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). Scope and focus AIOps methodologies are fundamentally geared toward enhancing and automating IT operations. AIOps and MLOps: What’s the difference?

Big Data 266

Big Data DevOps Automation Machine Learning 266

AWS Machine Learning Blog

FEBRUARY 29, 2024

Creating scalable and efficient machine learning (ML) pipelines is crucial for streamlining the development, deployment, and management of ML models. In this post, we present a framework for automating the creation of a directed acyclic graph (DAG) for Amazon SageMaker Pipelines based on simple configuration files.

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Automation Python Machine Learning ML 116

Unite.AI

JUNE 29, 2023

They realize how it can help draw valuable insights from data, streamline operations through smart automation, and create unrivaled customer experiences. By employing DocumentAI, businesses can automate document-related workflows, saving time and improving accuracy.

Machine Learning 262

Machine Learning ML AI AI 262

Towards AI

APRIL 27, 2023

for e.g., if a manufacturing or logistics company is collecting recording data from CCTV across its manufacturing hubs and warehouses, there could be a potentially a good number of use cases ranging from workforce safety, visual inspection automation, etc. 99% of consultants will rather ask you to actually execute these POCs.

Automation 98

Automation Auto-complete Computer Vision ChatGPT 98

Snorkel AI

MARCH 20, 2025

However, if we can capture SME domain knowledge in the form of well-defined acceptance criteria, and scale it via automated, specialized evaluators, we can accelerate evaluation exponentially from several weeks or more to a few hours or less. Its far more likely that the AI/ML engineer needs to go back and continue iterating on the prompt.

ML Engineer 52

ML Engineer Automation Prompt Engineer Prompt Engineering 52

IBM Journey to AI blog

AUGUST 11, 2023

It advances the scalability of ML in real-world applications by using algorithms to improve model performance and reproducibility. MLOps aims to streamline the time and resources it takes to run data science models using automation, ML and iterative improvements on each model version. What is MLOps? Where they are deployed.

Data Science 170

Data Science DevOps Machine Learning Data Scientist 170

AWS Machine Learning Blog

MARCH 22, 2023

Automating the whole workflow can help reduce manual work. In this post, we show how you can use AWS Step Functions to build and automate the workflow. The workflow allows application developers and ML engineers to automate the custom label classification steps for any computer vision use case.

Automation 95

Automation Machine Learning ML Computer Vision 95

AWS Machine Learning Blog

DECEMBER 19, 2023

Artificial intelligence (AI) and machine learning (ML) are becoming an integral part of systems and processes, enabling decisions in real time, thereby driving top and bottom-line improvements across organizations. However, putting an ML model into production at scale is challenging and requires a set of best practices.

Machine Learning 127

Machine Learning ML Engineer DevOps ML 127

AWS Machine Learning Blog

MARCH 1, 2023

Continuous ML model retraining is one method to overcome this challenge by relearning from the most recent data. This requires not only well-designed features and ML architecture, but also data preparation and ML pipelines that can automate the retraining process.

Automation 100

Automation ETL Data Drift ML 100

Towards AI

AUGUST 7, 2024

From Solo Notebooks to Collaborative Powerhouse: VS Code Extensions for Data Science and ML Teams Photo by Parabol | The Agile Meeting Toolbox on Unsplash In this article, we will explore the essential VS Code extensions that enhance productivity and collaboration for data scientists and machine learning (ML) engineers.

Data Science 116

Data Science ML ML Engineer Data Scientist 116

AWS Machine Learning Blog

FEBRUARY 21, 2025

To promote the success of this migration, we collaborated with the AWS team to create automated and intelligent digital experiences that demonstrated Rockets understanding of its clients and kept them connected. With just one part-time ML engineer for support, our average issue backlog with the vendor is practically non-existent.

Data Science 70

Data Science Data Scientist Data Ingestion DevOps 70

Unite.AI

JUNE 19, 2024

Operations ML Model Deployment : Implementing and deploying ML models into production environments. CI/CD Pipelines : Setting up continuous integration and delivery pipelines to automate model updates and deployments. ML Operations : Deploy and maintain ML models using established DevOps practices.

Software Engineer 182

Software Engineer DevOps Machine Learning ML 182

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. JuMa is now available to all data scientists, ML engineers, and data analysts at BMW Group.

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ML DevOps Data Scientist ML Engineer 151

ODSC - Open Data Science

FEBRUARY 9, 2023

That responsibility usually falls in the hands of a role called Machine Learning (ML) Engineer. Having empathy for your ML Engineering colleagues means helping them meet operational constraints. To continue with this analogy, you might think of the ML Engineer as the data scientist’s “editor.”

Data Scientist 105

Data Scientist Machine Learning ML Engineer ML 105

AWS Machine Learning Blog

JULY 8, 2024

TWCo data scientists and ML engineers took advantage of automation, detailed experiment tracking, integrated training, and deployment pipelines to help scale MLOps effectively. Amazon CloudWatch – Collects and visualizes real-time logs that provide the basis for automation. Used to deploy training and inference code.

Data Scientist 114

Data Scientist ML Engineer Machine Learning Data Science 114

AWS Machine Learning Blog

FEBRUARY 11, 2025

Clean up To clean up the model and endpoint, use the following code: predictor.delete_model() predictor.delete_endpoint() Conclusion In this post, we explored how SageMaker JumpStart empowers data scientists and ML engineers to discover, access, and run a wide range of pre-trained FMs for inference, including the Falcon 3 family of models.

ML 79

ML Machine Learning Python Computer Vision 79

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. Register a successful model in the Amazon SageMaker Model Registry.

Automation 83

Automation LLM DevOps Python 83

Marktechpost

JANUARY 10, 2024

Automated Machine Learning (AutoML) has been introduced to address the pressing need for proactive and continual learning in content moderation defenses on the LinkedIn platform. It is a framework for automating the entire machine-learning process, specifically focusing on content moderation classifiers.

Machine Learning 101

Machine Learning Continuous Learning ML Engineer Automation 101

ODSC - Open Data Science

FEBRUARY 13, 2025

Using a step-by-step approach, he demonstrated how to integrate AI models with structured databases, enabling automated insights generation, query execution, and data visualization. He also demonstrated workflow automation using Koo.ai, highlighting how AI-driven knowledge extraction can enhance research dissemination.

Large Language Models 52

Large Language Models AI AI Automation 52

AWS Machine Learning Blog

OCTOBER 20, 2023

This framework considers multiple personas and services to govern the ML lifecycle at scale. An ML engineer deploys the model pipeline into the ML team test environment using a shared services CI/CD process. After stakeholder validation, the ML model is deployed to the team’s production environment.

ML 126

ML Data Scientist ML Engineer Data Science 126

Marktechpost

JUNE 19, 2024

Its ability to automate and enhance creative tasks makes it a valuable skill for professionals across industries. It is ideal for ML engineers, data scientists, and technical leaders, providing real-world training for production-ready generative AI using Amazon Bedrock and cloud-native services.

Artificial Intelligence 121

Artificial Intelligence Artificial Intelligence Prompt Engineering Prompt Engineer 121

AWS Machine Learning Blog

FEBRUARY 12, 2025

Machine learning (ML) engineers must make trade-offs and prioritize the most important factors for their specific use case and business requirements. Along with protecting against toxicity and harmful content, it can also be used for Automated Reasoning checks , which helps you protect against hallucinations.

Auto-classification 83

Auto-classification Metadata Generative AI Machine Learning 83

AWS Machine Learning Blog

SEPTEMBER 4, 2024

Specifically for the model building stage, Amazon SageMaker Pipelines automates the process by managing the infrastructure and resources needed to process data, train models, and run evaluation tests. Solution overview We consider a use case in which an ML engineer configures a SageMaker model building pipeline using a Jupyter notebook.

DevOps 101

DevOps ML Engineer ML Metadata 101

AWS Machine Learning Blog

MARCH 18, 2025

Streamlined data collection and analysis Automating the process of extracting relevant data points from patient-physician interactions can significantly reduce the time and effort required for manual data entry and analysis, enabling more efficient clinical trial management.

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LLM NLP Data Integration AI 80

AWS Machine Learning Blog

OCTOBER 28, 2024

For automated alerts for model monitoring, creating an Amazon Simple Notification Service (Amazon SNS) topic is recommended, which email user groups will subscribe to for alerts on a given CloudWatch metric alarm. Ajay Raghunathan is a Machine Learning Engineer at AWS. About the Authors Joe King is a Sr.

ML 104

ML Metadata Data Scientist DevOps 104

Pickl AI

JANUARY 30, 2023

Data Automation: Automate data processing pipelines and workflows using Python scripting and libraries such as PyAutoGUI and Task Scheduler. Scripting: Use Python as a scripting language to automate and simplify tasks and processes. Data Visualization: Use libraries such as Matplotlib, Seaborn, Plotly, etc.,

Python 97

Python DevOps Machine Learning Data Science 97

AWS Machine Learning Blog

JANUARY 5, 2024

Artificial intelligence (AI) and machine learning (ML) offerings from Amazon Web Services (AWS) , along with integrated monitoring and notification services, help organizations achieve the required level of automation, scalability, and model quality at optimal cost.

Data Science 131

Data Science Data Drift DevOps Auto-complete 131

AWS Machine Learning Blog

JUNE 27, 2023

Earth.com didn’t have an in-house ML engineering team, which made it hard to add new datasets featuring new species, release and improve new models, and scale their disjointed ML system. All steps are run in an automated manner after the pipeline has been run. Endpoints had to be deployed manually as well.

DevOps 114

DevOps ML Machine Learning ML Engineer 114

AWS Machine Learning Blog

JUNE 19, 2024

About the Authors Rushabh Lokhande is a Senior Data & ML Engineer with AWS Professional Services Analytics Practice. Andrew Ang is a Senior ML Engineer with the AWS Generative AI Innovation Center, where he helps customers ideate and implement generative AI proof of concept projects.

ML Engineer 100

ML Engineer Generative AI Big Data ML 100

AWS Machine Learning Blog

NOVEMBER 16, 2023

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

Data Scientist 113

Data Scientist ML Data Science Automation 113

TheSequence

JULY 21, 2024

Sergio Ferragut, Principal Developer Advocate at Tecton, will show how to enhance collaboration, automate feature materialization, and support diverse data types. He will also cover how these frameworks automate production-ready pipelines, speeding up AI projects and making AI-powered applications more intelligent.

ML Engineer 75

ML Engineer Automation LLM OpenAI 75

Towards AI

JANUARY 27, 2024

pub.towardsai.net Conclusion From the page, it is evident that the AutoKeras library facilitates the automation of developing deep learning models with minimal code.I Time Series Forecasting using PyCaret This page explains how to do forecasting using Python’s low-code AutoML library PyCaret.

Deep Learning 111

Deep Learning Explainability ML Engineer Data Scientist 111

ODSC - Open Data Science

SEPTEMBER 21, 2023

Secondly, to be a successful ML engineer in the real world, you cannot just understand the technology; you must understand the business. The other tendency to watch out for in the real world (to go along with let’s use ML for everything ) is the worry that people have that ML is coming for their job and should not be trusted.

Machine Learning 98

Machine Learning ML Engineer ML Data Science 98

AWS Machine Learning Blog

FEBRUARY 11, 2025

This cutting-edge model supports long-context processing, complex segmentation scenarios, and fine-grained analysis, making it ideal for automating processes for various industries such as medical imaging in healthcare, satellite imagery for environment monitoring, and object segmentation for autonomous systems. Meta SAM 2.1 Meta SAM 2.1

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Computer Vision ML Python Automation 80

AWS Machine Learning Blog

OCTOBER 2, 2023

An MLOps pipeline allows to automate the full ML lifecycle from data labeling to model training and deployment. Implementing an MLOps pipeline at the edge introduces additional complexities that make the automation, integration, and maintenance processes more challenging due to the increased operational overhead involved.

DevOps 115

DevOps Automation ML Computer Vision 115

Unite.AI

FEBRUARY 28, 2024

Vulnerable system compromise : LLMs could potentially assist hackers by automating components of cyberattacks. Foster closer collaboration between security teams and ML engineers to instill security best practices. Digital impersonation : Fake user accounts powered by LLMs can spread inflammatory content while evading detection.

Large Language Models 162

Large Language Models Machine Learning LLM Neural Network 162

AWS Machine Learning Blog

JANUARY 17, 2023

This development approach can be used in combination with other common software engineering best practices such as automated code deployments, tests, and CI/CD pipelines. The AWS CDK reduces the time required to perform typical infrastructure deployment tasks while shrinking the surface area for human error through automation.

Software Engineer 94

Software Engineer ML Engineer ML Machine Learning 94