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.

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ML Python Data Scientist Natural Language Processing 99

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 149

ML DevOps Data Scientist ML Engineer 149

Flipboard

SEPTEMBER 4, 2023

This lesson is the 1st of a 3-part series on Docker for Machine Learning : Getting Started with Docker for Machine Learning (this tutorial) Lesson 2 Lesson 3 Overview: Why the Need? Envision yourself as an ML Engineer at one of the world’s largest companies. How Do Containers Differ from Virtual Machines?

Machine Learning 148

Machine Learning Computer Vision Deep Learning Python 148

Unite.AI

JUNE 19, 2024

Master's Degree : Pursuing a Master's degree in Computer Science, Data Science, or a related field can further enhance your knowledge and skills, particularly in areas like ML, AI, and advanced software engineering concepts. Courses : Coursera – Machine Learning by Andrew Ng : A foundational course in machine learning.

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Software Engineer DevOps Machine Learning ML 182

Marktechpost

OCTOBER 10, 2024

Machine Learning Engineer : Specializes in building, optimizing, and deploying ML models. They focus on training deep learning models, reducing model latency, implementing model versioning, and deploying models in production environments. ML engineers work on scaling these models for real-world applications.

Data Science 108

Data Science Machine Learning Data Scientist ML Engineer 108

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 117

Machine Learning ML Data Scientist Robotics 117

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

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.

Artificial Intelligence 105

Artificial Intelligence Artificial Intelligence Computer Vision Deep Learning 105

Towards AI

JANUARY 27, 2024

Image created with Microsoft Bing Image Maker AutoKeras AutoKeras is Python’s Keras-based AutoML library for developing Deep Learning models. 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

Deep Learning 111

Deep Learning Explainability ML Engineer Data Scientist 111

AWS Machine Learning Blog

NOVEMBER 21, 2023

These two crucial parameters influence the efficiency, speed, and accuracy of training deep learning models. The following figure illustrates an SDK for high-performance deep learning inference. As part of his PhD, he worked on physics-based deep learning for numerical simulations at scale.

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ML Deep Learning Machine Learning DevOps 116

Flipboard

OCTOBER 9, 2023

This lesson is the 2nd of a 3-part series on Docker for Machine Learning : Getting Started with Docker for Machine Learning Getting Used to Docker for Machine Learning (this tutorial) Lesson 3 To learn how to create a Docker Container for Machine Learning, just keep reading. That’s not the case.

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Machine Learning Computer Vision Deep Learning Python 148

AWS Machine Learning Blog

DECEMBER 14, 2023

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. At the same time, the same solution can be deployed to production by an ML Engineer with little modifications needed.

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ETL Data Scientist Machine Learning Deep Learning 115

AWS Machine Learning Blog

OCTOBER 23, 2023

Given this mission, Talent.com and AWS joined forces to create a job recommendation engine using state-of-the-art natural language processing (NLP) and deep learning model training techniques with Amazon SageMaker to provide an unrivaled experience for job seekers. The recommendation system has driven an 8.6%

NLP 120

NLP Deep Learning Machine Learning Natural Language Processing 120

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. Foster closer collaboration between security teams and ML engineers to instill security best practices.

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Large Language Models Machine Learning LLM Neural Network 162

AWS Machine Learning Blog

SEPTEMBER 11, 2024

Further optimization is possible using SageMaker Training Compiler to compile deep learning models for training on supported GPU instances. SageMaker Training Compiler converts deep learning models from high-level language representation to hardware-optimized instructions.

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Auto-complete Deep Learning ML Machine Learning 112

Marktechpost

JULY 25, 2024

Their rise is driven by advancements in deep learning, data availability, and computing power. Learning about LLMs is essential to harness their potential for solving complex language tasks and staying ahead in the evolving AI landscape.

Large Language Models 113

Large Language Models Prompt Engineer Prompt Engineering Chatbots 113

Towards AI

AUGUST 25, 2023

Machine learning (ML) is a subset of AI that provides computer systems the ability to automatically learn and improve from experience without being explicitly programmed. In ML, there are a variety of algorithms that can help solve problems. Any competent software engineer can implement any algorithm. 30, 2021.

Machine Learning 98

Machine Learning Software Engineer Neural Network Data Science 98

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. After all, this is what machine learning really is; a series of algorithms rooted in mathematics that can iterate some internal parameters based on data.

Machine Learning 98

Machine Learning ML Engineer ML Data Science 98

ODSC - Open Data Science

FEBRUARY 2, 2023

This will lead to algorithm development for any machine or deep learning processes. Scikit-learn also earns a top spot thanks to its success with predictive analytics and general machine learning. Big Data As datasets become larger and more complex, knowing how to work with them will be key.

Data Science 98

Data Science Data Scientist Big Data Machine Learning 98

ODSC - Open Data Science

MARCH 27, 2023

ML Governance: A Lean Approach Ryan Dawson | Principal Data Engineer | Thoughtworks Meissane Chami | Senior ML Engineer | Thoughtworks During this session, you’ll discuss the day-to-day realities of ML Governance. Some of the questions you’ll explore include How much documentation is appropriate?

Machine Learning 98

Machine Learning Data Ingestion Data Science Deep Learning 98

ODSC - Open Data Science

FEBRUARY 25, 2025

Topics Include: Agentic AI DesignPatterns LLMs & RAG forAgents Agent Architectures &Chaining Evaluating AI Agent Performance Building with LangChain and LlamaIndex Real-World Applications of Autonomous Agents Who Should Attend: Data Scientists, Developers, AI Architects, and ML Engineers seeking to build cutting-edge autonomous systems.

Data Scientist 52

Data Scientist Large Language Models Machine Learning ML Engineer 52

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, ml.p4de.24xlarge,

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Deep Learning Machine Learning ML Data Scientist 121

AWS Machine Learning Blog

MARCH 9, 2023

As machine learning (ML) models have improved, data scientists, ML engineers and researchers have shifted more of their attention to defining and bettering data quality. This has led to the emergence of a data-centric approach to ML and various techniques to improve model performance by focusing on data requirements.

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ML Engineer Data Scientist Convolutional Neural Networks ML 98

AWS Machine Learning Blog

SEPTEMBER 20, 2023

This approach is beneficial if you use AWS services for ML for its most comprehensive set of features, yet you need to run your model in another cloud provider in one of the situations we’ve discussed. Key concepts Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning.

ML 130

ML Python Deep Learning Machine Learning 130

Pickl AI

NOVEMBER 28, 2024

Continuous learning is essential to keep pace with advancements in Machine Learning technologies. Fundamental Programming Skills Strong programming skills are essential for success in ML. Python’s readability and extensive community support and resources make it an ideal choice for ML engineers.

Machine Learning 52

Machine Learning Neural Network ML Engineer Algorithm 52

AWS Machine Learning Blog

APRIL 27, 2023

Deep learning (DL) is a fast-evolving field, and practitioners are constantly innovating DL models and inventing ways to speed them up. Custom operators are one of the mechanisms developers use to push the boundaries of DL innovation by extending the functionality of existing machine learning (ML) frameworks such as PyTorch.

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Auto-complete Deep Learning Machine Learning ML 97

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 124

Data Scientist ML Data Science Automation 124

The MLOps Blog

JUNE 27, 2023

Collaborative workflows : Dataset storage and versioning tools should support collaborative workflows, allowing multiple users to access and contribute to datasets simultaneously, ensuring efficient collaboration among ML engineers, data scientists, and other stakeholders. Monitor the performance of machine learning models.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

AWS Machine Learning Blog

JUNE 27, 2023

Earth.com’s leadership team recognized the vast potential of EarthSnap and set out to create an application that utilizes the latest deep learning (DL) architectures for computer vision (CV). We initiated a series of enhancements to deliver managed MLOps platform and augment ML engineering.

DevOps 115

DevOps ML Machine Learning ML Engineer 115

Towards AI

AUGUST 24, 2023

Since this is an AI website, I will assume that most readers will have the following goals: You are interested in becoming an AI/ML engineer. You are interested in learning software engineering best practices [1][2]. Thus, deep learning models should be your last choice. Know when not to use AI.

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Neural Network Software Engineer Algorithm Machine Learning 98

Heartbeat

FEBRUARY 1, 2023

Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models. In this article, we’ll learn everything there is to know about these operations and how ML engineers go about performing them. What is MLOps? We pay our contributors, and we don’t sell ads.

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DevOps Machine Learning Data Scientist Software Engineer 96

The MLOps Blog

MAY 10, 2023

In this section, you will see different ways of saving machine learning (ML) as well as deep learning (DL) models. Note: The focus of this article is not to show you how you can create the best ML model but to explain how effectively you can save trained models. Now let’s see how we can save our model.

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Python Metadata ML Machine Learning 105

AWS Machine Learning Blog

OCTOBER 4, 2024

About the Authors Akarsha Sehwag is a Data Scientist and ML Engineer in AWS Professional Services with over 5 years of experience building ML based solutions. Leveraging her expertise in Computer Vision and Deep Learning, she empowers customers to harness the power of the ML in AWS cloud efficiently.

Data Scientist 101

Data Scientist Generative AI Machine Learning ML 101

Heartbeat

SEPTEMBER 6, 2023

There are machine learning platforms that can perform all these tasks, and Comet is one such platform. Comet Comet is a machine learning platform built to help data scientists and ML engineers track, compare, and optimize machine learning experiments. We will build our deep learning model using those parameters.

Machine Learning 52

Machine Learning Deep Learning Auto-complete Data Scientist 52

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 118

Machine Learning Python ML Automation 118

AWS Machine Learning Blog

SEPTEMBER 18, 2023

SageMaker geospatial capabilities make it easy for data scientists and machine learning (ML) engineers to build, train, and deploy models using geospatial data. Thomas Attree is a Senior Solutions Architect at Amazon Web Services based in London.

Data Scientist 125

Data Scientist ML Machine Learning ML Engineer 125

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.

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LLM ML Natural Language Processing Machine Learning 77