Pickl AI

SEPTEMBER 18, 2024

Summary: Netflix’s sophisticated Big Data infrastructure powers its content recommendation engine, personalization, and data-driven decision-making. As a pioneer in the streaming industry, Netflix utilises advanced data analytics to enhance user experience, optimise operations, and drive strategic decisions.

Big Data 52

Big Data Big Data Apache Kafka Big Data Analytics 52

Pickl AI

DECEMBER 2, 2024

Summary: Big Data encompasses vast amounts of structured and unstructured data from various sources. Key components include data storage solutions, processing frameworks, analytics tools, and governance practices. Key Takeaways Big Data originates from diverse sources, including IoT and social media.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

NOVEMBER 25, 2024

Summary: Big Data encompasses vast amounts of structured and unstructured data from various sources. Key components include data storage solutions, processing frameworks, analytics tools, and governance practices. Key Takeaways Big Data originates from diverse sources, including IoT and social media.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Precisely

DECEMBER 28, 2023

How do streaming data pipelines work? The first step in a streaming data pipeline is where information enters the pipeline. One very popular platform is Apache Kafka , a powerful open-source tool used by thousands of companies. Interested in learning more about streaming data pipelines for your organization?

Data Pipeline 91

Data Pipeline Apache Kafka Big Data Big Data 91

AWS Machine Learning Blog

MARCH 30, 2023

How it’s implemented Positional data from an ongoing match, which is recorded at a sampling rate of 25 Hz, is utilized to determine the time taken to recover the ball. This allows for seamless communication of positional data and various outputs of Bundesliga Match Facts between containers in real time.

AWS 100

AWS Machine Learning Machine Learning Apache Kafka 100

IBM Journey to AI blog

NOVEMBER 29, 2023

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. Events as fuel for AI Models: Artificial intelligence models rely on big data to refine the effectiveness of their capabilities.

Artificial Intelligence 105

Artificial Intelligence Artificial Intelligence Apache Kafka AI 105

DagsHub

OCTOBER 23, 2024

Unstructured data makes up 80% of the world's data and is growing. 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.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

AWS Machine Learning Blog

APRIL 19, 2023

Businesses are increasingly using machine learning (ML) to make near-real-time decisions, such as placing an ad, assigning a driver, recommending a product, or even dynamically pricing products and services. Apache Flink is a popular framework and engine for processing data streams.

ML 98

ML ML Apache Kafka SQL 98

ODSC - Open Data Science

JULY 14, 2023

AI and Bias: How to Detect It and How to Prevent It Sandra Wachter, PhD | Professor, Technology and Regulation | Oxford Internet Institute, University of Oxford In recognition of the extensive biases and inequality that are present in training data, there has been much work done to test for bias in machine learning and AI systems.

Machine Learning 52

Machine Learning Machine Learning Apache Kafka Data Science 52

Pickl AI

DECEMBER 4, 2024

Summary: The future of Data Science is shaped by emerging trends such as advanced AI and Machine Learning, augmented analytics, and automated processes. As industries increasingly rely on data-driven insights, ethical considerations regarding data privacy and bias mitigation will become paramount.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

The MLOps Blog

FEBRUARY 5, 2023

We will also get familiar with tools that can help record this data and further analyse it. In the later part of this article, we will discuss its importance and how we can use machine learning for streaming data analysis with the help of a hands-on example. What is streaming data? pip install tensorflow== 2.7.1 !

Machine Learning 72

Machine Learning Machine Learning Data Pipeline Apache Kafka 72

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

These tools use machine learning models trained on vast amounts of code to assist developers in writing cleaner, more efficient code. Tools like Testim and Applitools leverage machine learning to improve both unit testing and UI testing. How you might ask?

Apache Kafka 40

Apache Kafka AI AI Machine Learning 40

Pickl AI

NOVEMBER 4, 2024

Introduction Data Engineering is the backbone of the data-driven world, transforming raw data into actionable insights. As organisations increasingly rely on data to drive decision-making, understanding the fundamentals of Data Engineering becomes essential. million by 2028.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

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

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 Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 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

AWS Machine Learning Blog

MARCH 30, 2023

Therefore, it’s no surprise that determining the proficiency of goalkeepers in preventing the ball from entering the net is considered one of the most difficult tasks in football data analysis. The result is a machine learning (ML)-powered insight that allows fans to easily evaluate and compare the goalkeepers’ proficiencies.

Machine Learning 101

Machine Learning Machine Learning AWS ML 101

The MLOps Blog

AUGUST 11, 2023

At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. Many questions regarding building machine learning pipelines and systems have already been answered and come from industry best practices and patterns. How should the machine learning pipeline operate?

ML 52

ML ML Machine Learning Machine Learning 52

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

Pickl AI

MARCH 19, 2025

Python, SQL, and Apache Spark are essential for data engineering workflows. Real-time data processing with Apache Kafka enables faster decision-making. offers Data Science courses covering essential data tools with a job guarantee. It integrates well with various data sources, making analysis easier.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 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 67

Analytics Analytics AWS Clustering 67