AWS Machine Learning Blog

APRIL 19, 2023

The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. With the DJL, integrating this deep learning is simple. Business requirements We are the US squad of the Sportradar AI department. The architecture of DJL is engine agnostic.

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Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Tools and frameworks like Scikit-Learn, TensorFlow, and Keras are often covered.

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Dataconomy

MAY 16, 2023

Key skills and qualifications for machine learning engineers include: Strong programming skills: Proficiency in programming languages such as Python, R, or Java is essential for implementing machine learning algorithms and building data pipelines.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

Learning means identifying and capturing historical patterns from the data, and inference means mapping a current value to the historical pattern. The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

Zeta’s AI innovations over the past few years span 30 pending and issued patents, primarily related to the application of deep learning and generative AI to marketing technology. It simplifies feature access for model training and inference, significantly reducing the time and complexity involved in managing data pipelines.

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Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

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AWS Machine Learning Blog

OCTOBER 23, 2024

He focuses on Deep learning including NLP and Computer Vision domains. Since joining SnapLogic in 2010, Greg has helped design and implement several key platform features including cluster processing, big data processing, the cloud architecture, and machine learning.

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The MLOps Blog

MARCH 28, 2024

TL;DR GPUs can greatly accelerate deep learning model training, as they are specialized for performing the tensor operations at the heart of neural networks. We’ll explore how factors like batch size, framework selection, and the design of your data pipeline can profoundly impact the efficient utilization of GPUs.

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Heartbeat

JANUARY 5, 2024

Introduction to LangChain for Including AI from Large Language Models (LLMs) Inside Data Applications and Data Pipelines This article will provide an overview of LangChain, the problems it addresses, its use cases, and some of its limitations. Python : Great for including AI in Python-based software or data pipelines.

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DagsHub

DECEMBER 23, 2024

Balanced Dataset Creation Balanced Dataset Creation refers to active learning's ability to select samples that ensure proper representation across different classes and scenarios, especially in cases of imbalanced data distribution. Pool-Based Active Learning Scenario : Classifying images of artwork styles for a digital archive.  

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Iguazio

JANUARY 16, 2024

The system’s architecture ensures the data flows through the different systems effectively. First, the data lake is fed from a number of data sources. These include conversational data, ATS Data and more. Sense onboarded Iguazio as an MLOps solution for the ML training and serving component of the pipeline.

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ODSC - Open Data Science

JANUARY 7, 2025

Machine Learning As machine learning is one of the most notable disciplines under data science, most employers are looking to build a team to work on ML fundamentals like algorithms, automation, and so on. Deep Learning Deep learning is a cornerstone of modern AI, and its applications are expanding rapidly.

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Iguazio

JANUARY 16, 2024

The system’s architecture ensures the data flows through the different systems effectively. First, the data lake is fed from a number of data sources. These include conversational data, ATS data, and more. Sense onboarded Iguazio as an MLOps platform for the ML training and serving component of the pipeline.

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Pickl AI

JULY 18, 2023

Solution Design Creating a high-level architectural design that encompasses data pipelines, model training, deployment strategies, and integration with existing systems. Learn Machine Learning and Deep Learning Deepen your understanding of machine learning algorithms, statistical modelling, and deep learning architectures.

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DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date.

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DagsHub

APRIL 21, 2024

By understanding the role of each tool within the MLOps ecosystem, you'll be better equipped to design and deploy robust ML pipelines that drive business impact and foster innovation. TensorFlow TensorFlow is a popular machine learning framework developed by Google that offers the implementation of a wide range of neural network models.

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Pickl AI

MARCH 7, 2024

Applications: It is extensively used for statistical analysis, data visualisation, and machine learning tasks such as regression, classification, and clustering. PyTorch Functionality: PyTorch is an open-source machine learning library for Python developed by Facebook’s AI research group.

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The MLOps Blog

MARCH 21, 2023

Orchestrators are concerned with lower-level abstractions like machines, instances, clusters, service-level grouping, replication, and so on. Along with the schedulers, they are integral to managing the regular workflows your data scientists run and how the tasks in those workflows communicate with the ML platform. Allegro.io

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The MLOps Blog

MARCH 12, 2024

Data and workflow orchestration: Ensuring efficient data pipeline management and scalable workflows for LLM performance. Related Deep Learning Model Optimization Methods Read more Example Scenario: Deploying customer service chatbot Imagine that you are in charge of implementing a LLM-powered chatbot for customer support.

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The MLOps Blog

AUGUST 11, 2023

Internally within Netflix’s engineering team, Meson was built to manage, orchestrate, schedule, and execute workflows within ML/Data pipelines. Meson managed the lifecycle of ML pipelines, providing functionality such as recommendations and content analysis, and leveraged the Single Leader Architecture.

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