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.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

In this post, we demonstrate how to build a robust real-time anomaly detection solution for streaming time series data using Amazon Managed Service for Apache Flink and other AWS managed services. This solution employs machine learning (ML) for anomaly detection, and doesn’t require users to have prior AI expertise.

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AWS Machine Learning Blog

JANUARY 6, 2023

The key requirement for TR’s new machine learning (ML)-based personalization engine was centered around an accurate recommendation system that takes into account recent customer trends. ML training pipeline. TR customer data is changing at a faster rate than the business rules can evolve to reflect changing customer needs.

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Precisely

DECEMBER 28, 2023

More than ever, advanced analytics, ML, and AI are providing the foundation for innovation, efficiency, and profitability. One very popular platform is Apache Kafka , a powerful open-source tool used by thousands of companies. But in all likelihood, Kafka doesn’t natively connect with the applications that contain your data.

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IBM Journey to AI blog

NOVEMBER 29, 2023

Aggregates as predictive insights : Aggregates, which consolidate data from various sources across your business environment, can serve as valuable predictors for machine learning (ML) algorithms. Event processing helps continuously update and refine our understanding of ongoing business scenarios.

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Flipboard

NOVEMBER 21, 2023

For this particular use case, you can use streaming ingestion with Amazon SageMaker Feature Store and Amazon Managed Streaming for Apache Kafka, MSK, to make machine learning-backed decisions in near real-time. Shun Mao is a Senior AI/ML Partner Solutions Architect in the Emerging Technologies team at Amazon Web Services.

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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). His skills and areas of expertise include application development, data science, and machine learning (ML).

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ODSC - Open Data Science

JULY 14, 2023

Probabilistic Machine Learning for Finance and Investing Deepak Kanungo | Founder and CEO, Advisory Board Member | Hedged Capital LLC, AIKON This session will introduce you to the reasons why probabilistic machine learning is the next generation of AI in finance and investing.

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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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ODSC - Open Data Science

JULY 22, 2023

Some of our most popular in-person sessions were: Data Communication in the Age of AI: Alan Rutter | Founder | Fire Plus Algebra Autoencoders — a Magical Approach to Unsupervised Machine Learning: Oliver Zeigermann | Blue Collar ML Architect | Freelancer Fast Option Pricing Using Deep Learning Methods: Chakri Cherukuri | Senior Quantitative Researcher (..)

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Alation

AUGUST 4, 2022

Spark, Tensorflow, Apache Kafka, et cetera, are all out found in cloud databases,” points out Jones. We also need to “learn about both better AI/ML /analysis tools and understanding the implicit and explicit biases that exist within them.” To improve ML and Ethics, data literacy training is critical.

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DagsHub

APRIL 7, 2024

These pipelines cover the entire lifecycle of an ML project, from data ingestion and preprocessing, to model training, evaluation, and deployment. Adopted from [link] In this article, we will first briefly explain what ML workflows and pipelines are. around the world to streamline their data and ML pipelines.

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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. Fotinos Kyriakides is an ML Engineer with AWS Professional Services. He is also an enthusiastic cyclist, taking long bike-packing trips.

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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.

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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.

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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!

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ODSC - Open Data Science

OCTOBER 9, 2024

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

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Mlearning.ai

AUGUST 4, 2023

Apache Kafka and R abbitMQ are particularly popular in LEs. Graph 7: Percentage of Programming Languages MiscTech Tools In Both LEs and SMEs: ‘. NET (5+) ’, ‘ pandas ’, ‘ numpy ’, and ‘. NET Framework (1.0–4.8)’ 4.8)’ are widely used.

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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?

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Pickl AI

JULY 8, 2024

Must Read Blogs: Elevate Your Data Quality: Unleashing the Power of AI and ML for Scaling Operations. Utilise in-memory data processing tools like Apache Kafka and Apache Flink, which provide low-latency data ingestion and processing capabilities. The Difference Between Data Observability And Data Quality.

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DagsHub

JANUARY 14, 2025

A massive amount of diverse data powers today's ML models. Each of the above-mentioned techniques has its strengths and weaknesses, and the choice of method often depends on the specific requirements of the dataset, ML model, and the available computational resources. Duplicate images are typically found  in the same cluster.

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Mlearning.ai

MAY 16, 2023

The events can be published to a message broker such as Apache Kafka or Google Cloud Pub/Sub. The company can use the Pub/Sub pattern to process customer events such as product views, add to cart, and checkout. The events can be published by various sources such as mobile apps, web applications, and IoT devices.

Data Engineering 52

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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?

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ODSC - Open Data Science

MAY 24, 2023

Building a Business with a Real-Time Analytics Stack, Streaming ML Without a Data Lake, and Google’s PaLM 2 Building a Pizza Delivery Service with a Real-Time Analytics Stack The best businesses react quickly and with informed decisions. Here’s a use case of how you can use a real-time analytics stack to build a pizza delivery service.

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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).

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phData

SEPTEMBER 27, 2024

Technologies like Apache Kafka, often used in modern CDPs, use log-based approaches to stream customer events between systems in real-time. Activity Schema Processing : To capture and process customer activities, you might use a stream processing technology like Apache Kafka or Apache Flink.

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