Flipboard

MARCH 27, 2023

Devops teams build their infrastructure as code, automate deployments with continuous integration/continuous delivery (CI/CD), and establish …

DevOps 166

DevOps Automation Big Data Machine Learning 166

Marktechpost

OCTOBER 27, 2024

Best Use Cases: Data science, machine learning, artificial intelligence, web development, automation, and scientific computing. It’s used in enterprise applications, Android app development, and big data processing. It’s used in system programming, network programming, cloud computing, and DevOps.

Data Analysis 133

Data Analysis Big Data Natural Language Processing Machine Learning 133

IBM Journey to AI blog

SEPTEMBER 5, 2024

Access to high-quality data can help organizations start successful products, defend against digital attacks, understand failures and pivot toward success. Emerging technologies and trends, such as machine learning (ML), artificial intelligence (AI), automation and generative AI (gen AI), all rely on good data quality.

Metadata 189

Metadata Data Quality Auto-classification DevOps 189

IBM Journey to AI blog

AUGUST 6, 2024

Reduced latency: In a serverless environment, code runs closer to the end user, decreasing its latency , which is the amount of time it takes for data to travel from one point to another on a network. Big data analytics Serverless dramatically reduces the cost and complexity of writing and deploying code for big data applications.

DevOps 113

DevOps Big Data Chatbots Artificial Intelligence 113

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. And everybody agrees that in production, this should be automated.” Yet this leads into another important point.

Data Ingestion 165

Data Ingestion Explainability Big Data ETL 165

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 317

DevOps Automation Generative AI Big Data 317

IBM Journey to AI blog

JULY 11, 2024

AI can also provide actionable recommendations to address issues and augment incomplete or inconsistent data, leading to more accurate insights and informed decision-making. Developments in machine learning , automation and predictive analytics are helping operations managers improve planning and streamline workflows.

Artificial Intelligence 124

Artificial Intelligence Artificial Intelligence Automation DevOps 124

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 315

DevOps Machine Learning Automation Big Data 315

IBM Journey to AI blog

JULY 11, 2024

AI can also provide actionable recommendations to address issues and augment incomplete or inconsistent data, leading to more accurate insights and informed decision-making. Developments in machine learning , automation and predictive analytics are helping operations managers improve planning and streamline workflows.

Artificial Intelligence 100

Artificial Intelligence Artificial Intelligence Automation DevOps 100

ODSC - Open Data Science

OCTOBER 9, 2024

By automating repetitive tasks and generating boilerplate code, these tools free up time for engineers to focus on more complex, creative aspects of software development. Well, it is offering a way to automate the time-consuming process of writing and running tests. Just keep in mind, that this shouldn’t replace the human element.

Software Engineer 40

Software Engineer Software Development DevOps ETL 40

IBM Journey to AI blog

FEBRUARY 14, 2024

McDonald’s is building AI solutions for customer care with IBM Watson AI technology and NLP to accelerate the development of its automated order taking (AOT) technology. For example, Amazon reminds customers to reorder their most often-purchased products, and shows them related products or suggestions.

Computer Vision 261

Computer Vision Robotics Automation NLP 261

Pickl AI

JANUARY 30, 2023

The role of Python is not just limited to Data Science. It’s a universal programming language that finds application in different technologies like AI, ML, Big Data and others. Data Automation: Automate data processing pipelines and workflows using Python scripting and libraries such as PyAutoGUI and Task Scheduler.

Python 97

Python DevOps Machine Learning Data Science 97

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 149

ML DevOps Data Scientist ML Engineer 149

AWS Machine Learning Blog

NOVEMBER 7, 2024

Regardless of the models used, they all include data preprocessing, training, and inference over several billions of records containing weekly data spanning multiple years and markets to produce forecasts. A fully automated production workflow The MLOps lifecycle starts with ingesting the training data in the S3 buckets.

ML 109

ML Machine Learning Automation Data Scientist 109

Flipboard

NOVEMBER 22, 2024

The functional architecture with different capabilities is implemented using a number of AWS services, including AWS Organizations , Amazon SageMaker , AWS DevOps services, and a data lake. Conclusion Effective governance is crucial for organizations to unlock their data’s potential while maintaining compliance and security.

ML 119

ML Data Science Metadata DevOps 119

ODSC - Open Data Science

JULY 24, 2023

This firm is a leader in AI and NLP-powered no-code solutions that help build AI co-workers that help “automate complex people- and process-centric processes across functions.” This push for what they call “AI co-workers” allow companies to automate complex business processes that would normally keep their human employees focused.

Data Science 52

Data Science Chatbots Automation Big Data 52

IBM Journey to AI blog

AUGUST 12, 2024

The difference comes in the level of control vs. the level of automation the two architectures offer an organization. While microservices offers greater control over the development environment, it also requires a higher level of expertise for developers when it comes to DevOps , the methodology that enables application development.

Software Development 113

Software Development Artificial Intelligence Artificial Intelligence DevOps 113

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 117

ML Data Scientist Machine Learning Automation 117

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 Data Scientist 52

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.

Data Mining 94

Data Mining Machine Learning Data Scientist Artificial Intelligence 94

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 123

Python Data Scientist Data Science Automation 123

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 Scientist Data Quality 134

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 126

ML Data Scientist ML Engineer Data Science 126

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 116

LLM ML Natural Language Processing Machine Learning 116

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 83

ML Software Development Automation Metadata 83

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

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 124

Data Drift Auto-complete ML Automation 124

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 85

Data Scientist Data Quality Python ML 85

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 96

Data Drift Metadata Data Quality ML 96

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 298

DevOps Big Data Artificial Intelligence Artificial Intelligence 298

IBM Journey to AI blog

JUNE 5, 2024

However, SaaS architectures can easily overwhelm DevOps teams with data aggregation, sorting and analysis tasks. Broadly speaking, application analytics refers to the process of collecting application data and performing real-time analysis of SaaS, mobile, desktop and web application performance and usage data.

DevOps 240

DevOps Automation Machine Learning ML 240

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