ODSC - Open Data Science

FEBRUARY 17, 2023

Developing NLP tools isn’t so straightforward, and requires a lot of background knowledge in machine & deep learning, among others. In a change from last year, there’s also a higher demand for those with data analysis skills as well. Having mastery of these two will prove that you know data science and in turn, NLP.

Deep Learning 111

Deep Learning Deep Learning Data Science Natural Language Processing 111

AWS Machine Learning Blog

FEBRUARY 24, 2023

AWS recently released Amazon SageMaker geospatial capabilities to provide you with satellite imagery and geospatial state-of-the-art machine learning (ML) models, reducing barriers for these types of use cases. In the following sections, we dive into each pipeline in more detail.

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AWS Machine Learning Blog

MARCH 8, 2023

In order to train a model using data stored outside of the three supported storage services, the data first needs to be ingested into one of these services (typically Amazon S3). This requires building a data pipeline (using tools such as Amazon SageMaker Data Wrangler ) to move data into Amazon S3.

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ML ML AWS Python 136

AWS Machine Learning Blog

SEPTEMBER 11, 2024

Examples of other PBAs now available include AWS Inferentia and AWS Trainium , Google TPU, and Graphcore IPU. Around this time, industry observers reported NVIDIA’s strategy pivoting from its traditional gaming and graphics focus to moving into scientific computing and data analytics.

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Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. To do this, we provide an AWS CloudFormation template to create a stack that contains the resources.

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ML ML AWS Data Warehouse 123

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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AWS Machine Learning Blog

SEPTEMBER 24, 2024

Working with the AWS Generative AI Innovation Center , DoorDash built a solution to provide Dashers with a low-latency self-service voice experience to answer frequently asked questions, reducing the need for live agent assistance, in just 2 months. “We You can deploy the solution in your own AWS account and try the example solution.

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AWS Machine Learning Blog

OCTOBER 7, 2024

By using the natural language processing and generation capabilities of generative AI, the chat assistant can understand user queries, retrieve relevant information from various data sources, and provide tailored, contextual responses. See Data source connectors for a list of supported data source connectors for Amazon Kendra.

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AWS AI AI Natural Language Processing 107

Towards AI

MAY 30, 2024

Meme shared by bin4ry_d3struct0r TAI Curated section Article of the week Graph Neural Networks (GNN) — Concepts and Applications by Tan Pengshi Alvin Graph Neural Networks (GNN) are a very interesting application in deep learning and have strong potential for important use cases, albeit a less well-known and more niche domain.

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AI AI Data Pipeline Deep Learning 80

The MLOps Blog

JUNE 27, 2023

For example, if you use AWS, you may prefer Amazon SageMaker as an MLOps platform that integrates with other AWS services. SageMaker Studio offers built-in algorithms, automated model tuning, and seamless integration with AWS services, making it a powerful platform for developing and deploying machine learning solutions at scale.

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Machine Learning Machine Learning ML ML 134

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.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

ODSC - Open Data Science

FEBRUARY 2, 2023

As you’ll see in the next section, data scientists will be expected to know at least one programming language, with Python, R, and SQL being the leaders. This will lead to algorithm development for any machine or deep learning processes. Saturn Cloud is picking up a lot of momentum lately too thanks to its scalability.

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Data Science Data Scientist Computer Science Computer Science 98

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.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

DrivenData Labs

MAY 21, 2024

Some projects manage this folder like the data folder and sync it to a canonical store (e.g., AWS S3) separately from source code. The second is to provide a directed acyclic graph (DAG) for data pipelining and model building. Some projects opt to remove it and use a separate experiment tracking tool.

Data Science 233

Data Science Python Data Scientist Data Warehouse 233

AWS Machine Learning Blog

JANUARY 25, 2023

Solution overview SageMaker algorithms have fixed input and output data formats. But customers often require specific formats that are compatible with their data pipelines. Option A In this option, we use the inference pipeline feature of SageMaker hosting. Dhawal Patel is a Principal Machine Learning Architect at AWS.

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Algorithm Natural Language Processing Machine Learning Machine Learning 98

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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ML ML DataOps Data Scientist 52

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.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

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.

ML 52

ML ML DataOps Data Scientist 52

phData

OCTOBER 11, 2024

To help, phData designed and implemented AI-powered data pipelines built on the Snowflake AI Data Cloud , Fivetran, and Azure to automate invoice processing. phData’s Approach phData implemented Optical Character Recognition (OCR), which was performed using the open-source tool Paddle (Parallel Distributed Deep Learning).

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AI AI Data Pipeline ML 52

Mlearning.ai

JULY 11, 2023

Before diving into the world of data science, it is essential to familiarize yourself with certain key aspects. The process or lifecycle of machine learning and deep learning tends to follow a similar pattern in most companies.

Data Science 40

Data Science Exploratory Data Analysis Azure Power BI 40

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.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Mlearning.ai

JUNE 16, 2023

It also can minimize the risks of miscommunication in the process since the analyst and customer can align on the prototype before proceeding to the build phase Design: DALL-E, another deep learning model developed by OpenAI to generate digital images from natural language descriptions, can contribute to the design of applications.

AI 52

AI AI Data Analysis Data Analysis 52

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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Machine Learning Machine Learning ML ML 59

The MLOps Blog

MARCH 21, 2023

You don’t need a bigger boat : The repository curated by Jacopo Tagliabue shows how several (mostly open-source) tools can be effectively combined together to run data pipelines at scale with very small teams. Name Short Description Algorithmia Securely govern your machine learning operations with a healthy ML lifecycle.

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Machine Learning Machine Learning Data Scientist ML 69

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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Database Machine Learning Machine Learning AI 52

AWS Machine Learning Blog

MARCH 15, 2024

This approach can help heart stroke patients, doctors, and researchers with faster diagnosis, enriched decision-making, and more informed, inclusive research work on stroke-related health issues, using a cloud-native approach with AWS services for lightweight lift and straightforward adoption. Stroke victims can lose around 1.9

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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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ML ML Machine Learning Machine Learning 52

AWS Machine Learning Blog

OCTOBER 24, 2024

Large language models (LLMs) are very large deep-learning models that are pre-trained on vast amounts of data. Data pipelines must seamlessly integrate new data at scale. Diverse data amplifies the need for customizable cleaning and transformation logic to handle the quirks of different sources.

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