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 294

DevOps Machine Learning Automation Big Data 294

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 190

Data Ingestion Big Data Explainability ETL 190

AI News

JULY 11, 2024

Software development emerges as the most popular area for AI investment (59%), followed by quality assurance (44%) and DevOps and automation (44%). Photo by Nick Fewings ) See also: Microsoft and Apple back away from OpenAI board Want to learn more about AI and big data from industry leaders?

Software Development 135

Software Development DevOps Big Data AI 135

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 247

Computer Vision Robotics Automation NLP 247

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.

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Metadata Data Quality Auto-classification DevOps 188

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 Automation 52

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 122

Artificial Intelligence Artificial Intelligence Automation DevOps 122

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.

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ML DevOps Data Scientist ML Engineer 153

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 112

DevOps Big Data Chatbots Artificial Intelligence 112

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.

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Python DevOps Machine Learning Data Science 97

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

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 132

ML Data Science Metadata DevOps 132

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 127

ML Data Scientist Machine Learning Automation 127

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

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 112

Software Development Artificial Intelligence Artificial Intelligence DevOps 112

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 134

ML Data Scientist ML Engineer Data Science 134

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 98

Data Mining Machine Learning Data Scientist Artificial Intelligence 98

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

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.

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Software Engineer Software Development DevOps Machine Learning 40

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.

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ML LLM Natural Language Processing Machine Learning 126

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.

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Data Drift Auto-complete ML Automation 130

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 110

Data Drift Metadata Data Quality ML 110

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.

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ML Software Development Automation Metadata 92

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 94

Data Scientist Data Quality Python ML 94

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

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

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.

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DevOps Big Data Artificial Intelligence Artificial Intelligence 281

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.

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DevOps Automation ML Machine Learning 236

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 135

Data Analysis Big Data Natural Language Processing Machine Learning 135

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 120

ML Machine Learning Automation Data Scientist 120

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.

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DevOps Data Quality Machine Learning Generative AI 147