The MLOps Blog

FEBRUARY 5, 2023

The machine learning model is part of the Stream processing engine, and it provides the logic that helps the streaming data pipeline expose features within the stream and potentially within a historical data store. It can be used to collect, store, and process streaming data in real-time. Happy Learning!

Machine Learning 72

Machine Learning Machine Learning Data Pipeline Apache Kafka 72

ODSC - Open Data Science

OCTOBER 9, 2024

So what are you waiting for? Get your pass today !

Apache Kafka 40

Apache Kafka AI AI Machine Learning 40

DagsHub

OCTOBER 23, 2024

Managing unstructured data is essential for the success of machine learning (ML) projects. Without structure, data is difficult to analyze and extracting meaningful insights and patterns is challenging. This article will discuss managing unstructured data for AI and ML projects. What is Unstructured Data?

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Mlearning.ai

MAY 16, 2023

The events can be published to a message broker such as Apache Kafka or Google Cloud Pub/Sub. The message broker can then distribute the events to various subscribers such as data processing pipelines, machine learning models, and real-time analytics dashboards.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

The MLOps Blog

MARCH 15, 2023

Data pipeline stages But before delving deeper into the technical aspects of these tools, let’s quickly understand the core components of a data pipeline succinctly captured in the image below: Data pipeline stages | Source: Author What does a good data pipeline look like? No built-in data quality functionality.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

The MLOps Blog

AUGUST 11, 2023

There comes a time when every ML practitioner realizes that training a model in Jupyter Notebook is just one small part of the entire project. Getting a workflow ready which takes your data from its raw form to predictions while maintaining responsiveness and flexibility is the real deal.

ML 52

ML ML Machine Learning Machine Learning 52

AWS Machine Learning Blog

MARCH 30, 2023

The result is a machine learning (ML)-powered insight that allows fans to easily evaluate and compare the goalkeepers’ proficiencies. An ML model is trained through Amazon SageMaker , using data from four seasons of the first and second Bundesliga, encompassing all shots that landed on target (either resulting in a goal or being saved).

Machine Learning 89

Machine Learning Machine Learning AWS ML 89

Pickl AI

FEBRUARY 23, 2025

Summary: Big Data tools empower organizations to analyze vast datasets, leading to improved decision-making and operational efficiency. Ultimately, leveraging Big Data analytics provides a competitive advantage and drives innovation across various industries.

Big Data 52

Big Data Big Data Apache Hadoop Apache Kafka 52

AWS Machine Learning Blog

FEBRUARY 7, 2025

However, it lacked essential services required for machine learning (ML) applications, such as frontend and backend infrastructure, DNS, load balancers, scaling, blob storage, and managed databases. At that time, the application was deployed as a single monolithic container, which included Kafka and a database.

Analytics 68

Analytics Analytics AWS Clustering 68