Smart Data Collective

SEPTEMBER 23, 2020

Type of Data: structured and unstructured from different sources of data Purpose: Cost-efficient big data storage Users: Engineers and scientists Tasks: storing data as well as big data analytics, such as real-time analytics and deep learning Sizes: Store data which might be utilized.

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Heartbeat

NOVEMBER 6, 2023

Image Source —  Pixel Production Inc In the previous article, you were introduced to the intricacies of data pipelines, including the two major types of existing data pipelines. You might be curious how a simple tool like Apache Airflow can be powerful for managing complex data pipelines.

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Flipboard

FEBRUARY 13, 2023

Project Structure Creating Our Configuration File Creating Our Data Pipeline Preprocessing Faces: Detection and Cropping Summary Citation Information Building a Dataset for Triplet Loss with Keras and TensorFlow In today’s tutorial, we will take the first step toward building our real-time face recognition application. The dataset.py

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Flipboard

MARCH 6, 2023

In the previous tutorial of this series, we built the dataset and data pipeline for our Siamese Network based Face Recognition application. Specifically, we looked at an overview of triplet loss and discussed what kind of data samples are required to train our model with the triplet loss. That’s not the case.

Deep Learning 144

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How to Learn Machine Learning

DECEMBER 24, 2024

The platform handles: Automatic model deployment Load balancing High availability Security compliance Model monitoring and updating Integration with the Broader AWS Machine Learning Ecosystem One often-overlooked advantage is the seamless integration with other AWS services. medium notebook instances for development 50 hours per month of m4.xlarge

Machine Learning 52

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Smart Data Collective

JUNE 10, 2022

In this role, you would perform batch processing or real-time processing on data that has been collected and stored. As a data engineer, you could also build and maintain data pipelines that create an interconnected data ecosystem that makes information available to data scientists.

Data Science 144

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PyImageSearch

MARCH 20, 2023

Jump Right To The Downloads Section Training and Making Predictions with Siamese Networks and Triplet Loss In the second part of this series, we developed the modules required to build the data pipeline for our face recognition application. Figure 1: Overview of our Face Recognition Pipeline (source: image by the author).

Deep Learning 106

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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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Applied Data Science

AUGUST 2, 2021

Machine learning The 6 key trends you need to know in 2021 ? Automation Automating data pipelines and models ➡️ 6. First, let’s explore the key attributes of each role: The Data Scientist Data scientists have a wealth of practical expertise building AI systems for a range of applications.

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Analytics Vidhya

SEPTEMBER 16, 2022

Introduction Data science is a practical subject that the experts can best explain in the field. These sessions will enhance your domain knowledge and help you learn new […].

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The MLOps Blog

JUNE 27, 2023

Monte Carlo Monte Carlo is a popular data observability platform that provides real-time monitoring and alerting for data quality issues. It could help you detect and prevent data pipeline failures, data drift, and anomalies. Metaplane supports collaboration, anomaly detection, and data quality rule management.

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Data Science Blog

FEBRUARY 4, 2023

If the data sources are additionally expanded to include the machines of production and logistics, much more in-depth analyses for error detection and prevention as well as for optimizing the factory in its dynamic environment become possible.

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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. He works to enable the development, training, and production inference of deep learning.

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Analytics Vidhya

FEBRUARY 17, 2023

Introduction Are you curious about the latest advancements in the data tech industry? Perhaps you’re hoping to advance your career or transition into this field. In that case, we invite you to check out DataHour, a series of webinars led by experts in the field.

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AWS Machine Learning Blog

FEBRUARY 24, 2023

Solution overview In brief, the solution involved building three pipelines: Data pipeline – Extracts the metadata of the images Machine learning pipeline – Classifies and labels images Human-in-the-loop review pipeline – Uses a human team to review results The following diagram illustrates the solution architecture.

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AWS Machine Learning Blog

MARCH 1, 2023

To solve this problem, we had to design a strong data pipeline to create the ML features from the raw data and MLOps. Multiple data sources ODIN is an MMORPG where the game players interact with each other, and there are various events such as level-up, item purchase, and gold (game money) hunting.

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Flipboard

NOVEMBER 24, 2023

Data scientists and ML engineers require capable tooling and sufficient compute for their work. Therefore, BMW established a centralized ML/deep learning infrastructure on premises several years ago and continuously upgraded it.

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ODSC - Open Data Science

FEBRUARY 25, 2025

AI Engineering TrackBuild Scalable AISystems Learn how to bridge the gap between AI development and software engineering. This track will focus on AI workflow orchestration, efficient data pipelines, and deploying robust AI solutions. This track provides practical guidance on building and optimizing deep learningsystems.

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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.

Data Scientist 156

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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.

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AWS Machine Learning Blog

FEBRUARY 23, 2023

SageMaker has developed the distributed data parallel library , which splits data per node and optimizes the communication between the nodes. You can use the SageMaker Python SDK to trigger a job with data parallelism with minimal modifications to the training script.

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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.

Deep Learning 52

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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.

Data Engineer 52

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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.

Data Science 150

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ODSC - Open Data Science

MAY 10, 2023

Many mistakenly equate tabular data with business intelligence rather than AI, leading to a dismissive attitude toward its sophistication. Standard data science practices could also be contributing to this issue. Feature engineering activities frequently focus on single-table data transformations, leading to the infamous “yawn factor.”

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PyImageSearch

JANUARY 8, 2024

Project Structure Creating Adversarial Examples Robustness Toward Adversarial Examples Summary Citation Information Adversarial Learning with Keras and TensorFlow (Part 1): Overview of Adversarial Learning In this tutorial, you will learn about adversarial examples and how they affect the reliability of neural network-based computer vision systems.

Deep Learning 63

Deep Learning Deep Learning Data Pipeline Computer Science 63

DrivenData Labs

MAY 21, 2024

The second is to provide a directed acyclic graph (DAG) for data pipelining and model building. If you use the filesystem as an intermediate data store, you can easily DAG-ify your data cleaning, feature extraction, model training, and evaluation.

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PyImageSearch

FEBRUARY 5, 2024

Furthermore, we also import the keras library ( Line 6 ), tensorflow library ( Line 7 ), numpy ( Line 8 ), and os module ( Line 9 ) for various deep learning or matrix or manipulation functionalities, as always. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated?

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ODSC - Open Data Science

OCTOBER 7, 2024

Keeping track of changes in data, model parameters, and infrastructure configurations is essential for reliable AI development, ensuring models can be rebuilt and improved efficiently. Building Scalable Data Pipelines The foundation of any AI pipeline is the data it consumes.

Machine Learning 52

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Pickl AI

NOVEMBER 29, 2024

Use Cases in ML Workflows Hydra excels in scenarios requiring frequent parameter tuning, such as hyperparameter optimisation, multi-environment testing, and orchestrating pipelines. It also simplifies managing configuration dependencies in Deep Learning projects and large-scale data pipelines.

Machine Learning 52

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Dataconomy

AUGUST 22, 2024

I led several projects that dramatically advanced the company’s technological capabilities: Real-time Video Analytics for Security: We developed an advanced system integrating deep learning algorithms with existing CCTV infrastructure.

ML 91

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PyImageSearch

JUNE 5, 2023

Specifically, we will develop our data pipeline, implement the loss functions discussed in Part 1 and write our own code to train the CycleGAN model end-to-end using Keras and TensorFlow. Finally, we combine and consolidate our entire training data (i.e., Let us open the train.py file and get started. That’s not the case.

Deep Learning 56

Deep Learning Deep Learning Data Pipeline Python 56

Iguazio

JANUARY 22, 2024

Definitions: Foundation Models, Gen AI, and LLMs Before diving into the practice of productizing LLMs, let’s review the basic definitions of GenAI elements: Foundation Models (FMs) - Large deep learning models that are pre-trained with attention mechanisms on massive datasets. This helps cleanse the data.

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IBM Journey to AI blog

SEPTEMBER 3, 2024

Beyond architecture, engineers are finding value in other methods, such as quantization , chips designed specifically for inference, and fine-tuning , a deep learning technique that involves adapting a pretrained model for specific use cases. Hybrid architectures that use multiple types of models are also gaining traction.

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Flipboard

AUGUST 17, 2023

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development. Above all, this solution offers you a native Spark way to implement an end-to-end data pipeline from Amazon Redshift to SageMaker.

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Snorkel AI

JULY 3, 2023

As the algorithms we use have gotten more robust and we have increased our compute power through new technologies, we haven’t made nearly as much progress on the data part of our jobs. Because of this, I’m always looking for ways to automate and improve our data pipelines. So why should we use data pipelines?

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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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