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.

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Machine Learning ML Software Engineer ML Engineer 105

TheSequence

OCTOBER 13, 2023

Event Details - Date: Tuesday, November 14 - Time: 9:30AM – 3:00PM PT - Location: Virtual REGISTER NOW

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ML Engineer Software Engineer Software Development Data Scientist 52

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.

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Machine Learning ML Engineer DevOps ML 137

IBM Journey to AI blog

AUGUST 12, 2024

It helps companies streamline and automate the end-to-end ML lifecycle, which includes data collection, model creation (built on data sources from the software development lifecycle), model deployment, model orchestration, health monitoring and data governance processes.

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Big Data DevOps Automation Machine Learning 278

Towards AI

AUGUST 24, 2023

Common mistakes and misconceptions about learning AI/ML Markus Spiske on Unsplash A common misconception of beginners is that they can learn AI/ML from a few tutorials that implement the latest algorithms, so I thought I would share some notes and advice on learning AI. Thus, a student of AI also needs a healthy dose of skepticism [8].

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Neural Network Software Engineer Algorithm AI Engineer 86

Towards AI

AUGUST 25, 2023

What is AI Engineering AI Engineering is a new discipline focused on developing tools, systems, and processes to enable the application of artificial intelligence in real-world contexts [1]. In a nutshell, AI Engineering is the application of software engineering best practices to the field of AI. Bourque and R.

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Software Engineer Machine Learning Neural Network Artificial Intelligence 92

Chatbots Life

MAY 12, 2023

We will discuss how models such as ChatGPT will affect the work of software engineers and ML engineers. Will ChatGPT replace software engineers? Will ChatGPT replace ML Engineers? This makes the tool extremely useful as a software engineer's assistant.

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ChatGPT Software Engineer NLP Prompt Engineer 105

IBM Journey to AI blog

AUGUST 11, 2023

MLOps fosters greater collaboration between data scientists, software engineers and IT staff. Origins of the MLOps process MLOps was born out of the realization that ML lifecycle management was slow and difficult to scale for business application. How to use ML to automate the refining process into a cyclical ML process.

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Data Science DevOps Machine Learning Data Scientist 188

AWS Machine Learning Blog

MARCH 22, 2024

Before joining AWS, he worked for AWS customers and partners in software engineering, consulting, and architecture roles for 8+ years. In his free time he is pursuing a PhD in ML Engineering at University of Regensburg, focussing on applied NLP in the science domain.

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Large Language Models Natural Language Processing Software Engineer NLP 132

AWS Machine Learning Blog

JANUARY 17, 2023

This development approach can be used in combination with other common software engineering best practices such as automated code deployments, tests, and CI/CD pipelines. Cory Hairston is a Software Engineer at the Amazon ML Solutions Lab. Marcelo Aberle is an ML Engineer in the AWS AI organization.

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Software Engineer ML Engineer ML Machine Learning 110

O'Reilly Media

OCTOBER 19, 2021

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. Software Architecture. This approach is not novel.

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DevOps Software Engineer Data Scientist ML 140

AWS Machine Learning Blog

SEPTEMBER 25, 2024

Conclusion In this post, we explored how SageMaker JumpStart empowers data scientists and ML engineers to discover, access, and deploy a wide range of pre-trained FMs for inference, including Meta’s most advanced and capable models to date. Search for the embedding and text generation endpoints. Choose Delete again to confirm.

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Software Engineer Software Development ML Python 136

TensorFlow

MAY 11, 2023

Posted by Thad Starner (Professor, Georgia Tech and Staff Research Scientist, Google), Sam Sepah (ML Research Program Manager), Manfred Georg (Software Engineer, Google), Mark Sherwood (Senior Product Manager, Google), Glenn Cameron (Product Marketing Manager, Google) Over 70 million deaf people around the world use sign language to communicate.

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Software Engineer Large Language Models ML Engineer ML 98

ODSC - Open Data Science

FEBRUARY 2, 2023

They’re looking for people who know all related skills, and have studied computer science and software engineering. As MLOps become more relevant to ML demand for strong software architecture skills will increase as well. While knowing Python, R, and SQL are expected, you’ll need to go beyond that.

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Data Science Data Scientist Big Data Machine Learning 98

AWS Machine Learning Blog

NOVEMBER 13, 2024

With the unification of SageMaker Model Cards and SageMaker Model Registry, architects, data scientists, ML engineers, or platform engineers (depending on the organization’s hierarchy) can now seamlessly register ML model versions early in the development lifecycle, including essential business details and technical metadata.

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Metadata ML Software Engineer Machine Learning 124

TheSequence

JANUARY 19, 2024

You probably don’t need ML engineers In the last two years, the technical sophistication needed to build with AI has dropped dramatically. ML engineers used to be crucial to AI projects because you needed to train custom models from scratch. Instead, Twain employs linguists and salespeople as prompt engineers.

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Generative AI Prompt Engineering Prompt Engineer Large Language Models 59

The MLOps Blog

JANUARY 23, 2023

Most of our customers are doing ML/MLOps at a reasonable scale, NOT at the hyperscale of big-tech FAANG companies. Not a fork: – The MLOps team should consist of a DevOps engineer, a backend software engineer, a data scientist, + regular software folks. How about the ML engineer? Let me explain.

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DevOps Metadata Software Engineer Data Scientist 59

AWS Machine Learning Blog

AUGUST 20, 2024

About the Authors Sundar Raghavan is an AI/ML Specialist Solutions Architect at AWS, helping customers build scalable and cost-efficient AI/ML pipelines with Human in the Loop services. Michael Borde is a lead software engineer at Amazon AI, where he has been for seven years.

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Software Engineer ML Machine Learning Metadata 124

ODSC - Open Data Science

FEBRUARY 20, 2023

We’ll also have a series of introductory sessions on AI literacy, intros to programming, etc.

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Data Science Machine Learning ML Engineer Python 96

AssemblyAI

DECEMBER 15, 2022

Shubham Saboo (Head of DevRel at Tenstorrent Inc. Final Words With our first-ever hackathon, we hoped to foster innovation and creativity for AI-first products.

Software Engineer 130

Software Engineer ML Engineer Data Scientist AI Chatbots 130

Pickl AI

JULY 14, 2023

The average salary of a ML Engineer per annum is $125,087. Data Engineer: Data Engineers ae responsible for building systems that collect, manage and convert raw data into usable information for Data Scientists, business analysts and others. The average salary for a Robotics Engineer stands at $101,062.

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Neural Network Software Engineer Artificial Intelligence Artificial Intelligence 52

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? They might also help with data preparation and cleaning.

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DevOps Machine Learning Data Scientist Software Engineer 96

AWS Machine Learning Blog

FEBRUARY 13, 2024

Let’s demystify this using the following personas and a real-world analogy: Data and ML engineers (owners and producers) – They lay the groundwork by feeding data into the feature store Data scientists (consumers) – They extract and utilize this data to craft their models Data engineers serve as architects sketching the initial blueprint.

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ML Machine Learning ML Engineer Data Scientist 135

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. Scaling AI/ML Workloads with Ray Kai Fricke | Senior Software Engineer | Anyscale Inc.

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Machine Learning Data Ingestion Deep Learning Explainability 98

Towards AI

JUNE 18, 2024

The principles of CNNs and early vision transformers are still important as a good background for ML engineers, even though they are much less popular nowadays. Yes, LlamaIndex and LangChain will transform or even disappear, just as Tensorflow is no longer maintained. We believe it’s the same for the tech stack covered in the book.

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Large Language Models Prompt Engineer Prompt Engineering LLM 57

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 Convolutional Neural Networks Data Scientist Machine Learning 98

The MLOps Blog

JUNE 13, 2024

Data scientists often lack focus, time, or knowledge about software engineering principles. As a result, poor code quality and reliance on manual workflows are two of the main issues in ML development processes. I started as a full-stack developer but have gradually moved toward data and ML engineering.

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ML Data Scientist Automation Software Development 59

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.

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Data Scientist ML Data Science Automation 134

ODSC - Open Data Science

FEBRUARY 22, 2024

By the end of this session, you’ll have a practical blueprint to efficiently harness feature stores within ML workflows. Using Graphs for Large Feature Engineering Pipelines Wes Madrigal | ML Engineer | Mad Consulting Feature engineering from raw entity-level data is complex, but there are ways to reduce that complexity.

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Machine Learning Data Science Python Data Ingestion 52

ODSC - Open Data Science

FEBRUARY 24, 2023

Apache Superset GitHub | Website Apache Superset is a must-try project for any ML engineer, data scientist, or data analyst. Its goal is to help with a quick analysis of target characteristics, training vs testing data, and other such data characterization tasks.

Data Analysis 126

Data Analysis Data Science Business Intelligence Python 126

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

AWS Machine Learning Blog

SEPTEMBER 29, 2023

This guest post is co-written by Lydia Lihui Zhang, Business Development Specialist, and Mansi Shah, Software Engineer/Data Scientist, at Planet Labs. Planet and AWS’s partnership on geospatial ML SageMaker geospatial capabilities empower data scientists and ML engineers to build, train, and deploy models using geospatial data.

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Machine Learning Data Scientist ML Python 139

AWS Machine Learning Blog

OCTOBER 22, 2024

Data scientists and machine learning (ML) engineers use pipelines for tasks such as continuous fine-tuning of large language models (LLMs) and scheduled notebook job workflows. About the Authors Lauren Mullennex is a Senior AI/ML Specialist Solutions Architect at AWS. Brock Wade is a Software Engineer for Amazon SageMaker.

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Automation LLM DevOps Python 95

AWS Machine Learning Blog

NOVEMBER 30, 2023

In this example, Code Editor can be used by an ML engineering team who needs advanced IDE features to debug their code and deploy the endpoint. We will deploy a Mistral-7B large language model (LLM) model into an Amazon SageMaker real-time endpoint using a built-in container from HuggingFace.

Machine Learning 134

Machine Learning ML LLM Software Engineer 134

AWS Machine Learning Blog

NOVEMBER 14, 2024

Madhubalasri is dedicated to driving innovation in ML governance and optimizing model management processes Saumitra Vikaram is a Senior Software Engineer at AWS. He is focused on AI/ML technology, ML model management, ML governance, and MLOps to improve overall organizational efficiency and productivity.

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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. With over 15 years of experience, he supports customers globally in leveraging AI and ML for innovative solutions and building ML platforms on AWS.

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Machine Learning ML Data Scientist Robotics 129

AWS Machine Learning Blog

SEPTEMBER 25, 2023

Kemeng Zhang is an ML Engineer based in Zurich. She helps global customers design, develop, and scale ML-based applications to empower their digital capabilities to increase business revenue and reduce cost. He enjoys every aspect of software engineering, from design to implementation, and from deployment to maintenance.

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ML Machine Learning Software Engineer ML Engineer 133