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. Data scientists and ML engineers require capable tooling and sufficient compute for their work.

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ML DevOps Data Scientist ML Engineer 153

Marktechpost

NOVEMBER 16, 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 104

Artificial Intelligence Artificial Intelligence Prompt Engineering Prompt Engineer 104

AWS Machine Learning Blog

OCTOBER 20, 2023

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.

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ML Data Scientist ML Engineer Data Science 124

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

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. You can also explore the Google Cloud Skills Boost program, specifically designed for ML APIs, which offers extra support and expertise in this field.

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Machine Learning ML AI AI 262

AWS Machine Learning Blog

MAY 9, 2024

The rapid advancements in artificial intelligence and machine learning (AI/ML) have made these technologies a transformative force across industries. An effective approach that addresses a wide range of observed issues is the establishment of an AI/ML center of excellence (CoE). What is an AI/ML CoE?

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ML Generative AI AI AI 128

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.

Automation 115

Automation Python Machine Learning ML 115

Unite.AI

JUNE 19, 2024

In world of Artificial Intelligence (AI) and Machine Learning (ML), a new professionals has emerged, bridging the gap between cutting-edge algorithms and real-world deployment. Meet the MLOps Engineer: the orchestrating the seamless integration of ML models into production environments, ensuring scalability, reliability, and efficiency.

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Software Engineer DevOps Machine Learning ML 182

IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. What is MLOps?

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Data Science DevOps Machine Learning Data Scientist 170

AWS Machine Learning Blog

FEBRUARY 21, 2025

Data exploration and model development were conducted using well-known machine learning (ML) tools such as Jupyter or Apache Zeppelin notebooks. To address the legacy data science environment challenges, Rocket decided to migrate its ML workloads to the Amazon SageMaker AI suite.

Data Science 70

Data Science Data Scientist Data Ingestion DevOps 70

AWS Machine Learning Blog

APRIL 26, 2023

Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? Most executives think ML can apply to any business decision, but on average only half of the ML projects make it to production. Challenges Customers may face several challenges when implementing machine learning (ML) solutions.

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ML Machine Learning Data Science Data Drift 110

AWS Machine Learning Blog

FEBRUARY 11, 2025

Get started with SageMaker JumpStart SageMaker JumpStart is a machine learning (ML) hub that can help accelerate your ML journey. Marc Karp is an ML Architect with the Amazon SageMaker Service team. He focuses on helping customers design, deploy, and manage ML workloads at scale.

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ML Machine Learning Python Computer Vision 81

AWS Machine Learning Blog

OCTOBER 28, 2024

Real-world applications vary in inference requirements for their artificial intelligence and machine learning (AI/ML) solutions to optimize performance and reduce costs. SageMaker Model Monitor monitors the quality of SageMaker ML models in production. Your client applications invoke this endpoint to get inferences from the model.

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ML Metadata Data Scientist DevOps 103

AWS Machine Learning Blog

SEPTEMBER 20, 2023

In these scenarios, as you start to embrace generative AI, large language models (LLMs) and machine learning (ML) technologies as a core part of your business, you may be looking for options to take advantage of AWS AI and ML capabilities outside of AWS in a multicloud environment.

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ML Python Deep Learning Machine Learning 123

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.

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Machine Learning ML Engineer DevOps ML 127

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 Engineering Prompt Engineer 52

AWS Machine Learning Blog

MARCH 1, 2023

Statistical methods and machine learning (ML) methods are actively developed and adopted to maximize the LTV. In this post, we share how Kakao Games and the Amazon Machine Learning Solutions Lab teamed up to build a scalable and reliable LTV prediction solution by using AWS data and ML services such as AWS Glue and Amazon SageMaker.

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Automation ETL Data Drift ML 99

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.

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Automation Auto-complete Computer Vision ChatGPT 98

AWS Machine Learning Blog

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts.

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ML Machine Learning Auto-complete Auto-classification 92

AWS Machine Learning Blog

MARCH 22, 2023

Generating this data can take months to gather and require large teams of labelers to prepare it for use in machine learning (ML). Behind the scenes, Rekognition Custom Labels automatically loads and inspects the training data, selects the right ML algorithms, trains a model, and provides model performance metrics.

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Automation Machine Learning ML Computer Vision 92

AWS Machine Learning Blog

JULY 8, 2024

As industries begin adopting processes dependent on machine learning (ML) technologies, it is critical to establish machine learning operations (MLOps) that scale to support growth and utilization of this technology. There were noticeable challenges when running ML workflows in the cloud.

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Data Scientist ML Engineer Machine Learning Data Science 113

ODSC - Open Data Science

APRIL 2, 2025

In 2025, artificial intelligence isnt just trendingits transforming how engineering teams build, ship, and scale software. Whether its automating code, enhancing decision-making, or building intelligent applications, AI is rewriting what it means to be a modern engineer. Jupyter notebooks remain a staple for data scientists.

Prompt Engineering 52

Prompt Engineering Prompt Engineer Large Language Models ML Engineer 52

AWS Machine Learning Blog

JUNE 9, 2023

a low-code enterprise graph machine learning (ML) framework to build, train, and deploy graph ML solutions on complex enterprise-scale graphs in days instead of months. With GraphStorm, we release the tools that Amazon uses internally to bring large-scale graph ML solutions to production. license on GitHub. GraphStorm 0.1

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ML Machine Learning BERT Neural Network 101

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.”

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Data Scientist Machine Learning ML Engineer ML 105

The MLOps Blog

JUNE 13, 2024

Data scientists often lack focus, time, or knowledge about software engineering principles. As a result, poor code quality and reliance on manual workflows are two of the main issues in ML development processes. I started as a full-stack developer but have gradually moved toward data and ML engineering.

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ML Data Scientist Automation Software Development 59

AWS Machine Learning Blog

FEBRUARY 7, 2025

This post, part of the Governing the ML lifecycle at scale series ( Part 1 , Part 2 , Part 3 ), explains how to set up and govern a multi-account ML platform that addresses these challenges. An enterprise might have the following roles involved in the ML lifecycles. This ML platform provides several key benefits.

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ML Data Scientist ML Engineer Data Science 64

AWS Machine Learning Blog

JUNE 23, 2023

For data scientists, moving machine learning (ML) models from proof of concept to production often presents a significant challenge. Additionally, you can use AWS Lambda directly to expose your models and deploy your ML applications using your preferred open-source framework, which can prove to be more flexible and cost-effective.

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Large Language Models ML Python Machine Learning 99

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 102

Machine Learning Continuous Learning ML Engineer Automation 102

AWS Machine Learning Blog

SEPTEMBER 4, 2024

Its scalability and load-balancing capabilities make it ideal for handling the variable workloads typical of machine learning (ML) applications. Amazon SageMaker provides capabilities to remove the undifferentiated heavy lifting of building and deploying ML models. They often work with DevOps engineers to operate those pipelines.

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DevOps ML Engineer ML Metadata 99

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. Create a complete AI/ML pipeline for fine-tuning an LLM using drag-and-drop functionality.

Automation 83

Automation LLM DevOps Python 83

AWS Machine Learning Blog

JUNE 27, 2023

When machine learning (ML) models are deployed into production and employed to drive business decisions, the challenge often lies in the operation and management of multiple models. They swiftly began to work on AI/ML capabilities by building image recognition models using Amazon SageMaker.

DevOps 114

DevOps ML Machine Learning ML Engineer 114

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 82

AWS Machine Learning Blog

JANUARY 5, 2024

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.

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Data Science Data Drift DevOps Auto-complete 131

AWS Machine Learning Blog

FEBRUARY 27, 2024

In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker. This is a guest post written by Axfood AB.

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Machine Learning DevOps Data Scientist Data Quality 133

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 111

Data Scientist ML Data Science Automation 111

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 124

Artificial Intelligence Artificial Intelligence Prompt Engineering Prompt Engineer 124

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. Some typical examples are given in the following table, along with some discussion as to whether or not ML would be an appropriate tool for solving the problem: Figure 1.1:

Machine Learning 98

Machine Learning ML Engineer ML Data Science 98