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.

Generative AI 73

Generative AI LLM Large Language Models Machine Learning 73

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.

Data Drift 99

Data Drift Auto-complete ML Automation 99

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.

Python 93

Python Automation Data Scientist ML 93

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

DevOps 52

DevOps Python ML ML Engineer 52

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.

DevOps 94

DevOps ML Machine Learning Automation 94

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.

DevOps 91

DevOps Automation ML Computer Vision 91

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.

Machine Learning 52

Machine Learning ML Automation 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 ML Engineer 52

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 ML Engineer 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

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.,

Machine Learning 134

Machine Learning Metadata Data Quality Data Scientist 134

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.

ML 77

ML Automation Data Scientist DevOps 77

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. .

LLM 83

LLM Data Scientist Algorithm Generative AI 83

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.

ML 98

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

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.

Large Language Models 52

Large Language Models Python Auto-complete Natural Language Processing 52