TheSequence

MAY 26, 2023

Want to connect with the ML engineering community and learn best practices from ML practitioners on how to build risk and fraud detection systems? Francisco Arceo, Engineering Manager at Affirm , will discuss the common pitfalls and share simple approaches to avoiding them. Need more reasons to sign up? REGISTER NOW

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ML ML Engineer Software Engineer Data Scientist 52

IBM Journey to AI blog

AUGUST 12, 2024

AIOPs refers to the application of artificial intelligence (AI) and machine learning (ML) techniques to enhance and automate various aspects of IT operations (ITOps). ML technologies help computers achieve artificial intelligence. However, they differ fundamentally in their purpose and level of specialization in AI and ML environments.

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Big Data DevOps Automation Machine Learning 278

AWS Machine Learning Blog

APRIL 26, 2023

Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? Most executives think ML can apply to any business decision, but on average only half of the ML projects make it to production. Challenges Customers may face several challenges when implementing machine learning (ML) solutions.

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ML Machine Learning Data Science Data Drift 125

Towards AI

FEBRUARY 15, 2024

AI Engineers: Your Definitive Career Roadmap Become a professional certified AI engineer by enrolling in the best AI ML Engineer certifications that help you earn skills to get the highest-paying job. AI engineers usually work in an office environment as part of a team.

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AI Engineer Software Engineer Artificial Intelligence Artificial Intelligence 89

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

TheSequence

OCTOBER 13, 2023

Join the global machine learning community at this virtual event to network and share best practices with your peers on platforms and architectures for production ML. Event Details - Date: Tuesday, November 14 - Time: 9:30AM – 3:00PM PT - Location: Virtual REGISTER NOW

ML Engineer 52

ML Engineer Software Engineer Software Development Data Scientist 52

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. What is MLOps?

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Data Science DevOps Machine Learning Data Scientist 188

AWS Machine Learning Blog

NOVEMBER 13, 2024

You can now register machine learning (ML) models in Amazon SageMaker Model Registry with Amazon SageMaker Model Cards , making it straightforward to manage governance information for specific model versions directly in SageMaker Model Registry in just a few clicks.

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Metadata ML Software Engineer Machine Learning 122

The MLOps Blog

AUGUST 4, 2023

Situations described above arise way too often in ML teams, and their consequences vary from a single developer’s annoyance to the team’s inability to ship their code as needed. Let’s dive into the world of monorepos, an architecture widely adopted in major tech companies like Google, and how they can enhance your ML workflows.

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ML Machine Learning Python Software Engineer 52

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. Trying to code ML algorithms from scratch.

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Neural Network Software Engineer Algorithm AI Engineer 86

Towards AI

AUGUST 25, 2023

As a reminder, I highly recommend that you refer to more than one resource (other than documentation) when learning ML, preferably a textbook geared toward your learning level (beginner/intermediate / advanced). In a nutshell, AI Engineering is the application of software engineering best practices to the field of AI.

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Software Engineer Machine Learning Neural Network Artificial Intelligence 92

The MLOps Blog

AUGUST 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. Stefan: Yeah.

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ML Data Scientist Software Engineer Machine Learning 52

The MLOps Blog

OCTOBER 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. It was two or three times more.

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ML Data Scientist Machine Learning Data Science 52

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. All ML projects are software projects.

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DevOps Software Engineer Data Scientist ML 140

AWS Machine Learning Blog

SEPTEMBER 25, 2024

SageMaker JumpStart is a machine learning (ML) hub that provides access to algorithms, models, and ML solutions so you can quickly get started with ML. SageMaker Studio is a comprehensive IDE that offers a unified, web-based interface for performing all aspects of the ML development lifecycle. Deploy Llama 3.2

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Software Engineer Software Development ML Python 136

AWS Machine Learning Blog

JANUARY 17, 2023

Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) partly based on JupyterLab 3. Studio provides a web-based interface to interactively perform ML development tasks required to prepare data and build, train, and deploy ML models.

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Software Engineer ML Engineer ML Machine Learning 109

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

AWS Machine Learning Blog

FEBRUARY 13, 2024

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts.

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ML Machine Learning ML Engineer Data Scientist 135

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

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. SageMaker Processing provisions cluster resources for you to run city-, country-, or continent-scale geospatial ML workloads.

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Machine Learning ML Data Scientist Robotics 126

AWS Machine Learning Blog

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts.

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ML Auto-complete Machine Learning Auto-classification 112

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. They’re looking for people who know all related skills, and have studied computer science and software engineering.

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Data Science Data Scientist Big Data Machine Learning 98

The MLOps Blog

JANUARY 23, 2023

Came to ML from software. Founded two successful software services companies. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”. Most of our customers are doing ML/MLOps at a reasonable scale, NOT at the hyperscale of big-tech FAANG companies.

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DevOps Metadata Software Engineer Data Scientist 59

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 130

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 123

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

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 131

ODSC - Open Data Science

MARCH 27, 2023

Learn about the flow, difficulties, and tools for performing ML clustering at scale Ori Nakar | Principal Engineer, Threat Research | Imperva Given that there are billions of daily botnet attacks from millions of different IPs, the most difficult challenge of botnet detection is choosing the most relevant data.

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Machine Learning Data Ingestion Deep Learning Explainability 98

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

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. In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image.

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Machine Learning Data Scientist ML Python 139

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

AWS Machine Learning Blog

NOVEMBER 30, 2023

Code Editor is based on Code-OSS , Visual Studio Code Open Source, and provides access to the familiar environment and tools of the popular IDE that machine learning (ML) developers know and love, fully integrated with the broader SageMaker Studio feature set. Choose Open CodeEditor to launch the IDE.

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Machine Learning ML LLM Software Engineer 134

Pickl AI

JULY 14, 2023

Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Deep Learning, a subfield of ML, gained attention with the development of deep neural networks. Artificial Intelligence and the Future of Humans 1.

Neural Network 52

Neural Network Software Engineer Artificial Intelligence Artificial Intelligence 52

ODSC - Open Data Science

FEBRUARY 22, 2024

Key points of this talk are: In this talk, we will focus on: The dangers of using post-hoc explainability methods as tools for decision making, and how traditional ML isn’t suited in situations where want to perform interventions on the system. technical architecture that simplifies the process of managing real-time ML models and features.

Machine Learning 52

Machine Learning Data Science Python Data Ingestion 52

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

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. Create a complete AI/ML pipeline for fine-tuning an LLM using drag-and-drop functionality. Brock Wade is a Software Engineer for Amazon SageMaker.

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Automation LLM DevOps Python 94

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.

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Machine Learning Data Drift Data Science Data Scientist 52