IBM Journey to AI blog

MARCH 20, 2024

Integration helps connect, automate and digitally transform businesses. With IBM Cloud Pak® for Integration, organizations are able to unleash their business potential, deliver new experiences, improve customer outcomes and enable automations that can scale across the enterprise.

Automation 196

Automation DevOps AI AI 196

IBM Journey to AI blog

SEPTEMBER 20, 2023

IBM watsonx.governance is an end-to-end automated AI lifecycle governance toolkit that is built to enable responsible, transparent and explainable AI workflows. This appliance can handle complex use cases out of the box, and it builds the hub-and-spoke framework for centralized management, automation and self-service.

Large Language Models 238

Large Language Models DevOps Data Science AI Modeling 238

AWS Machine Learning Blog

SEPTEMBER 4, 2024

DevOps engineers often use Kubernetes to manage and scale ML applications, but before an ML model is available, it must be trained and evaluated and, if the quality of the obtained model is satisfactory, uploaded to a model registry. This configuration takes the form of a Directed Acyclic Graph (DAG) represented as a JSON pipeline definition.

DevOps 94

DevOps ML Engineer ML Metadata 94

Pickl AI

APRIL 1, 2025

Definition and Significance in the IT Industry A Cloud Engineer is responsible for building and managing cloud systems that allow businesses to work efficiently without relying on physical storage. Collaborating with DevOps Teams and Software Developers Cloud Engineers work closely with developers to create, test, and improve applications.

DevOps 52

DevOps Data Science Automation Big Data 52

IBM Journey to AI blog

JUNE 26, 2023

One of the few pre-scripted questions I ask in most of the episodes is about the guest’s definition of “hybrid cloud.” It isn’t a surprise that so many of the guests on my podcast work on topics and technologies directly related to cloud. ” The answers have all been comparable.

DevOps 180

DevOps Explainability Automation AI 180

AWS Machine Learning Blog

NOVEMBER 26, 2024

With lifecycle configurations, system administrators can apply automated controls to their SageMaker Studio domains and their users. These automations can greatly decrease overhead related to ML project setup, facilitate technical consistency, and save costs related to running idle instances.

Data Scientist 79

Data Scientist Python ML Automation 79

The MLOps Blog

JANUARY 23, 2023

Machine Learning Operations (MLOps): Overview, Definition, and Architecture” By Dominik Kreuzberger, Niklas Kühl, Sebastian Hirschl Great stuff. If you haven’t read it yet, definitely do so. Lived through the DevOps revolution. If you’d like a TLDR, here it is: MLOps is an extension of DevOps. Some are my 3–4 year bets.

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

Towards AI

OCTOBER 4, 2024

The growing complexity of cloud environments and the demand for faster, more reliable, and reproducible infrastructure management practices highlighted the need for a more efficient solution.Infrastructure-as-code (IaC) is a DevOps practice that uses code to define and deploy infrastructure.

DevOps 116

DevOps Automation AI AI 116

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. It’s much more than just automation. Only a small fraction of a real-world ML use case comprises the model itself.

Machine Learning 121

Machine Learning ML Engineer DevOps ML 121

AWS Machine Learning Blog

OCTOBER 22, 2024

It accelerates your generative AI journey from prototype to production because you don’t need to learn about specialized workflow frameworks to automate model development or notebook execution at scale. Download the pipeline definition as a JSON file to your local environment by choosing Export at the bottom of the visual editor.

Automation 79

Automation LLM DevOps Python 79

AWS Machine Learning Blog

OCTOBER 2, 2023

It is architected to automate the entire machine learning (ML) process, from data labeling to model training and deployment at the edge. Automating data labeling Data labeling is an inherently labor-intensive task that involves humans (labelers) to label the data. Let’s look at how to automate model building next!

Automation 110

Automation DevOps Machine Learning ML 110

AWS Machine Learning Blog

FEBRUARY 27, 2024

The SageMaker project template includes seed code corresponding to each step of the build and deploy pipelines (we discuss these steps in more detail later in this post) as well as the pipeline definition—the recipe for how the steps should be run. This is made possible by automating tedious, repetitive MLOps tasks as part of the template.

Machine Learning 129

Machine Learning DevOps Data Scientist Data Quality 129

IBM Journey to AI blog

AUGUST 2, 2023

Fact: All teams need access to the observability data The truth is that all teams— DevOps , SRE, Platform, ITOps and Development—need and deserve access to the data they want with the context of logical and physical dependencies across mobile, web, applications and infrastructure.

DevOps 176

DevOps Software Development Automation 176

AWS Machine Learning Blog

OCTOBER 2, 2023

In this post, we focus on how to automate the edge deployment part of the end-to-end MLOps pipeline. We show you how to use AWS IoT Greengrass to manage model inference at the edge and how to automate the process using AWS Step Functions and other AWS services. If you haven’t read it yet, we recommend checking out Part 1.

DevOps 107

DevOps Automation ML Machine Learning 107

AWS Machine Learning Blog

JUNE 11, 2024

This feature streamlines the process of launching new instances with the most up-to-date Neuron SDK, enabling you to automate your deployment workflows and make sure you’re always using the latest optimizations. Amazon ECS configuration For Amazon ECS, create a task definition that references your custom Docker image.

Deep Learning 113

Deep Learning ML Automation Auto-complete 113

Unite.AI

JUNE 23, 2023

There isn’t one definition of what “the edge” is, it’s actually a spectrum spanning out from the traditional data center or cloud at one end and extending all the way through to highly constrained devices. Many of our customers today run virtualized network functions, along with services like firewalls, SD-WAN, etc. at the edge.

DevOps 144

DevOps Automation AI AI 144

IBM Journey to AI blog

JULY 16, 2024

To maintain consistency and expedited deployments, Kate’s code repository is configured to trigger Azure DevOps pipeline builds, which has automation capability to perform all deployment operations. As a result of these changes, the user interface was now modern and intuitive, and the processes were automated and efficient.

DevOps 100

DevOps Automation Software Development Generative AI 100

AWS Machine Learning Blog

JANUARY 12, 2023

In this post, The Very Group shows how they use Amazon Comprehend to add a further layer of automated defense on top of policies to design threat modelling into all systems, to prevent PII from being sent in log data to Elasticsearch for indexing. Overview of solution.

DevOps 122

DevOps Natural Language Processing Metadata Automation 122

O'Reilly Media

FEBRUARY 25, 2025

You can move the slider forward and backward to see how this code runs step-by-step: AI Chat for Python Tutors Code Visualizer Way back in 2009 when I was a grad student, I envisioned creating Python Tutor to be an automated tutor that could help students with programming questions (which is why I chose that project name).

Generative AI 81

Generative AI Python LLM AI 81

AWS Machine Learning Blog

AUGUST 22, 2024

As you move from pilot and test phases to deploying generative AI models at scale, you will need to apply DevOps practices to ML workloads. In the notebook, we already added the @step decorator at the beginning of each function definition in the cell where the function was defined, as shown in the following code.

Generative AI 114

Generative AI Metadata Python ML 114

AWS Machine Learning Blog

SEPTEMBER 1, 2023

After the completion of the research phase, the data scientists need to collaborate with ML engineers to create automations for building (ML pipelines) and deploying models into production using CI/CD pipelines. All the produced models and code automation are stored in a centralized tooling account using the capability of a model registry.

Generative AI 120

Generative AI Prompt Engineering Prompt Engineer ML 120

Snorkel AI

JUNE 22, 2023

They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. Transparency is a critical ingredient in improving the quality and changing data through automated approaches. Most, most definitely. But the risks are real, and governance is critical to this process.

Data Quality 52

Data Quality Automation Data Scientist ML 52

Snorkel AI

JUNE 22, 2023

They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. Transparency is a critical ingredient in improving the quality and changing data through automated approaches. Most, most definitely. But the risks are real, and governance is critical to this process.

Data Quality 52

Data Quality Automation Data Scientist ML 52

Snorkel AI

JUNE 22, 2023

They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. Transparency is a critical ingredient in improving the quality and changing data through automated approaches. Most, most definitely. But the risks are real, and governance is critical to this process.

Data Quality 52

Data Quality Automation Data Scientist ML 52

AWS Machine Learning Blog

OCTOBER 16, 2023

They provide advanced technology that combines AI-powered automation with human feedback, deep insights, and expertise. Furthermore, DevOps were burdened with manually provisioning GPU instances in response to demand patterns.

Data Scientist 100

Data Scientist Computer Vision DevOps ML 100

AWS Machine Learning Blog

MARCH 26, 2024

In this post, we show how to automate the accounts payable process using Amazon Textract for data extraction. We also provide a reference architecture to build an invoice automation pipeline that enables extraction, verification, archival, and intelligent search. The following figure shows the Step Functions workflow.

IDP 107

IDP Metadata Data Extraction DevOps 107

AWS Machine Learning Blog

MAY 16, 2024

Problem definition Traditionally, the recommendation service was mainly provided by identifying the relationship between products and providing products that were highly relevant to the product selected by the customer. For more information about the model, refer to the paper Neural Collaborative Filtering.

Deep Learning 111

Deep Learning Auto-complete Algorithm ML 111

IBM Journey to AI blog

AUGUST 12, 2024

While microservices are often talked about in the context of their architectural definition, it can be easier to understand their business value by looking at them through the lens of their most popular enterprise benefits: Change or update code without affecting the rest of an application.

Software Development 112

Software Development Artificial Intelligence Artificial Intelligence DevOps 112

AWS Machine Learning Blog

JULY 8, 2024

This automated solution helps you get started quickly by providing all the code and configuration necessary to generate your unique images—all you need is images of your subject. However, this solution uses the equivalent GUI parameters as a pre-configured TOML file to automate the entire Stable Diffusion XL fine-tuning process.

ML 114

ML Automation DevOps Software Engineer 114

AWS Machine Learning Blog

JUNE 4, 2024

In this post, we provide guidance on how to create an Amazon Bedrock custom model using HashiCorp Terraform that allows you to automate the process, including preparing datasets used for customization. Terraform provides the benefits of automation, versioning, and repeatability. Kevon Mayers is a Solutions Architect at AWS.

Python 126

Python DevOps Automation Artificial Intelligence 126

AWS Machine Learning Blog

NOVEMBER 9, 2023

Automated retraining mechanism – The training pipeline built with SageMaker Pipelines is triggered whenever a data drift is detected in the inference pipeline. Under Advanced Project Options , for Definition , select Pipeline script from SCM. This will enable us to test the pattern to trigger automated retraining of the model.

Data Drift 116

Data Drift Auto-complete ML Automation 116

ODSC - Open Data Science

JULY 19, 2023

The data science team is now expected to be equipped with CI/CD skills to sustain ongoing inference with retraining cycles and automated redeployments of models. Definition of project team users, their roles, and access controls to other resources. Creation of Azure Machine Learning workspaces for the project.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52

The MLOps Blog

MARCH 28, 2023

The DevOps and Automation Ops departments are under the infrastructure team. This is the phase where they would expose the MVP with automation and structured engineering code put on top of the experiments they run. “We We are using the internal automation tools we already have to make it easy to show our model endpoints.

Machine Learning 59

Machine Learning Data Scientist Automation ML 59

Bugra Akyildiz

NOVEMBER 20, 2022

AI for DevOps to infuse AI/ML into the entire software development lifecycle to achieve high productivity. Use it to automate development workflows — including machine provisioning, model training and evaluation, comparing ML experiments across project history, and monitoring changing datasets. Do you have legacy notebooks?

ML 52

ML Data Science ETL DevOps 52

Explosion

AUGUST 3, 2017

Most of those insights have been used to make spaCy better: AI DevOps was hard, so we made sure models could be installed via pip. By definition, you can’t directly control what the process returns. Keeping the issue tracker tidy is something many open source projects struggle with – so automated tools could definitely be helpful.

Python 52

Python Convolutional Neural Networks Machine Learning Algorithm 52

Pickl AI

DECEMBER 30, 2024

Definition and Basic Concept of a Hypervisor At its core, a hypervisor acts as a Virtual Machine Monitor (VMM). Containers : Choose containers for lightweight, scalable applications, microservices, and DevOps workflows where rapid deployment is essential.

DevOps 52

DevOps Machine Learning Categorization Artificial Intelligence 52

The MLOps Blog

OCTOBER 3, 2023

Mikiko Bazeley: You definitely got the details correct. For me, it was a little bit of a longer journey because I kind of had data engineering and cloud engineering and DevOps engineering in between. I definitely don’t think I’m an influencer. And so what we do is version the definitions. For example, Feast.

ML 52

ML Data Scientist Machine Learning Data Science 52