Unite.AI

JANUARY 13, 2025

Unlike traditional AI tools that operate in isolation, Agent Laboratory creates a collaborative environment where these agents interact and build upon each other's work.

ML Engineer 179

ML Engineer Automation AI Research AI Researcher 179

AWS Machine Learning Blog

DECEMBER 12, 2024

The new SDK is designed with a tiered user experience in mind, where the new lower-level SDK ( SageMaker Core ) provides access to full breadth of SageMaker features and configurations, allowing for greater flexibility and control for ML engineers. In the following example, we show how to fine-tune the latest Meta Llama 3.1

ML 103

ML Python Data Scientist Natural Language Processing 103

Flipboard

MAY 31, 2023

Learn how to easily build any AI model and customize your own LLM in just a few lines of code with a declarative approach to machine learning.

ML Engineer 132

ML Engineer ML Machine Learning LLM 132

Analytics Vidhya

OCTOBER 22, 2021

This article was published as a part of the Data Science Blogathon Introduction Working as an ML engineer, it is common to be in situations where you spend hours to build a great model with desired metrics after carrying out multiple iterations and hyperparameter tuning but cannot get back to the same results with the […].

Machine Learning 348

Machine Learning ML Engineer Data Science ML 348

Analytics Vidhya

OCTOBER 6, 2023

Ray streamlines complex tasks for ML engineers, data scientists, and developers. Python Ray is a dynamic framework revolutionizing distributed computing. Developed by UC Berkeley’s RISELab, it simplifies parallel and distributed Python applications.

Python 343

Python ML Engineer Data Scientist ML 343

Analytics Vidhya

FEBRUARY 21, 2022

Image designed by the author – Shanthababu Introduction Every ML Engineer and Data Scientist must understand the significance of “Hyperparameter Tuning (HPs-T)” while selecting your right machine/deep learning model and improving the performance of the model(s). This article was published as a part of the Data Science Blogathon.

ML Engineer 337

ML Engineer Data Scientist Deep Learning Data Science 337

Towards AI

APRIL 4, 2023

Last Updated on April 4, 2023 by Editorial Team Introducing a Python SDK that allows enterprises to effortlessly optimize their ML models for edge devices. With their groundbreaking web-based Studio platform, engineers have been able to collect data, develop and tune ML models, and deploy them to devices.

ML Engineer 98

ML Engineer ML Python Neural Network 98

Towards AI

FEBRUARY 21, 2023

A sensible proxy sub-question might then be: Can ChatGPT function as a competent machine learning engineer? The Set Up If ChatGPT is to function as an ML engineer, it is best to run an inventory of the tasks that the role entails. ChatGPT’s job as our ML engineer […]

ML Engineer 98

ML Engineer ML ChatGPT Computer Vision 98

Analytics Vidhya

JUNE 12, 2023

Introduction Meet Tajinder, a seasoned Senior Data Scientist and ML Engineer who has excelled in the rapidly evolving field of data science. Tajinder’s passion for unraveling hidden patterns in complex datasets has driven impactful outcomes, transforming raw data into actionable intelligence.

Data Scientist 306

Data Scientist Machine Learning ML Engineer Actionable Intelligence 306

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.

ML 153

ML DevOps Data Scientist ML Engineer 153

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.

ML 135

ML Data Scientist ML Engineer Data Science 135

AWS Machine Learning Blog

OCTOBER 22, 2024

Amazon SageMaker is a cloud-based machine learning (ML) platform within the AWS ecosystem that offers developers a seamless and convenient way to build, train, and deploy ML models. He focuses on architecting and implementing large-scale generative AI and classic ML pipeline solutions.

ML Engineer 115

ML Engineer Data Scientist Machine Learning ML 115

AI News

MARCH 8, 2024

A job listing for an “Embodied Robotics Engineer” sheds light on the project’s goals, which include “designing, building, and maintaining open-source and low cost robotic systems that integrate AI technologies, specifically in deep learning and embodied AI.”

Robotics 348

Robotics Big Data Deep Learning Machine Learning 348

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

FEBRUARY 11, 2025

With a team of 30 AI researchers and ML engineers from Microsoft, Amazon, and top Ivy League institutions, Future AGI is at the forefront of AI innovation, bringing patented technologies and deep expertise to solve AIs most pressing challenges.

Auto-complete 147

Auto-complete ML Engineer AI AI 147

AWS Machine Learning Blog

OCTOBER 16, 2024

Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. SageMaker Processing provisions cluster resources for you to run city-, country-, or continent-scale geospatial ML workloads.

Machine Learning 121

Machine Learning ML Data Scientist Robotics 121

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 134

TheSequence

NOVEMBER 7, 2024

But how good is AI in traditional machine learning(ML) engineering tasks such as training or validation. This is the purpose of a new work proposed by OpenAI with MLE-Bench, a benchmark to evaluate AI agents in ML engineering tasks. One of the ultimate manifestations of this proposition is AI writing AI code.

OpenAI 59

OpenAI ML Engineer Machine Learning ML 59

AWS Machine Learning Blog

APRIL 8, 2025

With the support of AWS, iFood has developed a robust machine learning (ML) inference infrastructure, using services such as Amazon SageMaker to efficiently create and deploy ML models. In this post, we show how iFood uses SageMaker to revolutionize its ML operations.

Machine Learning 110

Machine Learning ML Auto-complete Data Scientist 110

AWS Machine Learning Blog

FEBRUARY 12, 2024

Sharing in-house resources with other internal teams, the Ranking team machine learning (ML) scientists often encountered long wait times to access resources for model training and experimentation – challenging their ability to rapidly experiment and innovate. If it shows online improvement, it can be deployed to all the users.

ML 136

ML Explainability Machine Learning Deep Learning 136

Flipboard

APRIL 17, 2025

The AI Model Serving team supports a wide range of models for both traditional machine learning (ML) and generative AI including LLMs, multi-modal foundation models (FMs), speech recognition, and computer vision-based models. He has over 7 years of experience in software and ML engineering with a focus on scalable NLP and speech solutions.

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Large Language Models Deep Learning Machine Learning AI 137

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 87

Data Science Data Scientist Data Ingestion DevOps 87

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.

ML 132

ML Python Deep Learning Machine Learning 132

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 Machine Learning 117

AWS Machine Learning Blog

DECEMBER 2, 2024

This enhancement allows customers running high-throughput production workloads to handle sudden traffic spikes more efficiently, providing more predictable scaling behavior and minimal impact on end-user latency across their ML infrastructure, regardless of the chosen inference framework.

Generative AI 110

Generative AI Machine Learning Large Language Models ML Engineer 110

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). ML technologies help computers achieve artificial intelligence. However, they differ fundamentally in their purpose and level of specialization in AI and ML environments.

Big Data 266

Big Data DevOps Automation Machine Learning 266

Flipboard

DECEMBER 12, 2023

Whether you're a seasoned ML engineer or a new LLM developer, these tools will help you get more productive and accelerate the development and deployment of your AI projects.

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LLM ML Engineer ML AI 165

Unite.AI

JANUARY 27, 2025

End users should also seek companies that can help with this testing as often an ML Engineer can help with deployment vs. the Data Scientist that created the model. If performance requirements can be met at a lower cost, those savings fall to the bottom line and might even make the solution viable.

Deep Learning 159

Deep Learning Data Quality ML Engineer Data Scientist 159

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 117

Marktechpost

NOVEMBER 16, 2024

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 Engineer Prompt Engineering 104

Marktechpost

SEPTEMBER 25, 2024

With the rapid advancement of technology, surpassing human abilities in tasks like image classification and language processing, evaluating the energy impact of ML is essential. Historically, ML projects prioritized accuracy over energy efficiency, contributing to increased energy consumption.

Machine Learning 104

Machine Learning ML Software Engineer ML Engineer 104

Unite.AI

JUNE 29, 2023

You can also explore the Google Cloud Skills Boost program, specifically designed for ML APIs, which offers extra support and expertise in this field. Optimizing workloads and costs To address the challenges of expensive and complex ML infrastructure, many companies increasingly turn to cloud services.

Machine Learning 260

Machine Learning ML AI AI 260

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.

ML 93

ML Machine Learning Python Computer Vision 93

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.

ML 107

ML Machine Learning Auto-complete Auto-classification 107

AWS Machine Learning Blog

JANUARY 23, 2025

The solution described in this post is geared towards machine learning (ML) engineers and platform teams who are often responsible for managing and standardizing custom environments at scale across an organization. This approach helps you achieve machine learning (ML) governance, scalability, and standardization.

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Automation DevOps Machine Learning ML 107

Towards AI

FEBRUARY 9, 2025

Lets understand the most useful linear feature scaling techniques of Machine Learning (ML) in detail! Source: Image by NIR HIMI on Unsplash Machine Learning (ML) is a very vast field & requires a proper approach to formulate the solution for every problem, irrespective of the solution or problem being small scale or large scale.

Machine Learning 70

Machine Learning ML Engineer ML AI 70

Flipboard

FEBRUARY 13, 2025

AI/ML engineers would prefer to focus on model training and data engineering, but the reality is that we also need to understand the infrastructure and mechanics […]

Machine Learning 135

Machine Learning ML Engineer ML AI 135