IBM Journey to AI blog

JULY 24, 2023

ITOA turns operational data into real-time insights. It is often a part of AIOps , which uses artificial intelligence (AI) and machine learning to improve the overall DevOps of an organization so the organization can provide better service. It aims to understand what’s happening within a system by studying external data.

DevOps 148

DevOps Automation Big Data Data Mining 148

AWS Machine Learning Blog

NOVEMBER 9, 2023

Batch predictions with model monitoring – The inference pipeline built with Amazon SageMaker Pipelines runs on a scheduled basis to generate predictions along with model monitoring using SageMaker Model Monitor to detect data drift. data/ mammo-train-dataset-part2.csv data/mammo-batch-dataset.csv – Will be used to generate inferences.

Data Drift 99

Data Drift Auto-complete ML Automation 99

IBM Journey to AI blog

FEBRUARY 6, 2024

Automation Automation tools are a significant feature of cloud-based infrastructure. Cloud-based applications and services Cloud-based applications and services support myriad business use cases—from backup and disaster recovery to big data analytics to software development.

DevOps 273

DevOps Automation Software Development Big Data 273

AWS Machine Learning Blog

APRIL 21, 2023

This allows you to automate both pipelines while incorporating the different lifecycles between training and inference. At a minimum, it’s recommended to automate exception handling by filtering logs and creating alarms. At a minimum, it’s recommended to automate exception handling by filtering logs and creating alarms.

Data Drift 74

Data Drift Metadata Data Quality ML 74

Flipboard

NOVEMBER 24, 2023

This offering enables BMW ML engineers to perform code-centric data analytics and ML, increases developer productivity by providing self-service capability and infrastructure automation, and tightly integrates with BMW’s centralized IT tooling landscape. A data scientist team orders a new JuMa workspace in BMW’s Catalog.

ML 95

ML DevOps Data Scientist ML Engineer 95

IBM Journey to AI blog

DECEMBER 11, 2023

Kubernetes , Docker Swarm ) to automate the deployment of apps across all clouds. With a hybrid-cloud architecture, the airlines can deal with fluctuating volumes of data, such as during the busy holiday travel season, which requires scaling up resources and data in real-time to improve workflows and deliver better customer experiences.

DevOps 278

DevOps Automation Generative AI Big Data 278

IBM Journey to AI blog

DECEMBER 20, 2023

Also known as “k8s” or “kube,” Kubernetes is a container orchestration platform for scheduling and automating the deployment, management and scaling of containerized applications. Workloads involving web content, big data analytics and AI are ideal for a hybrid cloud infrastructure.

DevOps 258

DevOps Machine Learning Automation Big Data 258

AWS Machine Learning Blog

OCTOBER 20, 2023

The functional architecture with different capabilities is implemented using a number of AWS services, including AWS Organizations , SageMaker, AWS DevOps services, and a data lake. A framework for vending new accounts is also covered, which uses automation for baselining new accounts when they are provisioned.

ML 101

ML Data Scientist ML Engineer Data Science 101

Marktechpost

JULY 17, 2023

Data gathering, pre-processing, modeling, and deployment are all steps in the iterative process of predictive analytics that results in output. We can automate the procedure to deliver forecasts based on new data continuously fed throughout time. This tool’s user-friendly UI consistently receives acclaim from users.

Machine Learning 93

Machine Learning Data Mining Data Scientist Artificial Intelligence 93

ODSC - Open Data Science

JULY 19, 2023

A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team. For the customer, this helps them reduce the time it takes to bootstrap a new data science project and get it to production.

Machine Learning 52

Machine Learning Data Drift Data Science ML Engineer 52

AWS Machine Learning Blog

NOVEMBER 14, 2023

In the era of big data and AI, companies are continually seeking ways to use these technologies to gain a competitive edge. At the core of these cutting-edge solutions lies a foundation model (FM), a highly advanced machine learning model that is pre-trained on vast amounts of data.

LLM 89

LLM ML Natural Language Processing Machine Learning 89

The MLOps Blog

JUNE 27, 2023

This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics. Automated pipelining and workflow orchestration: Platforms should provide tools for automated pipelining and workflow orchestration, enabling you to define and manage complex ML pipelines.

Machine Learning 134

Machine Learning Metadata Data Quality Data Scientist 134

AWS Machine Learning Blog

AUGUST 31, 2023

In addition to data engineers and data scientists, there have been inclusions of operational processes to automate & streamline the ML lifecycle. Through automation, that model card is shared with ML Prod account in read-only mode. His core area of focus includes Machine Learning, DevOps, and Containers.

ML 91

ML Data Scientist Machine Learning Automation 91

Flipboard

MARCH 7, 2023

You can implement comprehensive tests, governance, security guardrails, and CI/CD automation to produce custom app images. Implement an automated image authoring process As already mentioned, you can use the Studio Image Build CLI to implement an automated CI/CD process of app image creation and deployment with CodeBuild and sm-docker CLI.

Python 93

Python Automation Data Scientist ML 93

AWS Machine Learning Blog

AUGUST 31, 2023

In addition to data engineers and data scientists, there have been inclusions of operational processes to automate & streamline the ML lifecycle. Through automation, that model card is shared with ML Prod account in read-only mode. His core area of focus includes Machine Learning, DevOps, and Containers.

ML 52

ML Data Scientist Machine Learning Automation 52

AWS Machine Learning Blog

FEBRUARY 21, 2023

Amazon SageMaker for MLOps provides purpose-built tools to automate and standardize steps across the ML lifecycle, including capabilities to deploy and manage new models using advanced deployment patterns. Similar to traditional CI/CD systems, we want to automate software tests, integration testing, and production deployments.

ML 70

ML Automation Software Development Metadata 70

AWS Machine Learning Blog

AUGUST 29, 2023

In this post, we describe how to create an MLOps workflow for batch inference that automates job scheduling, model monitoring, retraining, and registration, as well as error handling and notification by using Amazon SageMaker , Amazon EventBridge , AWS Lambda , Amazon Simple Notification Service (Amazon SNS), HashiCorp Terraform, and GitLab CI/CD.

Data Scientist 72

Data Scientist Data Quality Python ML 72

Unite.AI

MARCH 21, 2024

She has innovated and delivered several product lines and services specializing in distributed systems, cloud computing, big data, machine learning and security. In this way, companies must be proactive about data cleanup and taxonomy, and there are opportunities to use generative AI to manage your AI governance and quality.

DevOps 147

DevOps Data Quality Machine Learning Generative AI 147

IBM Journey to AI blog

MARCH 20, 2024

Cloud-native applications and DevOps A public cloud setting supports cloud-native applications—software programs that consist of multiple small, interdependent services called microservices , a crucial part of DevOps practices. When developers finish using a testing environment, they can easily take it down.

DevOps 246

DevOps Big Data Artificial Intelligence Artificial Intelligence 246