer 96

IBM Journey to AI blog

SEPTEMBER 20, 2023

Foundational models (FMs) are marking the beginning of a new era in machine learning (ML) and artificial intelligence (AI) , which is leading to faster development of AI that can be adapted to a wide range of downstream tasks and fine-tuned for an array of applications. IBM watsonx consists of the following: IBM watsonx.ai

Large Language Models 238

Large Language Models DevOps Data Science AI Modeling 238

AWS Machine Learning Blog

MAY 8, 2024

They focused on improving customer service using data with artificial intelligence (AI) and ML and saw positive results, with their Group AI Maturity increasing from 50% to 80%, according to the TM Forum’s AI Maturity Index. Amazon SageMaker Pipelines – Amazon SageMaker Pipelines is a CI/CD service for ML.

ML 113

ML Categorization AI AI 113

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.

ML 101

ML Deep Learning Machine Learning DevOps 101

Pickl AI

APRIL 17, 2023

How can a DevOps team take advantage of Artificial Intelligence (AI)? DevOps is mainly the practice of combining different teams including development and operations teams to make improvements in the software delivery processes. So now, how can a DevOps team take advantage of Artificial Intelligence (AI)?

DevOps 52

DevOps Artificial Intelligence Artificial Intelligence Machine Learning 52

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. for installing and managing Kubernetes applications.

DevOps 96

DevOps ML Engineer ML Metadata 96

AWS Machine Learning Blog

NOVEMBER 19, 2024

This year, generative AI and machine learning (ML) will again be in focus, with exciting keynote announcements and a variety of sessions showcasing insights from AWS experts, customer stories, and hands-on experiences with AWS services. Visit the session catalog to learn about all our generative AI and ML sessions.

ML 89

ML Generative AI AI AI 89

Unite.AI

OCTOBER 16, 2023

LLMOps versus MLOps Machine learning operations (MLOps) has been well-trodden, offering a structured pathway to transition machine learning (ML) models from development to production. Adjusting parameters like batch sizes and learning rates can markedly alter the training speed and costs.

Large Language Models 187

Large Language Models LLM Machine Learning DevOps 187

AWS Machine Learning Blog

MAY 7, 2024

Hugging Face is an open-source machine learning (ML) platform that provides tools and resources for the development of AI projects. About the Authors Gabriel Rodriguez Garcia is a Machine Learning engineer at AWS Professional Services in Zurich.

Automation 122

Automation Auto-complete DevOps UX Design 122

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.

DevOps 114

DevOps ML Machine Learning ML Engineer 114

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.

ML 81

ML Computer Vision Data Scientist Machine Learning 81

The MLOps Blog

APRIL 5, 2023

Have you ever spent weeks or months building a machine learning model, only to later find out that deploying it into a production environment is complicated and time-consuming? Machine learning model packaging is crucial to the machine learning development lifecycle.

ML 69

ML Machine Learning Deep Learning Metadata 69

IBM Journey to AI blog

JANUARY 30, 2024

Multicloud architecture not only empowers businesses to choose a mix of the best cloud products and services to match their business needs, but it also accelerates innovation by supporting game-changing technologies like generative AI and machine learning (ML).

Automation 288

Automation DevOps Artificial Intelligence Artificial Intelligence 288

AWS Machine Learning Blog

MAY 1, 2024

For example, you can ask a question such as, “Which EC2 instance is recommended for machine learning model inference and how much does it cost?” Call the GET::ChatBotBedrockAgentActionGroup::getUc2Response function to get the latest EC2 instance type and pricing details for machine learning inference. 32xlarge instance.

Chatbots 119

Chatbots Automation Machine Learning DevOps 119

Marktechpost

JULY 5, 2024

These courses cover foundational topics such as machine learning algorithms, deep learning architectures, natural language processing (NLP), computer vision, reinforcement learning, and AI ethics. Udacity offers comprehensive courses on AI designed to equip learners with essential skills in artificial intelligence.

Data Science 62

Data Science Machine Learning Data Analysis Software Engineer 62