AWS Machine Learning Blog

JUNE 27, 2023

After the model results had been restored, the scattered components of the ML workflow were merged into an automated ML pipeline using Amazon SageMaker Pipelines, a purpose-built CI/CD service for ML. After the model is confirmed to be working as expected, it’s deployed to the production environment (CD).

DevOps 94

DevOps ML Machine Learning Automation 94

AWS Machine Learning Blog

APRIL 21, 2023

The target attribute is binary, meaning the model predicts the output as one of two categories ( True or False ). To expand this solution, you could incorporate this into a custom SageMaker project that also incorporates CI/CD and automated triggers using standardized MLOps templates.

Data Drift 74

Data Drift Metadata Data Quality ML 74

Explosion

AUGUST 3, 2017

Large models made CI tricky, so the new models are less than 1/10th the size. prodigy dataset gh_issues "Classify issues on GitHub" prodigy textcat.teach gh_issues en_core_web_sm "docs" --api github --label DOCS Opening localhost:8080 , we get a sequence of recent GitHub issue titles, displayed with our category as the title.

Convolutional Neural Networks 52

Convolutional Neural Networks Python Machine Learning Algorithm 52

Unite.AI

JANUARY 15, 2024

CodePal AI Tools CodePal offers a range of AI tools that can be grouped into seven categories: Code Writers Code Helpers DevOps Web Developers Product Tools Excel Tools Superheroes I will explain each tool so you get an overview of everything CodePal can do. CI/CD Writer: Create CI/CD pipelines on any platform.

Explainability 147

Explainability DevOps AI AI 147

IBM Journey to AI blog

NOVEMBER 14, 2023

Figure 1: The Data Monetization Lifecycle The raw data that fuels data monetization will come from three source categories: enterprise systems, external data and personal data. It is a supply chain from your raw data sources to the transparent and traceable exchange of value when a data asset is consumed in an end-user experience.

ESG 287

ESG Generative AI AI AI 287

The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. CI/CD/CT compatibility : how well does it work with CI/CD tools?

Machine Learning 134

Machine Learning Metadata Data Quality Data Scientist 134

AWS Machine Learning Blog

MARCH 1, 2023

MLOps is the discipline of integrating ML workloads into release management, CI/CD, and operations. In this dataset, data is classified in four news categories: WORLD , SPORTS , BUSINESS , and SCI_TEC. MLOps requires the integration of software development, operations, data engineering, and data science.

Data Ingestion 84

Data Ingestion DevOps Automation ML 84

AWS Machine Learning Blog

OCTOBER 10, 2023

There are two major categories of FL depending on the client type: cross-device and cross-silo. There may be tens of training rounds until the global model converges or until the number of training rounds reaches a threshold. One important challenge to building such an architecture is to enable the ease of deployment.

ML 92

ML Algorithm Machine Learning Neural Network 92

The MLOps Blog

OCTOBER 20, 2023

You could use a CI/CD workflow to create and push an HTML rendering of your notebook to S3 when the code gets merged into a specific branch. For example, you can use tools like nbconvert to generate PDFs from Jupyter notebooks or export them as HTML pages, so that they can be easily shared with anyone, even outside the technical teams.

Data Scientist 52

Data Scientist Python Explainability ETL 52

The MLOps Blog

OCTOBER 3, 2023

Mikiko Bazeley: I think it comes down to understanding the different types of feature stores that are out there, and even understanding why a virtual feature store is maybe just a terrible name for what FeatureForm is category-wise; it’s not very descriptive. There are three types of feature stores. That was a project I worked on.

ML 52

ML Data Scientist Machine Learning Data Science 52

The MLOps Blog

MAY 9, 2023

For a fully automated machine learning pipeline, continuous integration (CI), continuous delivery (CD), and continuous training ( CT ) become crucial. Pipelines can be scheduled to carry out CI, CD, or CT. They can also be triggered by: 1 model drift, 2 data drift, 3 on-demand by the data scientist in charge.

ML 98

ML Machine Learning Metadata Automation 98

AWS Machine Learning Blog

JANUARY 13, 2023

TR has identified five major categories that modularize all TR’s requirements: Data service – To enable easy and secured access to enterprise data assets. Model deployment service – To provide various inference deployment options following TR’s enterprise CI/CD practices. Bring a single pane of glass for ML activities.

ML 75

ML Data Scientist Machine Learning Metadata 75

AWS Machine Learning Blog

JANUARY 9, 2023

This ensures portability across platforms, immutable infrastructure deployments, and easier change management and CI/CD implementations. The ML application in this use case may want to use different custom models for recommending different categories of products.

ML 74

ML Auto-complete Auto-classification Deep Learning 74