AWS Machine Learning Blog

AUGUST 20, 2024

Use Amazon SageMaker Ground Truth to label data : This guide explains how to use SageMaker Ground Truth for data labeling tasks, including setting up workteams and workforces. Michael Borde is a lead software engineer at Amazon AI, where he has been for seven years. Understanding how presigned URLs work will be beneficial.

Software Engineer 113

Software Engineer ML Machine Learning Metadata 113

Pickl AI

JULY 14, 2023

Explainable AI and Ethical Considerations (2010s-present): As AI systems became more complex and influential, concerns about transparency, fairness, and accountability arose. Researchers began addressing the need for Explainable AI (XAI) to make AI systems more understandable and interpretable.

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. Conclusion Can’t wait to start learning from these incredible speakers and experts?

Machine Learning 52

Machine Learning Data Science Python Data Ingestion 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.

DevOps 96

DevOps Machine Learning Data Scientist Software Engineer 96

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

AWS Machine Learning Blog

NOVEMBER 14, 2024

Transparency and explainability : Making sure that AI systems are transparent, explainable, and accountable. However, explaining why that decision was made requires next-level detailed reports from each affected model component of that AI system. Mitigation strategies : Implementing measures to minimize or eliminate risks.

ML 92

ML Auto-complete Machine Learning Auto-classification 92

Mlearning.ai

AUGUST 18, 2023

TL;DR This series explain how to implement intermediate MLOps with simple python code, without introducing MLOps frameworks (MLflow, DVC …). As a software engineer your role is to write code for a certain cause. As an ML engineer you’re in charge of some code/model. Replace MLOps with program .Source

DevOps 52

DevOps Python ML ML Engineer 52

AWS Machine Learning Blog

APRIL 26, 2023

The first is by using low-code or no-code ML services such as Amazon SageMaker Canvas , Amazon SageMaker Data Wrangler , Amazon SageMaker Autopilot , and Amazon SageMaker JumpStart to help data analysts prepare data, build models, and generate predictions. Adoption of AWS ML services such as SageMaker reduces these issues.

ML 116

ML Machine Learning Data Science Data Drift 116

AWS Machine Learning Blog

OCTOBER 19, 2023

Amazon SageMaker provides purpose-built tools for ML teams to automate and standardize processes across the ML lifecycle. For more data preparation options, check out the blog post series that explains how to use Amazon Comprehend to react, translate, and analyze text from either Amazon Athena or Amazon Redshift.

Machine Learning 115

Machine Learning Python ML Automation 115

The MLOps Blog

JUNE 27, 2023

Model governance and compliance : They should address model governance and compliance requirements, so you can implement ethical considerations, privacy safeguards, and regulatory compliance into your ML solutions. This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking.

Metadata 134

Metadata Machine Learning Data Scientist Data Quality 134

The MLOps Blog

OCTOBER 3, 2023

I started from tech, my first job was an internship at Google as a software engineer. I’m from Poland, and I remember when I got an offer from Google to join as a regular software engineer. I see so many of these job seekers, especially on the MLOps side or the ML engineer side. It’s two things.

ML 52

ML Data Scientist Machine Learning Data Science 52

The MLOps Blog

JULY 18, 2023

You have a bit of education in music composition, math, and science before you get more into the software engineering side of things. But you have started out in software design engineering, is that correct? Sabine: Right, so, Jason, to kind of warm you up a bit… In 1 minute, how would you explain conversational AI?

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Conversational AI Natural Language Processing AI AI 52

The MLOps Blog

MAY 9, 2023

One of the most prevalent complaints we hear from ML engineers in the community is how costly and error-prone it is to manually go through the ML workflow of building and deploying models. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times.

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ML Machine Learning Metadata Automation 98

The MLOps Blog

MARCH 21, 2023

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. In this comprehensive guide, we’ll explore everything you need to know about machine learning platforms, including: Components that make up an ML platform.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

Unite.AI

DECEMBER 15, 2023

My role at Prolific is split between being an advisor regarding AI use cases and opportunities, and being a more hands-on ML Engineer. I started my career in Software Engineering and have gradually transitioned to Machine Learning. Thus, building good explainability and interpretability tools is important.

Machine Learning 130

Machine Learning AI AI AI Modeling 130

The MLOps Blog

DECEMBER 28, 2022

Organization Gigaforce Inc Industry InsurTech provider Team size Gigaforce built an ML team three years ago in 2020 and has a team size of 5-7. Team composition The team comprises domain experts, data engineers, data scientists, and ML engineers.

ML 78

ML Data Scientist ML Engineer Machine Learning 78