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.

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Machine Learning BERT Deep Learning ML Engineer 123

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. His research interests are 3D deep learning, and vision and language representation learning.

Machine Learning 103

Machine Learning ML Data Scientist Robotics 103

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.

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Machine Learning ML Engineer ML Data Science 98

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

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.

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Data Science DevOps Machine Learning Data Scientist 170

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. As businesses across industries increasingly embrace AI and ML to gain a competitive edge, the demand for MLOps Engineers has skyrocketed.

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Software Engineer DevOps Machine Learning ML 182

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.

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ML Python Data Scientist Natural Language Processing 83

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).

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Machine Learning Python ML Automation 107

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

Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Understanding Machine Learning algorithms and effective data handling are also critical for success in the field. billion by 2031, growing at a CAGR of 34.20%.

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Machine Learning Neural Network ML Engineer Algorithm 52

ODSC - Open Data Science

APRIL 1, 2025

In a compelling talk at ODSC West 2024 , Yan Liu, PhD , a leading machine learning expert and professor at the University of Southern California (USC), shared her vision for how GPT-inspired architectures could revolutionize how we model, understand, and act on complex time series data acrossdomains.

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BERT ML Engineer Data Science Deep Learning 52

AWS Machine Learning Blog

APRIL 25, 2023

In this release, you can run your local machine learning (ML) Python code as a single-node Amazon SageMaker training job or multiple parallel jobs. This allows ML engineers and admins to configure these environment variables so data scientists can focus on ML model building and iterate faster.

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Machine Learning Data Scientist Python ML 92

The MLOps Blog

JANUARY 26, 2024

Training and evaluating models is just the first step toward machine-learning success. For this, we have to build an entire machine-learning system around our models that manages their lifecycle, feeds properly prepared data into them, and sends their output to downstream systems. But what is an ML pipeline?

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Machine Learning Metadata ML Python 72

Towards AI

JANUARY 6, 2025

AI engineering professional certificate by IBM AI engineering professional certificate from IBM targets fundamentals of machine learning, deep learning, programming, computer vision, NLP, etc. Prior experience in Python, ML basics, data training, and deep learning will come in handy for a smooth ride ahead.

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Artificial Intelligence Artificial Intelligence Computer Vision Deep Learning 105

Heartbeat

SEPTEMBER 6, 2023

Experimenting with Comet, a machine learning platform Photo by Donny Jiang on Unsplash Creating a machine learning model is easy, but that’s not what machine learning is all about. There are machine learning platforms that can perform all these tasks, and Comet is one such platform.

Machine Learning 52

Machine Learning Deep Learning Auto-complete Data Scientist 52

ODSC - Open Data Science

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. 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.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52

Pickl AI

JANUARY 23, 2023

Introduction to Machine Learning Frameworks In the present world, almost every organization is making use of machine learning and artificial intelligence in order to stay ahead of the competition. So, let us see the most popular and best machine learning frameworks and their uses.

Machine Learning 52

Machine Learning ML Neural Network Auto-classification 52

Towards AI

AUGUST 25, 2023

DL Artificial intelligence (AI) is the study of ways to build intelligent programs and machines that can creatively solve problems, which has always been considered a human prerogative. In ML, there are a variety of algorithms that can help solve problems. Any competent software engineer can implement any algorithm. 12, 2014. [3]

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Machine Learning Software Engineer Neural Network Data Science 98

The MLOps Blog

JUNE 27, 2023

How to evaluate MLOps tools and platforms Like every software solution, evaluating MLOps (Machine Learning Operations) tools and platforms can be a complex task as it requires consideration of varying factors. Pay-as-you-go pricing makes it easy to scale when needed.

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Machine Learning Metadata Data Scientist Data Quality 134

Unite.AI

FEBRUARY 28, 2024

The sheer scale of these models, combined with advanced deep learning techniques, enables them to achieve state-of-the-art performance on language tasks. Adversarial attacks Adversarial attacks involve specially crafted inputs designed to deceive machine learning models and trigger unintended behaviors.

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Large Language Models Machine Learning LLM Neural Network 162

AWS Machine Learning Blog

FEBRUARY 12, 2025

About the authors Daniel Zagyva is a Senior ML Engineer at AWS Professional Services. He specializes in developing scalable, production-grade machine learning solutions for AWS customers. His experience extends across different areas, including natural language processing, generative AI and machine learning operations.

LLM 76

LLM ML Natural Language Processing Machine Learning 76

ODSC - Open Data Science

MARCH 27, 2023

The Next Generation of Low-Code Machine Learning Devvret Rishi | Co-founder and Chief Product Officer | Predibase In this session, you’ll explore declarative machine learning, a configuration-based modeling interface, which provides more flexibility and simplicity when implementing cutting-edge machine learning.

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Machine Learning Data Science Deep Learning Data Ingestion 98

AWS Machine Learning Blog

OCTOBER 23, 2023

This post is co-authored by Anatoly Khomenko, Machine Learning Engineer, and Abdenour Bezzouh, Chief Technology Officer at Talent.com. The system is developed by a team of dedicated applied machine learning (ML) scientists, ML engineers, and subject matter experts in collaboration between AWS and Talent.com.

NLP 109

NLP Deep Learning Machine Learning Natural Language Processing 109

Dlabs.ai

OCTOBER 23, 2019

There’s a misconception in the world of machine learning (ML). Developers have been led to believe that, to build and train an ML model, they are restricted to using a select few programming languages. But times are changing — as are the dynamics of ML engineering. in machine learning?

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Machine Learning Convolutional Neural Networks Python ML 52

AWS Machine Learning Blog

NOVEMBER 21, 2023

By taking care of the undifferentiated heavy lifting, SageMaker allows you to focus on working on your machine learning (ML) models, and not worry about things such as infrastructure. These two crucial parameters influence the efficiency, speed, and accuracy of training deep learning models.

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ML Deep Learning Machine Learning DevOps 101

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. Daniel Zagyva is a Data Scientist at AWS Professional Services.

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ML Explainability Machine Learning Deep Learning 129

ODSC - Open Data Science

FEBRUARY 2, 2023

Machine Learning As machine learning is one of the most notable disciplines under data science, most employers are looking to build a team to work on ML fundamentals like algorithms, automation, and so on. This will lead to algorithm development for any machine or deep learning processes.

Data Science 98

Data Science Data Scientist Big Data Machine Learning 98

AWS Machine Learning Blog

DECEMBER 14, 2023

This post is co-authored by Anatoly Khomenko, Machine Learning Engineer, and Abdenour Bezzouh, Chief Technology Officer at Talent.com. In line with this mission, Talent.com collaborated with AWS to develop a cutting-edge job recommendation engine driven by deep learning, aimed at assisting users in advancing their careers.

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ETL Data Scientist Machine Learning Deep Learning 100

Heartbeat

FEBRUARY 1, 2023

Machine learning has become an essential part of our lives because we interact with various applications of ML models, whether consciously or unconsciously. Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models. What is MLOps?

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DevOps Machine Learning Data Scientist Software Engineer 96

AWS Machine Learning Blog

APRIL 29, 2024

For AWS and Outerbounds customers, the goal is to build a differentiated machine learning and artificial intelligence (ML/AI) system and reliably improve it over time. At Netflix, he led the ML infrastructure team that created Metaflow, a popular open-source, human-centric foundation for ML/AI systems.

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ML Python Data Scientist Deep Learning 119

Heartbeat

OCTOBER 30, 2023

Ensuring Long-Term Performance and Adaptability of Deployed Models Source: [link] Introduction When working on any machine learning problem, data scientists and machine learning engineers usually spend a lot of time on data gathering , efficient data preprocessing , and modeling to build the best model for the use case.

Machine Learning 52

Machine Learning Data Drift ML Data Scientist 52

ODSC - Open Data Science

FEBRUARY 22, 2024

Today we are excited to bring you just a few of the machine learning sessions you’ll be able to participate in if you attend. In this session, you’ll take a deep dive into the three distinct types of Feature Stores and their uses in the machine learning ecosystem. Check them out below. Who Wants to Live Forever?

Machine Learning 52

Machine Learning Data Science Python ML 52

AWS Machine Learning Blog

SEPTEMBER 11, 2024

Machine learning operations (MLOps) are a set of practices that automate and simplify machine learning (ML) workflows and deployments. Further optimization is possible using SageMaker Training Compiler to compile deep learning models for training on supported GPU instances.

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Auto-complete Deep Learning ML Machine Learning 95

Viso.ai

MARCH 29, 2024

MLflow is an open-source platform designed to manage the entire machine learning lifecycle, making it easier for ML Engineers, Data Scientists, Software Developers, and everyone involved in the process. Many machine learning projects fail to deliver practical results due to difficulties in automation and deployment.

Machine Learning 52

Machine Learning ML Automation Data Scientist 52

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. That is where Provectus , an AWS Premier Consulting Partner with competencies in Machine Learning, Data & Analytics, and DevOps, stepped in.

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DevOps ML Machine Learning ML Engineer 112

AWS Machine Learning Blog

NOVEMBER 20, 2023

Building a distributed training environment with SageMaker SageMaker Training is a managed machine learning (ML) training environment on AWS that provides a suite of features and tools to simplify the training experience and can be useful in distributed computing, as illustrated in the following diagram. 24xlarge instances.

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Deep Learning Machine Learning ML Data Scientist 111

The MLOps Blog

MAY 10, 2023

When working on real-world machine learning (ML) use cases, finding the best algorithm/model is not the end of your responsibilities. In this article, you will learn about different methods of saving, storing, and packaging a trained machine-learning model, along with the pros and cons of each method.

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