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 134

Machine Learning ML Engineer ML AI 134

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 131

ML Engineer ML Machine Learning LLM 131

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. Janosch Woschitz is a Senior Solutions Architect at AWS, specializing in AI/ML. He is an ACM Fellow and IEEE Fellow.

Machine Learning 120

Machine Learning ML Data Scientist Robotics 120

TheSequence

NOVEMBER 7, 2024

Created Using Midjourney Coding the engineering are one of the areas that has been at the frontiers of generative AI. One of the ultimate manifestations of this proposition is AI writing AI code. But how good is AI in traditional machine learning(ML) engineering tasks such as training or validation.

OpenAI 59

OpenAI ML Engineer Machine Learning ML 59

Towards AI

JULY 2, 2024

Last Updated on July 3, 2024 by Editorial Team Author(s): Boris Meinardus Originally published on Towards AI. What are the most important skills for an ML Engineer? So, in this blog post, I want to share the 7 most important skill sets you might need to know if you want to become an ML engineer.

ML Engineer 98

ML Engineer ML Software Engineer Python 98

Marktechpost

OCTOBER 10, 2024

In today’s tech-driven world, data science and machine learning are often used interchangeably. This article explores the differences between data science vs. machine learning , highlighting their key functions, roles, and applications. What is Machine Learning? However, they represent distinct fields.

Data Science 108

Data Science Machine Learning Data Scientist ML Engineer 108

Unite.AI

SEPTEMBER 3, 2024

Computational power has become a critical factor in pushing the boundaries of what's possible in machine learning. As models grow more complex and datasets expand exponentially, traditional CPU-based computing often falls short of meeting the demands of modern machine learning tasks.

Machine Learning 130

Machine Learning Convolutional Neural Networks Neural Network Algorithm 130

Marktechpost

OCTOBER 12, 2024

Machine Learning (ML) models have shown promising results in various coding tasks, but there remains a gap in effectively benchmarking AI agents’ capabilities in ML engineering. MLE-bench is a novel benchmark aimed at evaluating how well AI agents can perform end-to-end machine learning engineering.

Machine Learning 113

Machine Learning OpenAI ML Engineer ML 113

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 133

Machine Learning ML Engineer DevOps ML 133

Towards AI

JANUARY 28, 2025

Last Updated on January 28, 2025 by Editorial Team Author(s): Deltan Lobo Originally published on Towards AI. Photo by Markus Winkler on Unsplash You might have wandered the internet for a complete roadmap to learn ML. Without consistency forget of being an ML engineer or ML geek. Published via Towards AI

Machine Learning 55

Machine Learning ML Engineer ML AI 55

Towards AI

FEBRUARY 21, 2023

Author(s): Eric Landau, Co-founder and CEO, Encord TLDR; Among the proliferation of recent use cases using the AI application ChatGPT, we ask whether it can be used to make improvements in other AI systems. We test it on a practical problem in a modality of AI in which it was not trained, computer vision, and report the results.

ML Engineer 98

ML Engineer ML ChatGPT Computer Vision 98

Unite.AI

JUNE 29, 2023

Business leaders in today's tech and startup scene know the importance of mastering AI and machine learning. However, developing these AI technologies and using tools such as Google Maps API for business purposes can be time-consuming and expensive.

Machine Learning 262

Machine Learning ML AI AI 262

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. Edge Impulse is known for its innovative tools that have greatly lowered the barrier to building edge AI solutions for digital health and industrial productivity.

ML Engineer 98

ML Engineer ML Python Neural Network 98

Marktechpost

JANUARY 31, 2024

Data scientists and ML engineers often need help to build full-stack applications. These professionals typically have a firm grasp of data and AI algorithms. Still, they may need more skills or time to learn new languages or frameworks to create user-friendly web applications. This is where Taipy comes into play.

Data Scientist 135

Data Scientist Machine Learning Python ML Engineer 135

IBM Journey to AI blog

AUGUST 12, 2024

Instead, businesses tend to rely on advanced tools and strategies—namely artificial intelligence for IT operations (AIOps) and machine learning operations (MLOps)—to turn vast quantities of data into actionable insights that can improve IT decision-making and ultimately, the bottom line.

Big Data 266

Big Data DevOps Automation Machine Learning 266

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.

Machine Learning 139

Machine Learning DevOps Data Scientist Data Quality 139

ODSC - Open Data Science

SEPTEMBER 21, 2023

The majority of us who work in machine learning, analytics, and related disciplines do so for organizations with a variety of different structures and motives. The following is an extract from Andrew McMahon’s book , Machine Learning Engineering with Python, Second Edition.

Machine Learning 98

Machine Learning ML Engineer ML Data Science 98

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.

Automation 107

Automation DevOps Machine Learning ML 107

AWS Machine Learning Blog

SEPTEMBER 18, 2023

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. Ray AI Runtime (AIR) reduces friction of going from development to production.

Machine Learning 132

Machine Learning ML Python Auto-complete 132

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

Flipboard

JUNE 20, 2023

Machine learning (ML) engineers have traditionally focused on striking a balance between model training and deployment cost vs. performance. This is important because training ML models and then using the trained models to make predictions (inference) can be highly energy-intensive tasks. Kamran Khan is a Sr.

Machine Learning 123

Machine Learning BERT Deep Learning ML Engineer 123

Marktechpost

SEPTEMBER 25, 2024

The study outlines key concepts for understanding AI and sustainability, focusing on model-centric optimization tactics to reduce the environmental impact of ML. Inference, which accounts for 90% of ML costs, is a key area for energy optimization. compile balanced accuracy and energy efficiency.

Machine Learning 104

Machine Learning ML Software Engineer ML Engineer 104

AWS Machine Learning Blog

FEBRUARY 11, 2025

In this post, we explore how to deploy this model efficiently on Amazon SageMaker AI. Get started with SageMaker JumpStart SageMaker JumpStart is a machine learning (ML) hub that can help accelerate your ML journey. His area of focus is generative AI and AWS AI Accelerators.

ML 87

ML Machine Learning Python Computer Vision 87

Snorkel AI

APRIL 27, 2023

Here at Snorkel AI, we devote our time to building and maintaining our machine-learning development platform, Snorkel Flow. Snorkel Flow handles intense machine learning workloads, and we’ve built our infrastructure on a foundation of Kubernetes—which was not designed with machine learning in mind.

Machine Learning 97

Machine Learning ML AI AI 97

Snorkel AI

APRIL 27, 2023

Here at Snorkel AI, we devote our time to building and maintaining our machine-learning development platform, Snorkel Flow. Snorkel Flow handles intense machine learning workloads, and we’ve built our infrastructure on a foundation of Kubernetes—which was not designed with machine learning in mind.

Machine Learning 96

Machine Learning ML AI AI 96

AWS Machine Learning Blog

OCTOBER 1, 2024

Over the last 18 months, AWS has announced more than twice as many machine learning (ML) and generative artificial intelligence (AI) features into general availability than the other major cloud providers combined. These services play a pivotal role in addressing diverse customer needs across the generative AI journey.

Data Science 99

Data Science Machine Learning Data Scientist Generative AI 99

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.

Data Science 170

Data Science DevOps Machine Learning Data Scientist 170

Marktechpost

AUGUST 8, 2023

In the ever-evolving landscape of machine learning, feature management has emerged as a key pain point for ML Engineers at Airbnb. Chronon enables users to generate thousands of features to power ML models effortlessly by simplifying feature engineering.

Machine Learning 98

Machine Learning ML Engineer Data Ingestion ML 98

AWS Machine Learning Blog

JANUARY 28, 2025

By investing in robust evaluation practices, companies can maximize the benefits of LLMs while maintaining responsible AI implementation and minimizing potential drawbacks. To support robust generative AI application development, its essential to keep track of models, prompt templates, and datasets used throughout the process.

LLM 116

LLM Large Language Models ML Algorithm 116

AWS Machine Learning Blog

DECEMBER 9, 2024

With access to a wide range of generative AI foundation models (FM) and the ability to build and train their own machine learning (ML) models in Amazon SageMaker , users want a seamless and secure way to experiment with and select the models that deliver the most value for their business. An MLflow 2.16.2

ML 104

ML Data Scientist Machine Learning Software Engineer 104

AWS Machine Learning Blog

OCTOBER 19, 2023

Customers increasingly want to use deep learning approaches such as large language models (LLMs) to automate the extraction of data and insights. For many industries, data that is useful for machine learning (ML) may contain personally identifiable information (PII).

Machine Learning 121

Machine Learning Python ML Automation 121

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. She has worked in several product roles in Amazon for over 5 years.

ML 100

ML Python Data Scientist Natural Language Processing 100

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. You can deploy your ML model to SageMaker hosting services and get a SageMaker endpoint for real-time inference.

ML 116

ML Metadata Data Scientist DevOps 116

AWS Machine Learning Blog

FEBRUARY 21, 2025

With the current housing shortage and affordability concerns, Rocket simplifies the homeownership process through an intuitive and AI-driven experience. Data exploration and model development were conducted using well-known machine learning (ML) tools such as Jupyter or Apache Zeppelin notebooks.

Data Science 80

Data Science Data Scientist Data Ingestion DevOps 80

Marktechpost

OCTOBER 11, 2023

Studies now investigate if building AI research agents with similar capabilities is possible. To evaluate AI research agents with free-form decision-making capabilities, researchers from Stanford University propose MLAgentBench, the first benchmark of its kind. Join our AI Channel on Whatsapp. We are also on WhatsApp.

Machine Learning 104

Machine Learning AI Researcher AI Research Convolutional Neural Networks 104

Marktechpost

MAY 30, 2024

Google plays a crucial role in advancing AI by developing cutting-edge technologies and tools like TensorFlow, Vertex AI, and BERT. Its AI courses provide valuable knowledge and hands-on experience, helping learners build and optimize AI models, understand advanced AI concepts, and apply AI solutions to real-world problems.

Artificial Intelligence 131

Artificial Intelligence Artificial Intelligence BERT Computer Vision 131

AWS Machine Learning Blog

FEBRUARY 12, 2025

This post demonstrates how to use Medusa-1, the first version of the framework, to speed up an LLM by fine-tuning it on Amazon SageMaker AI and confirms the speed up with deployment and a simple load test. Fine-tune an LLM using SageMaker AI training job We use the Zephyr 7B model as our backbone LLM.

LLM 80

LLM ML Natural Language Processing Machine Learning 80