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? Learn more lessons from the field with Comet experts.

DevOps 96

DevOps Machine Learning Data Scientist Software Engineer 96

Pickl AI

JANUARY 30, 2023

A Software Developer Uses Python: Backend Development : Python finds applications in developing server-side applications and APIs. The developer will use frameworks such as Django and Flask for this. Model Development: Use libraries such as TensorFlow, Keras, PyTorch, scikit-learn, etc.,

Python 97

Python DevOps Machine Learning Data Science 97

Chatbots Life

MAY 16, 2023

10CLOUDS Year Founded : 2009 HQ : Warsaw, Poland Team Size : 51–200 employees Clients : TrustStamp (Identity verification), Emergent Tech (G-Coin), AlephZero (Blockchain), Tapeke (BitCoin Software Development), Tagasauris (Crowdsourcing Software Development), CallerSmart.

Generative AI 98

Generative AI Chatbots Conversational AI Software Development 98

The MLOps Blog

SEPTEMBER 7, 2023

Have you ever talked to your Front-end or Back-end engineer peers and noticed how much they care about code quality? Writing legible, reusable, and efficient code has always been a challenge in the software development community. This situation is not different in the ML world. Aside neptune.ai What’s next?

Software Engineer 52

Software Engineer Machine Learning ETL Data Scientist 52

AWS Machine Learning Blog

AUGUST 22, 2024

As you move from pilot and test phases to deploying generative AI models at scale, you will need to apply DevOps practices to ML workloads. Data scientists, ML engineers, IT staff, and DevOps teams must work together to operationalize models from research to deployment and maintenance.

Generative AI 120

Generative AI Metadata Python ML 120

Mlearning.ai

AUGUST 18, 2023

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. DevOps cover all of the rest, like deployment, scheduling of automatic tests on code change, scaling machines to demanding load, cloud permissions, db configuration and much more.

DevOps 52

DevOps Python ML ML Engineer 52

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.

ML 86

ML Computer Vision Data Scientist Software Development 86

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. With a passion for automation, Joerg has worked as a software developer, DevOps engineer, and Site Reliability Engineer in his pre-AWS life.

DevOps 118

DevOps Automation ML Computer Vision 118

AWS Machine Learning Blog

NOVEMBER 21, 2023

Prior to working at Amazon Music, Siddharth was working at companies like Meta, Walmart Labs, Rakuten on E-Commerce centric ML Problems. Tarun Sharma is a Software Development Manager leading Amazon Music Search Relevance. Siddharth spent early part of his career working with bay area ad-tech startups.

ML 108

ML Deep Learning Machine Learning DevOps 108

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

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.

ML 125

ML Automation Metadata Software Development 125

Flipboard

MARCH 7, 2023

There are also limited options for ad hoc script customization by users, such as data scientists or ML engineers, due to permissions of the user profile execution role. He develops and codes cloud native solutions with a focus on big data, analytics, and data engineering.

Python 123

Python Data Scientist Data Science Automation 123

AWS Machine Learning Blog

JUNE 8, 2023

Victor Rojo is a highly experienced technologist who is passionate about the latest in AI, ML, and software development. 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.

Generative AI 86

Generative AI LLM Machine Learning Large Language Models 86

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