IBM Journey to AI blog

AUGUST 12, 2024

AIOPs refers to the application of artificial intelligence (AI) and machine learning (ML) techniques to enhance and automate various aspects of IT operations (ITOps). ML technologies help computers achieve artificial intelligence. However, they differ fundamentally in their purpose and level of specialization in AI and ML environments.

Big Data 266

Big Data DevOps Automation Machine Learning 266

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.

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ML Generative AI AI AI 95

Unite.AI

FEBRUARY 6, 2025

TrueFoundry offers a unified Platform as a Service (PaaS) that empowers enterprise AI/ML teams to build, deploy, and manage large language model (LLM) applications across cloud and on-prem infrastructure.

DevOps 179

DevOps Machine Learning Large Language Models Automation 179

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?

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DevOps Software Engineer Data Scientist ML 145

IBM Journey to AI blog

NOVEMBER 13, 2023

Overview of Kubernetes Containers —lightweight units of software that package code and all its dependencies to run in any environment—form the foundation of Kubernetes and are mission-critical for modern microservices, cloud-native software and DevOps workflows.

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DevOps Software Development Automation Machine Learning 323

AWS Machine Learning Blog

JANUARY 23, 2025

The solution described in this post is geared towards machine learning (ML) engineers and platform teams who are often responsible for managing and standardizing custom environments at scale across an organization. This approach helps you achieve machine learning (ML) governance, scalability, and standardization.

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Automation DevOps Machine Learning ML 106

databricks

SEPTEMBER 11, 2024

Personal Access Tokens (PATs) are a convenient way to access services like Azure Databricks or Azure DevOps without logging in with your password.

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DevOps ML Data Science 114

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.

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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.

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ML DevOps Software Development Machine Learning 111

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. Meet the MLOps Engineer: the orchestrating the seamless integration of ML models into production environments, ensuring scalability, reliability, and efficiency.

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Software Engineer DevOps Machine Learning ML 182

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.

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ML Data Scientist ML Engineer Data Science 128

Flipboard

MARCH 21, 2025

AI for IT operations (AIOps) is the application of AI and machine learning (ML) technologies to automate and enhance IT operations. By providing developers expert guidance grounded in AWS best practices, this AI assistant enables DevOps teams to review and optimize cloud architecture across of AWS accounts.

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Automation Large Language Models Generative AI DevOps 164

Analytics Vidhya

DECEMBER 11, 2022

Introduction Machine learning (ML) has become an increasingly important tool for organizations of all sizes, providing the ability to learn and improve from data automatically. However, successfully deploying and managing ML in production can be challenging, requiring careful coordination between data scientists and […].

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Machine Learning Data Scientist Data Science ML 360

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. Today, 35% of companies report using AI in their business, which includes ML, and an additional 42% reported they are exploring AI, according to the IBM Global AI Adoption Index 2022. What is MLOps? Where they are deployed.

Data Science 170

Data Science DevOps Machine Learning Data Scientist 170

AWS Machine Learning Blog

OCTOBER 28, 2024

Real-world applications vary in inference requirements for their artificial intelligence and machine learning (AI/ML) solutions to optimize performance and reduce costs. SageMaker Model Monitor monitors the quality of SageMaker ML models in production. Your client applications invoke this endpoint to get inferences from the model.

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ML Metadata Data Scientist Machine Learning 107

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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ML Machine Learning Data Science Data Drift 113

AWS Machine Learning Blog

FEBRUARY 28, 2025

As organizations adopt AI and machine learning (ML), theyre using these technologies to improve processes and enhance products. Automat-it specializes in helping startups and scaleups grow through hands-on cloud DevOps, MLOps and FinOps services. The collaboration aimed to achieve scalability and performance while optimizing costs.

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DevOps AI AI Deep Learning 97

AWS Machine Learning Blog

FEBRUARY 21, 2025

Data exploration and model development were conducted using well-known machine learning (ML) tools such as Jupyter or Apache Zeppelin notebooks. Inadequate data security and DevOps support The previous solution lacked robust security measures, and there was limited support for development and operations of the data science work.

Data Science 71

Data Science Data Scientist Data Ingestion DevOps 71

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.

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ML Categorization AI AI 120

Flipboard

FEBRUARY 10, 2025

In this post, we dive into how organizations can use Amazon SageMaker AI , a fully managed service that allows you to build, train, and deploy ML models at scale, and can build AI agents using CrewAI, a popular agentic framework and open source models like DeepSeek-R1. Pranav Murthy is an AI/ML Specialist Solutions Architect at AWS.

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LLM AI AI Python 161

AI News

AUGUST 29, 2023

The operationalisation of data projects has been a key factor in helping organisations turn a data deluge into a workable digital transformation strategy, and DataOps carries on from where DevOps started. So that’s on the vendor side.

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Data Ingestion Big Data Explainability ETL 245

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. This entire workflow is shown in the following solution diagram.

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DevOps ML Engineer ML Metadata 103

AWS Machine Learning Blog

OCTOBER 16, 2024

The call processing workflow uses custom machine learning (ML) models built by Intact that run on Amazon Fargate and Amazon Elastic Compute Cloud (Amazon EC2). This pipeline provides self-serving capabilities for data scientists to track ML experiments and push new models to an S3 bucket.

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Machine Learning Data Scientist Deep Learning ML 83

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)?

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DevOps Artificial Intelligence Artificial Intelligence Machine Learning 52

The MLOps Blog

JANUARY 23, 2023

Lived through the DevOps revolution. Came to ML from software. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”. Most of our customers are doing ML/MLOps at a reasonable scale, NOT at the hyperscale of big-tech FAANG companies. Probably sooner than you think.

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DevOps Metadata Software Engineer Data Scientist 59

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.

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DevOps Automation Big Data Software Development 299

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 114

DevOps Natural Language Processing Software Engineer Large Language Models 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.

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ML Computer Vision Data Scientist Machine Learning 85

AWS Machine Learning Blog

MARCH 21, 2025

JupyterLab applications flexible and extensive interface can be used to configure and arrange machine learning (ML) workflows. He is passionate about applying cloud technologies and ML to solve real life problems. Renu has a strong passion for learning with her area of specialization in DevOps.

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Metadata Convolutional Neural Networks Generative AI Data Scientist 114

IBM Journey to AI blog

JANUARY 15, 2024

As such, organizations are increasingly interested in seeing how they can apply the whole suite of artificial intelligence (AI) and machine learning (ML) technologies to improve their business processes. For example, applied ML will help organizations that depend on the supply chain engage in better decision making, in real time.

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Machine Learning Automation DevOps Artificial Intelligence 219

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 133

Data Science Data Drift DevOps Auto-complete 133

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.

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ML Deep Learning Machine Learning DevOps 108

AWS Machine Learning Blog

DECEMBER 3, 2024

Potential impacts The New Relic Intelligent Observability platform provides comprehensive incident response and application and infrastructure performance monitoring capabilities for SREs, application engineers, support engineers, and DevOps professionals.

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DevOps AI AI ML 104

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 NLP Robotics Automation 247

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.

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DevOps Metadata Auto-complete Automation 99

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 135

Machine Learning DevOps Data Scientist Data Quality 135

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

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Large Language Models DevOps Data Science AI Modeling 238