Flipboard

MARCH 7, 2023

This post presents and compares options and recommended practices on how to manage Python packages and virtual environments in Amazon SageMaker Studio notebooks. Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models.

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Python Data Science Data Scientist Automation 123

AWS Machine Learning Blog

DECEMBER 19, 2023

Many businesses already have data scientists and ML engineers who can build state-of-the-art models, but taking models to production and maintaining the models at scale remains a challenge. Machine learning operations (MLOps) applies DevOps principles to ML systems.

Machine Learning 130

Machine Learning ML Engineer DevOps ML 130

AWS Machine Learning Blog

JULY 8, 2024

TWCo data scientists and ML engineers took advantage of automation, detailed experiment tracking, integrated training, and deployment pipelines to help scale MLOps effectively. ML model experimentation is one of the sub-components of the MLOps architecture. For this proof of concept, pipelines were set up using this SDK.

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Data Scientist ML Engineer Machine Learning Data Science 117

AWS Machine Learning Blog

OCTOBER 28, 2024

Create a SageMaker Model Monitor schedule Next, you use the Amazon SageMaker Python SDK to create a model monitoring schedule. You can use this framework as a starting point to monitor your custom metrics or handle other unique requirements for model quality monitoring in your AI/ML applications. About the Authors Joe King is a Sr.

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ML Metadata Data Scientist DevOps 107

AWS Machine Learning Blog

AUGUST 22, 2024

In this post, we show you how to convert Python code that fine-tunes a generative AI model in Amazon Bedrock from local files to a reusable workflow using Amazon SageMaker Pipelines decorators. You can use Amazon SageMaker Model Building Pipelines to collaborate between multiple AI/ML teams. We use Python to do this.

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AWS Machine Learning Blog

JUNE 27, 2023

They needed a cloud platform and a strategic partner with proven expertise in delivering production-ready AI/ML solutions, to quickly bring EarthSnap to the market. That is where Provectus , an AWS Premier Consulting Partner with competencies in Machine Learning, Data & Analytics, and DevOps, stepped in.

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DevOps ML Machine Learning ML Engineer 115

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. In this example, you will use a Python function. Download the complete Python file , including the function and all imported libraries.

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Automation LLM DevOps Python 85

AWS Machine Learning Blog

OCTOBER 2, 2023

Use case: Inspecting the quality of metal tags As an ML engineer, it’s important to understand the business case you are working on. In this example, we’re using the Ultralytics YOLOv8 Python package and model architecture to train and export an object detection model to the ONNX ML model format for portability.

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DevOps Automation ML Computer Vision 118

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 …). My interpretation to MLOps is similar to my interpretation of DevOps. As a software engineer your role is to write code for a certain cause. Replace MLOps with program .Source

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DevOps Python ML ML Engineer 52

AWS Machine Learning Blog

NOVEMBER 21, 2023

NVIDIA Triton Inference Server provides two different kind backends: one for hosting models on GPU, and a Python backend where you can bring your own custom code to be used in preprocessing and postprocessing steps. This includes overheads like load balancing, preprocessing, model inferencing, and postprocessing times.

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ML Deep Learning Machine Learning DevOps 108

AWS Machine Learning Blog

DECEMBER 13, 2023

ML operations, known as MLOps, focus on streamlining, automating, and monitoring ML models throughout their lifecycle. Data scientists, ML engineers, IT staff, and DevOps teams must work together to operationalize models from research to deployment and maintenance.

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ML Automation Metadata Software Development 123

AWS Machine Learning Blog

SEPTEMBER 23, 2024

Throughout this exercise, you use Amazon Q Developer in SageMaker Studio for various stages of the development lifecycle and experience firsthand how this natural language assistant can help even the most experienced data scientists or ML engineers streamline the development process and accelerate time-to-value.

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ML Computer Vision Data Scientist Machine Learning 86

AWS Machine Learning Blog

FEBRUARY 7, 2025

ML engineers Develop model deployment pipelines and control the model deployment processes. ML engineers create the pipelines in Github repositories, and the platform engineer converts them into two different Service Catalog portfolios: ML Admin Portfolio and SageMaker Project Portfolio.

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ML Data Scientist ML Engineer Data Science 63

The MLOps Blog

JUNE 27, 2023

Knowledge and skills in the organization Evaluate the level of expertise and experience of your ML team and choose a tool that matches their skill set and learning curve. For example, if your team is proficient in Python and R, you may want an MLOps tool that supports open data formats like Parquet, JSON, CSV, etc.,

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Machine Learning Metadata Data Scientist Data Quality 134

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

The MLOps Blog

SEPTEMBER 7, 2023

This situation is not different in the ML world. Data Scientists and ML Engineers typically write lots and lots of code. Related post MLOps Is an Extension of DevOps. These combinations of Python code and SQL play a crucial role but can be challenging to keep them robust for their entire lifetime. Aside neptune.ai

Software Engineer 52

Software Engineer Machine Learning ETL Data Scientist 52

AWS Machine Learning Blog

NOVEMBER 9, 2023

She is passionate about developing, deploying, and explaining AI/ ML solutions across various domains. Prior to this role, she led multiple initiatives as a data scientist and ML engineer with top global firms in the financial and retail space. Saswata Dash is a DevOps Consultant with AWS Professional Services.

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Data Drift Auto-complete ML Automation 121

AWS Machine Learning Blog

JUNE 8, 2023

This is your Custom Python Hook speaking!" Ryan Gomes is a Data & ML Engineer with the AWS Professional Services Intelligence Practice. Solutions Architect at Amazon Web Services with specialization in DevOps and Observability. He leads the NYC machine learning and AI meetup. Mahesh Birardar is a Sr.

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Generative AI LLM Machine Learning Large Language Models 92

Viso.ai

MARCH 29, 2024

MLflow is an open-source platform designed to manage the entire machine learning lifecycle, making it easier for ML Engineers, Data Scientists, Software Developers, and everyone involved in the process. MLflow can be seen as a tool that fits within the MLOps (synonymous with DevOps) framework.

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Machine Learning ML Automation Data Scientist 52

The MLOps Blog

AUGUST 3, 2023

This is Piotr Niedźwiedź and Aurimas Griciūnas from neptune.ai , and you’re listening to ML Platform Podcast. Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. You could almost think of Hamilton as DBT for Python functions. Piotr: This is procedural Python code.

ML 52

ML Data Scientist Software Engineer Machine Learning 52

The MLOps Blog

OCTOBER 3, 2023

How did you manage to jump from a more analytical, scientific type of role to a more engineering one? I actually did not pick up Python until about a year before I made the transition to a data scientist role. I switched from analytics to data science, then to machine learning, then to data engineering, then to MLOps.

ML 52

ML Data Scientist Machine Learning Data Science 52

AWS Machine Learning Blog

JUNE 14, 2023

Amazon SageMaker is a fully managed service to prepare data and build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows. You can also add your own Python scripts and transformations to customize workflows. Python code file.

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ML Data Scientist Automation DevOps 100

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 Data Science 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.

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Machine Learning Data Scientist ML Metadata 69

AWS Machine Learning Blog

NOVEMBER 29, 2023

Data scientists collaborate with ML engineers to transition code from notebooks to repositories, creating ML pipelines using Amazon SageMaker Pipelines, which connect various processing steps and tasks, including pre-processing, training, evaluation, and post-processing, all while continually incorporating new production data. .

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Mlearning.ai

MAY 3, 2023

An open-source, low-code Python wrapper for easy usage of the Large Language Models such as ChatGPT, AutoGPT, LLaMa, GPT-J, and GPT4All An introduction to “ pychatgpt_gui” —  A GUI-based APP for LLM’s with custom-data training and pre-trained inferences. It is an open-source python package. The launched APP snapshot is as seen below.

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Large Language Models Python Auto-complete Natural Language Processing 52