DagsHub

OCTOBER 23, 2024

Managing unstructured data is essential for the success of machine learning (ML) projects. This article will discuss managing unstructured data for AI and ML projects. You will learn the following: Why unstructured data management is necessary for AI and ML projects. How to properly manage unstructured data.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

AWS Machine Learning Blog

MARCH 30, 2023

To ensure real-time updates of ball recovery times, we have implemented Amazon Managed Streaming for Apache Kafka (Amazon MSK) as a central solution for data streaming and messaging. A Lambda function retrieves all recovery times from the relevant Kafka topic and stores them in an Amazon Aurora Serverless database.

AWS 91

AWS Machine Learning Machine Learning Apache Kafka 91

Mlearning.ai

AUGUST 4, 2023

The focus of this investigation revolves around understanding their industry distribution, age demographics, developer types, and their adoption of various programming languages, databases, platforms, web frameworks, miscellaneous technologies, technical tools, new collaboration tools, and AI-powered search tools. NET Framework (1.0–4.8)’

Database 52

Database Apache Kafka SQL AI 52

The MLOps Blog

FEBRUARY 5, 2023

There are a number of tools that can help with streaming data collection and processing, some popular ones include: Apache Kafka : An open-source, distributed event streaming platform that can handle millions of events per second. 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

Pickl AI

JULY 8, 2024

Database Extraction: Retrieval from structured databases using query languages like SQL. Common options include: Relational Databases: Structured storage supporting ACID transactions, suitable for structured data. NoSQL Databases: Flexible, scalable solutions for unstructured or semi-structured data.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

Mlearning.ai

MAY 16, 2023

It is used to extract data from various sources, transform the data to fit a specific data model or schema, and then load the transformed data into a target system such as a data warehouse or a database. The events can be published to a message broker such as Apache Kafka or Google Cloud Pub/Sub.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

The MLOps Blog

MARCH 15, 2023

A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process. If a typical ML project involves standard pre-processing steps – why not make it reusable? Relational database connectors are available.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

phData

SEPTEMBER 27, 2024

It often involves specialized databases designed to handle this kind of atomic, temporal data. Technologies like Apache Kafka, often used in modern CDPs, use log-based approaches to stream customer events between systems in real-time. It’s precise but can impact database performance.

Data Models 52

Data Models Data Modeling Apache Kafka Data Lakes 52

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. At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. What are ML pipeline architecture design patterns?

ML 52

ML ML Machine Learning Machine Learning 52

Alation

AUGUST 4, 2022

New Big Data Concepts vs Cloud Delivered Databases? So, what has the emergence of cloud databases done to change big data? Spark, Tensorflow, Apache Kafka, et cetera, are all out found in cloud databases,” points out Jones. To improve ML and Ethics, data literacy training is critical.

Big Data 95

Big Data Big Data Apache Kafka Data Lakes 95

Pickl AI

DECEMBER 4, 2024

The rise of advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML) , and Big Data analytics is reshaping industries and creating new opportunities for Data Scientists. Apache Kafka), organisations can now analyse vast amounts of data as it is generated. Here are five key trends to watch.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

FEBRUARY 23, 2025

Best Big Data Tools Popular tools such as Apache Hadoop, Apache Spark, Apache Kafka, and Apache Storm enable businesses to store, process, and analyse data efficiently. Machine Learning Integration : Built-in ML capabilities streamline model development and deployment.

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 66

Analytics Analytics AWS Clustering 66