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

IBM Journey to AI blog

AUGUST 11, 2023

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning. How MLOps will be used within the organization.

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Data Science DevOps Machine Learning Data Scientist 170

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 93

AWS Machine Learning Blog

APRIL 17, 2025

To learn more, see Revolutionizing AI: How Amazon SageMaker Enhances Einsteins Large Language Model Latency and Throughput. Accelerating development with SageMaker Deep Learning Containers SageMaker AI Deep Learning Containers (DLCs) play a crucial role in accelerating model development and deployment.

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Large Language Models Deep Learning Machine Learning AI 55

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?

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Machine Learning Computer Vision Deep Learning Python 153

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

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

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

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.

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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.

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Machine Learning ML Data Scientist Robotics 115

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

AWS Machine Learning Blog

APRIL 7, 2025

yml file from the AWS Deep Learning Containers GitHub repository, illustrating how the model synthesizes information across an entire repository. Codebase analysis with Llama 4 Using Llama 4 Scouts industry-leading context window, this section showcases its ability to deeply analyze expansive codebases. Choose Delete again to confirm.

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Machine Learning Large Language Models Python Automation 116

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

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

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Deep Learning Explainability ML Engineer Data Scientist 111

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 153

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 114

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%

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NLP Deep Learning Machine Learning Natural Language Processing 121

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

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 113

AWS Machine Learning Blog

APRIL 29, 2024

Metaflow overview Metaflow was originally developed at Netflix to enable data scientists and ML engineers to build ML/AI systems quickly and deploy them on production-grade infrastructure. Scott Perry is a Solutions Architect on the Annapurna ML accelerator team at AWS.

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ML Python Data Scientist Deep Learning 127

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.

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Large Language Models Prompt Engineering Prompt Engineer Chatbots 113

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 108

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.

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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.

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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.

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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?

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Machine Learning Data Science Data Ingestion Deep Learning 98

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 122

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 96

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

BAIR

MARCH 11, 2024

Their work at BAIR, ranging from deep learning, robotics, and natural language processing to computer vision, security, and much more, has contributed significantly to their fields and has had transformative impacts on society. learning scenarios) for autonomous agents to improve generalization and sample efficiency.

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Robotics Machine Learning Natural Language Processing Deep Learning 100

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.

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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.

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Machine Learning Neural Network ML Engineer Algorithm 52

AWS Machine Learning Blog

APRIL 19, 2024

About the Authors Rajesh Ramchander is a Principal ML Engineer in Professional Services at AWS. He helps customers at various stages in their AI/ML and GenAI journey, from those that are just getting started all the way to those that are leading their business with an AI-first strategy.

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Metadata LLM Software Development Machine Learning 128

AWS Machine Learning Blog

FEBRUARY 12, 2024

The preceding images show an example of how a model explainability would look like for an arbitrary ML model. Training optimization The rise of deep learning (DL) has led to ML becoming increasingly reliant on computational power and vast amounts of data.

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ML Explainability Machine Learning Deep Learning 136

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.

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DevOps ML Machine Learning ML Engineer 119

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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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.

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Machine Learning Metadata Data Scientist Data Quality 134