IBM Journey to AI blog

NOVEMBER 29, 2023

Non-symbolic AI can be useful for transforming unstructured data into organized, meaningful information. This helps to simplify data analysis and enable informed decision-making. How event processing fuels AI By combining event processing and AI, businesses are helping to drive a new era of highly precise, data-driven decision making.

Artificial Intelligence 105

Artificial Intelligence Artificial Intelligence Apache Kafka AI 105

AWS Machine Learning Blog

APRIL 19, 2023

We use Amazon SageMaker to train a model using the built-in XGBoost algorithm on aggregated features created from historical transactions. Raj Ramasubbu is a Senior Analytics Specialist Solutions Architect focused on big data and analytics and AI/ML with Amazon Web Services.

ML 98

ML ML Apache Kafka SQL 98

Pickl AI

DECEMBER 4, 2024

This explosive growth is driven by the increasing volume of data generated daily, with estimates suggesting that by 2025, there will be around 181 zettabytes of data created globally. The field has evolved significantly from traditional statistical analysis to include sophisticated Machine Learning algorithms and Big Data technologies.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

JULY 29, 2024

It utilises the Hadoop Distributed File System (HDFS) and MapReduce for efficient data management, enabling organisations to perform big data analytics and gain valuable insights from their data. In a Hadoop cluster, data stored in the Hadoop Distributed File System (HDFS), which spreads the data across the nodes.

Hadoop 52

Hadoop Clustering Big Data Big Data 52

Pickl AI

JULY 20, 2023

Predictive Analytics Projects: Predictive analytics involves using historical data to predict future events or outcomes. Techniques like regression analysis, time series forecasting, and machine learning algorithms are used to predict customer behavior, sales trends, equipment failure, and more.

Analytics 40

Analytics Analytics Big Data Big Data 40

ODSC - Open Data Science

OCTOBER 9, 2024

Tools like Harness and JenkinsX use machine learning algorithms to predict potential deployment failures, manage resource usage, and automate rollback procedures when something goes wrong. In the world of DevOps, AI can help monitor infrastructure, analyze logs, and detect performance bottlenecks in real-time.

Apache Kafka 40

Apache Kafka AI AI Machine Learning 40

Pickl AI

OCTOBER 21, 2024

They provide flexibility in data models and can scale horizontally to manage large volumes of data. NoSQL is well-suited for big data applications and real-time analytics, allowing organisations to adapt to rapidly changing data landscapes. Examples include MongoDB, Cassandra, and Redis.

Database 52

Database Apache Kafka Machine Learning Machine Learning 52

DagsHub

OCTOBER 23, 2024

Data Lakes Data lakes are centralized repositories designed to store vast amounts of raw, unstructured, and structured data in their native format. They enable flexible data storage and retrieval for diverse use cases, making them highly scalable for big data applications.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

The MLOps Blog

MARCH 15, 2023

Listed below are some of the common types of data pipeline tools: Commercial vs open-source data pipeline tools When a business needs full control over the development process and wants to build highly customizable complex solutions, open-source tools come in handy. No built-in data quality functionality. No expert support.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

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