ODSC - Open Data Science

MAY 31, 2023

Be sure to check out his talk, “ Apache Kafka for Real-Time Machine Learning Without a Data Lake ,” there! The combination of data streaming and machine learning (ML) enables you to build one scalable, reliable, but also simple infrastructure for all machine learning tasks using the Apache Kafka ecosystem.

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Data Lakes Machine Learning Machine Learning Apache Kafka 131

IBM Journey to AI blog

APRIL 2, 2024

However, IBM MQ and Apache Kafka can sometimes be viewed as competitors, taking each other on in terms of speed, availability, cost and skills. MQ and Apache Kafka: Teammates Simply put, they are different technologies with different strengths, albeit often perceived to be quite similar. Interested in learning more?

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IBM Journey to AI blog

NOVEMBER 21, 2023

Apache Kafka is a well-known open-source event store and stream processing platform and has grown to become the de facto standard for data streaming. Apache Kafka transfers data without validating the information in the messages. What is a schema registry?

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Apache Kafka Clustering Data Quality Data Governance 119

IBM Journey to AI blog

JANUARY 8, 2024

They often use Apache Kafka as an open technology and the de facto standard for accessing events from a various core systems and applications. IBM provides an Event Streams capability build on Apache Kafka that makes events manageable across an entire enterprise.

EDA 110

EDA Apache Kafka Clustering Data Governance 110

phData

MAY 3, 2024

How Snowflake Helps Achieve Real-Time Analytics Snowflake is the ideal platform to achieve real-time analytics for several reasons, but two of the biggest are its ability to manage concurrency due to the multi-cluster architecture of Snowflake and its robust connections to 3rd party tools like Kafka. Looking for additional help?

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Apache Kafka Analytics Analytics ETL 52

Data Science Dojo

JULY 24, 2023

Clusters : Clusters are groups of interconnected nodes that work together to process and store data. Clustering allows for improved performance and fault tolerance as tasks can be distributed across nodes. Each node is capable of processing and storing data independently.

Big Data 195

Big Data Big Data Data Engineering Data Engineer 195

IBM Journey to AI blog

JULY 12, 2024

With its intuitive UI, it makes it easy to produce a valid AsyncAPI document for any Kafka cluster or system that adheres to the Apache Kafka protocol. One of the key benefits of event endpoint management is that it allows you to describe events in a standardized way according to the AysncAPI specification.

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ODSC - Open Data Science

JUNE 7, 2023

Wednesday, June 14th Me, my health, and AI: applications in medical diagnostics and prognostics: Sara Khalid | Associate Professor, Senior Research Fellow, Biomedical Data Science and Health Informatics | University of Oxford Iterated and Exponentially Weighted Moving Principal Component Analysis : Dr. Paul A.

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Apache Kafka Machine Learning Machine Learning Data Science 52

IBM Journey to AI blog

NOVEMBER 9, 2023

Apache Kafka is a high-performance, highly scalable event streaming platform. To unlock Kafka’s full potential, you need to carefully consider the design of your application. It’s all too easy to write Kafka applications that perform poorly or eventually hit a scalability brick wall. So, what can you do?

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Apache Kafka Algorithm Clustering 92

ODSC - Open Data Science

JULY 22, 2023

Andre Franca | VP of Research and Development | causaLens Popular virtual sessions: 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 Probabilistic Machine Learning for Finance and Investing: Deepak Kanungo | Founder and CEO, Advisory Board (..)

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Apache Kafka Machine Learning Machine Learning Data Science 52

AWS Machine Learning Blog

OCTOBER 9, 2024

Customers can use the CloudFormation template to bring up an application stack that receives time-series data from an Amazon Managed Streaming for Apache Kafka (Amazon MSK) streaming source and performs near-real-time anomaly detection in the streaming data. How do I delete my Amazon Lookout for Metrics resources? Choose Delete.

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ODSC - Open Data Science

MAY 24, 2023

Streaming Machine Learning Without a Data Lake The combination of data streaming and ML enables you to build one scalable, reliable, but also simple infrastructure for all machine learning tasks using the Apache Kafka ecosystem.

Data Lakes 52

Data Lakes ML ML Analytics 52

ODSC - Open Data Science

JULY 14, 2023

The session participants will learn the theory behind compound sparsity, state-of-the-art techniques, and how to apply it in practice using the Neural Magic platform.

Machine Learning 52

Machine Learning Machine Learning Apache Kafka Data Science 52

Pickl AI

NOVEMBER 4, 2024

Among these tools, Apache Hadoop, Apache Spark, and Apache Kafka stand out for their unique capabilities and widespread usage. Apache Hadoop Hadoop is a powerful framework that enables distributed storage and processing of large data sets across clusters of computers.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

AUGUST 9, 2024

Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers. Data Streaming Learning about real-time data collection methods using tools like Apache Kafka and Amazon Kinesis.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Mlearning.ai

MAY 16, 2023

The events can be published to a message broker such as Apache Kafka or Google Cloud Pub/Sub. Hadoop provides a MapReduce implementation that allows developers to write applications that process large amounts of data in parallel across a cluster of commodity hardware.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

DagsHub

APRIL 7, 2024

Also, while it is not a streaming solution, we can still use it for such a purpose if combined with systems such as Apache Kafka. Cloud-agnostic and can run on any Kubernetes cluster. Integration: It can work alongside other workflow orchestration tools (Airflow cluster or AWS SageMaker Pipelines, etc.)

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Machine Learning Machine Learning ML ML 59

Pickl AI

JULY 30, 2024

Scalability : NiFi can be deployed in a clustered environment, enabling organizations to scale their data processing capabilities as their data needs grow. Integration with Big Data Ecosystems NiFi integrates seamlessly with Big Data technologies such as Apache Hadoop, Apache Kafka, and Apache Spark.

ETL 52

ETL Data Lakes Big Data Big Data 52

AWS Machine Learning Blog

NOVEMBER 3, 2023

m How it’s implemented In our quest to accurately determine shot speed during live matches, we’ve implemented a cutting-edge solution using Amazon Managed Streaming for Apache Kafka (Amazon MSK). Simultaneously, the shot speed data finds its way to a designated topic within our MSK cluster. km/h with a distance to goal of 20.61

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AWS Apache Kafka Data Scientist Data Science 117

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. Additionally, the ball recovery times are sent to a specific topic in the MSK cluster, where they can be accessed by other Bundesliga Match Facts.

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AWS Machine Learning Machine Learning Apache Kafka 88

DagsHub

OCTOBER 23, 2024

Apache Kafka Apache Kafka is a distributed event streaming platform for real-time data pipelines and stream processing. Kafka is highly scalable and ideal for high-throughput and low-latency data pipeline applications. Data Processing Tools These tools are essential for handling large volumes of unstructured data.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

Pickl AI

JULY 20, 2023

Real-time Data Stream Analysis: Use Python with libraries like Apache Kafka and Apache Spark to process and analyze real-time data streams from sources like Twitter, sensors, or website logs. Implement real-time analytics to monitor trends or anomalies in the data.

Analytics 40

Analytics Analytics Big Data Big Data 40

Dataconomy

FEBRUARY 23, 2023

Some of these solutions include: Distributed computing: Distributed computing systems, such as Hadoop and Spark, can help distribute the processing of data across multiple nodes in a cluster. This approach allows for faster and more efficient processing of large volumes of data.

Big Data 91

Big Data Big Data Data Engineering Data Engineer 91

The MLOps Blog

MARCH 15, 2023

Typical examples include: Airbyte Talend Apache Kafka Apache Beam Apache Nifi While getting control over the process is an ideal position an organization wants to be in, the time and effort needed to build such systems are immense and frequently exceeds the license fee of a commercial offering. It connects to many DBs.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

The MLOps Blog

AUGUST 11, 2023

Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., Other areas in ML pipelines: transfer learning, anomaly detection, vector similarity search, clustering, etc. Today different stages exist within ML pipelines built to meet technical, industrial, and business requirements. 1 Data Ingestion (e.g.,

ML 52

ML ML Machine Learning Machine Learning 52

IBM Journey to AI blog

AUGUST 22, 2024

In recognizing the benefits of event-driven architectures, many companies have turned to Apache Kafka for their event streaming needs. Apache Kafka enables scalable, fault-tolerant and real-time processing of streams of data—but how do you manage and properly utilize the sheer amount of data your business ingests every second?

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