AWS Machine Learning Blog

FEBRUARY 27, 2024

The SageMaker project template includes seed code corresponding to each step of the build and deploy pipelines (we discuss these steps in more detail later in this post) as well as the pipeline definition—the recipe for how the steps should be run. Pavel Maslov is a Senior DevOps and ML engineer in the Analytic Platforms team.

Machine Learning 129

Machine Learning DevOps Data Scientist Data Quality 129

AWS Machine Learning Blog

DECEMBER 19, 2023

Machine learning operations (MLOps) applies DevOps principles to ML systems. Just like DevOps combines development and operations for software engineering, MLOps combines ML engineering and IT operations. This triggers the creation of the model deployment pipeline for that ML model.

Machine Learning 121

Machine Learning ML Engineer DevOps ML 121

AWS Machine Learning Blog

NOVEMBER 9, 2023

Under Advanced Project Options , for Definition , select Pipeline script from SCM. She is passionate about developing, deploying, and explaining AI/ ML solutions across various domains. Saswata Dash is a DevOps Consultant with AWS Professional Services. Select This project is parameterized. For Name , enter prodAccount.

Data Drift 116

Data Drift Auto-complete ML Automation 116

The MLOps Blog

APRIL 17, 2023

Explainability : Making sure they can explain their experiment results. This, of course, is not true in all situations, but in circumstances where they need to understand how and why your model makes predictions, “explainability” becomes crucial. Legal compliance is another reason why explainability is essential.

Metadata 52

Metadata Data Scientist Explainability ML 52

Snorkel AI

JUNE 22, 2023

Tools like Bigeye, Deequ, and HoloClean are leading the charge here as we see the shift moving from data health and data monitoring to fixing data on the right side—changing data to improve ML and to improve operations, there are definitely risks. Most, most definitely. So, how do you tackle this? Where do we go next?

Data Quality 52

Data Quality Automation Data Scientist ML 52

Snorkel AI

JUNE 22, 2023

Tools like Bigeye, Deequ, and HoloClean are leading the charge here as we see the shift moving from data health and data monitoring to fixing data on the right side—changing data to improve ML and to improve operations, there are definitely risks. Most, most definitely. So, how do you tackle this? Where do we go next?

Data Quality 52

Data Quality Automation Data Scientist ML 52

Snorkel AI

JUNE 22, 2023

Tools like Bigeye, Deequ, and HoloClean are leading the charge here as we see the shift moving from data health and data monitoring to fixing data on the right side—changing data to improve ML and to improve operations, there are definitely risks. Most, most definitely. So, how do you tackle this? Where do we go next?

Data Quality 52

Data Quality Automation Data Scientist ML 52

The MLOps Blog

OCTOBER 3, 2023

Mikiko Bazeley: You definitely got the details correct. For me, it was a little bit of a longer journey because I kind of had data engineering and cloud engineering and DevOps engineering in between. I definitely don’t think I’m an influencer. And so what we do is version the definitions. For example, Feast.

ML 52

ML Data Scientist Machine Learning Data Science 52

Explosion

JUNE 18, 2021

How implement models ML fundamentals training and evaluation improve accuracy use library APIs Python and DevOps What when to use ML decide what models and components to train understand what application will use outputs for find best trade-offs select resources and libraries The “how” is everything that helps you execute the plan.

NLP 52

NLP Machine Learning Natural Language Processing DevOps 52

The MLOps Blog

MARCH 21, 2023

” — Isaac Vidas , Shopify’s ML Platform Lead, at Ray Summit 2022 Monitoring Monitoring is an essential DevOps practice, and MLOps should be no different. Collaboration The principles you have learned in this guide are mostly born out of DevOps principles. My Story DevOps Engineers Who they are? Model serving.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

AWS Machine Learning Blog

JANUARY 13, 2023

TR’s AI Platform microservices are built with Amazon SageMaker as the core engine, AWS serverless components for workflows, and AWS DevOps services for CI/CD practices. Proper AWS Identity and Access Management (IAM) role definition for the experimentation workspace was hard to define. Bring a single pane of glass for ML activities.

ML 85

ML Data Scientist Machine Learning Metadata 85

Mlearning.ai

JUNE 16, 2023

Advise on getting started on topics Recommend get started materials Explain an implementation Explain general concepts in specific industry domain (e.g. Basic programming tasks can at least be prepared, I am seeing the trend of AI-assisted developers. Here are some articles 1 , 2 , 3.

Software Development 52

Software Development Generative AI ChatGPT Prompt Engineer 52

The MLOps Blog

JUNE 5, 2023

Stephen: Yeah, absolutely, we’ll definitely delve into that. To explain that a little further, when you think about what those models are, the way that GPT-3 or the other similar language models are trained is on this corpus of data called the Common Crawl, which is essentially the whole internet, right? What is GPT-3?

Large Language Models 52

Large Language Models Machine Learning Software Engineer Natural Language Processing 52