ODSC - Open Data Science

MAY 10, 2023

Embrace Data-Centric AI The key to unlocking value in AI lies in a data-centric approach, according to Andrew Ng. Data that has errors and is messy, is often fixed by ad hoc data engineering that relies on luck or individual data scientists’ skills. Your training data is a finite resource.

Data Scientist 87

Data Scientist Data Science Deep Learning Deep Learning 87

phData

AUGUST 24, 2023

Greater Accuracy Machine learning models can handle high-dimensional, nonlinear, and interactive relationships between variables. These nuanced algorithms can lead to more accurate and reliable credit scores and decisions. They can process large amounts of data in real time, providing instant credit scores and decisions.

ML 52

ML ML Machine Learning Machine Learning 52

Mlearning.ai

NOVEMBER 29, 2023

All the previously, recently, and currently collected data is used as input for time series forecasting where future trends, seasonal changes, irregularities, and such are elaborated based on complex math-driven algorithms. This results in quite efficient sales data predictions. In its core, lie gradient-boosted decision trees.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

JUNE 23, 2023

In the world of artificial intelligence (AI), data plays a crucial role. It is the lifeblood that fuels AI algorithms and enables machines to learn and make intelligent decisions. And to effectively harness the power of data, organizations are adopting data-centric architectures in AI. text, images, videos).

AI 52

AI AI Data Governance Data Quality 52

AWS Machine Learning Blog

NOVEMBER 29, 2023

Customers can select relevant evaluation datasets and metrics for their scenarios and extend them with their own prompt datasets and evaluation algorithms. Data scientists can analyze detailed results with SageMaker Clarify visualizations in Notebooks, SageMaker Model Cards, and PDF reports. temperature: 0.6

Algorithm 78

Algorithm ML ML Data Scientist 78

DagsHub

APRIL 21, 2024

You need to make that model available to the end users, monitor it, and retrain it for better performance if needed. MLOps helps these organizations to continuously monitor the systems for accuracy and fairness, with automated processes for model retraining and deployment as new data becomes available.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Tableau

MAY 6, 2021

Einstein Discovery in Tableau uses machine learning (ML) to create models and deliver predictions and recommendations within the analytics workflow. No code or algorithms needed. Einstein sifted through the data, discovered patterns, and surfaced recommendations in natural language. The best part?

Tableau 100

Tableau Natural Language Processing Machine Learning Machine Learning 100

Iguazio

FEBRUARY 8, 2024

Data Pipeline - Manages and processes various data sources. Application Pipeline - Manages requests and data/model validations. Multi-Stage Pipeline - Ensures correct model behavior and incorporates feedback loops. ML Pipeline - Focuses on training, validation and deployment. How Does MLOps Work?

ML 52

ML ML AI AI 52

The MLOps Blog

NOVEMBER 30, 2022

These days enterprises are sitting on a pool of data and increasingly employing machine learning and deep learning algorithms to forecast sales, predict customer churn and fraud detection, etc., Data science practitioners experiment with algorithms, data, and hyperparameters to develop a model that generates business insights.

ML 52

ML ML Machine Learning Machine Learning 52

AWS Machine Learning Blog

JUNE 22, 2023

New machines are added continuously to the system, so we had to make sure our model can handle prediction on new machines that have never been seen in training. Data preprocessing and feature engineering In this section, we discuss our methods for data preparation and feature engineering.

AWS 78

AWS ML ML Machine Learning 78

Tableau

MAY 6, 2021

Einstein Discovery in Tableau uses machine learning (ML) to create models and deliver predictions and recommendations within the analytics workflow. No code or algorithms needed. Einstein sifted through the data, discovered patterns, and surfaced recommendations in natural language. The best part?

Tableau 52

Tableau Natural Language Processing Machine Learning Machine Learning 52

The MLOps Blog

MAY 9, 2023

A typical machine learning pipeline with various stages highlighted | Source: Author Common types of machine learning pipelines In line with the stages of the ML workflow (data, model, and production), an ML pipeline comprises three different pipelines that solve different workflow stages. They include: 1 Data (or input) pipeline.

ML 98

ML ML Machine Learning Machine Learning 98

The MLOps Blog

JUNE 27, 2023

Learn more The Best Tools, Libraries, Frameworks and Methodologies that ML Teams Actually Use – Things We Learned from 41 ML Startups [ROUNDUP] Key use cases and/or user journeys Identify the main business problems and the data scientist’s needs that you want to solve with ML, and choose a tool that can handle them effectively.

Machine Learning 134

Machine Learning Machine Learning ML ML 134