Java Machine Learning Scala 
Netflix Tech
FEBRUARY 13, 2023
We have been leveraging machine learning (ML) models to personalize artwork and to help our creatives create promotional content efficiently. Case study: scaling match cutting using the media ML infra The Media Machine Learning Infrastructure is empowering various scenarios across Netflix, and some of them are described here.
Knowledge Hut
JULY 7, 2023
For over 2 decades, Java has been the mainstay of app development. Another reason for its popularity is its cross-platform and cross-browser compatibility, making applications written in Java highly portable. These very qualities gave rise to the need for reusability of code, version control, and other tools for Java developers.
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Confluent
JULY 16, 2019
For general information about how to build scalable and reliable machine learning infrastructures with Apache Kafka ecosystem, check out the article Using Apache Kafka to Drive Cutting Edge Machine Learning. I will show how to implement this use case in this blog post. Setting up your burglar alarm.
Data Engineering Podcast
AUGUST 13, 2022
In this episode founder Shayan Mohanty explains how he and his team are bringing software best practices and automation to the world of machine learning data preparation and how it allows data engineers to be involved in the process. The Machine Learning Podcast helps you go from idea to production with machine learning.
Data Engineering Podcast
AUGUST 6, 2022
For machine learning applications relational models require additional processing to be directly useful, which is why there has been a growth in the use of vector databases. The Machine Learning Podcast helps you go from idea to production with machine learning.
KDnuggets
AUGUST 13, 2019
Apache Spark is one of the hottest and largest open source project in data processing framework with rich high-level APIs for the programming languages like Scala, Python, Java and R. It realizes the potential of bringing together both Big Data and machine learning.
Monte Carlo
OCTOBER 31, 2024
AI data engineers tend to focus primarily on AI, generative AI (GenAI), and machine learning (ML)-specific needs, like handling unstructured data and supporting real-time analytics. Let’s dive into the tools necessary to become an AI data engineer. These frameworks are used to bring AI models into production and to conduct research.
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