O'Reilly Media

OCTOBER 19, 2021

While there isn’t an authoritative definition for the term, it shares its ethos with its predecessor, the DevOps movement in software engineering: by adopting well-defined processes, modern tooling, and automated workflows, we can streamline the process of moving from development to robust production deployments. Versioning.

DevOps 138

DevOps Software Engineer Data Scientist ML 138

Marktechpost

JULY 4, 2023

” Even for seasoned programmers, the syntax of shell commands might need to be explained. Regex generation Regular expression generation is time-consuming for developers; however, Autoregex.xyz leverages GPT-3 to automate the process.

Large Language Models 102

Large Language Models BERT Natural Language Processing Categorization 102

Mlearning.ai

MARCH 16, 2023

You have probably heard the term DevOps in conventional software development. To do so, run: py.decision_trees_experiment.py --data-file " /data/diabetes.csv" --mode "bayesian" –target “Y” --experiment-id {add_experiment_id_here} The entire argument list is explained in the readme file.

Machine Learning 52

Machine Learning DevOps Software Development Data Scientist 52

IBM Journey to AI blog

FEBRUARY 29, 2024

The system uses Docker images, which are read-only templates that are used for building containers, and Dockerfiles, which are text files that accompany and explain Docker images. This is managed by the container runtime—a software solution interacting with the OS to make the necessary room to run container images.

DevOps 230

DevOps Software Development Artificial Intelligence Artificial Intelligence 230

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

The MLOps Blog

APRIL 17, 2023

Explainability : Making sure they can explain their experiment results. Your users should be able to track changes to the model development codebase (data processing code, pipeline code, utility scripts, et cetera) that directly influence how they run experiments and the corresponding results.

Metadata 52

Metadata Data Scientist Explainability ML 52

Mlearning.ai

JUNE 16, 2023

Increase your productivity in software development with Generative AI As I mentioned in Generative AI use case article, we are seeing AI-assisted developers. SDLC stages Let’s review software development lifecycle first. Then software development phases are planned to deliver the software.

Software Development 52

Software Development Generative AI ChatGPT Prompt Engineer 52

IBM Journey to AI blog

MARCH 6, 2024

Software development is one arena where we are already seeing significant impacts from generative AI tools. A McKinsey study claims that software developers can complete coding tasks up to twice as fast with generative AI. A burned-out developer is usually an unproductive one.

Generative AI 288

Generative AI DevOps Software Development Auto-complete 288

Flipboard

MARCH 7, 2023

Because of this difference, there are some specifics of how you create and manage virtual environments in Studio notebooks , for example usage of Conda environments or persistence of ML development environments between kernel restarts. He develops and codes cloud native solutions with a focus on big data, analytics, and data engineering.

Python 93

Python Automation Data Scientist ML 93

The MLOps Blog

APRIL 5, 2023

These teams may include but are not limited to data scientists, software developers, machine learning engineers, and DevOps engineers. Save and Load Machine Learning Models in Python with scikit-learn ML Explained – Aggregate Intellect – AI.SCIENCE Model Packaging Overview (NLP + MLOps workshop sneak peak)

ML 69

ML Machine Learning Deep Learning Metadata 69

The MLOps Blog

MARCH 21, 2023

Version control for code is common in software development, and the problem is mostly solved. ” — Isaac Vidas , Shopify’s ML Platform Lead, at Ray Summit 2022 Monitoring Monitoring is an essential DevOps practice, and MLOps should be no different. My Story DevOps Engineers Who they are? Model serving.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69