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Home » Collections for John Patrick Laurel

About John Patrick Laurel (Edit profile)

Pats is the Head of Data Science at a European short-stay real estate business group. He boasts a diverse skill set in the realm of data and AI, encompassing Machine Learning Engineering, Data Engineering, and Analytics. Additionally, he serves as a Data Science Mentor at Eskwelabs. Outside of work, he enjoys taking long walks and reading.

Distributed Data Parallel Training with TensorFlow and Amazon SageMaker Distributed Training Library

2024-01-22T00:58:08+00:00

Introduction In the realm of machine learning, the ability to train models effectively and efficiently stands as a cornerstone of success. As datasets grow exponentially and models become more complex, traditional single-node training methods increasingly fall short. This is where distributed training enters the picture, offering a scalable solution to this growing challenge. Distributed Training Overview Distributed training is a technique used to train machine learning models on large datasets more efficiently. By splitting the workload across multiple compute nodes, it significantly reduces training time. There are two main strategies in distributed training: data parallelism, where the dataset is partitioned [...]

Distributed Data Parallel Training with TensorFlow and Amazon SageMaker Distributed Training Library2024-01-22T00:58:08+00:00

Securing Machine Learning Pipelines: Best Practices in Amazon SageMaker

2024-01-17T00:45:41+00:00

Introduction In today's digital era, the importance of security in machine learning (ML) pipelines cannot be overstated. As ML systems increasingly become integral to business operations and decision-making, ensuring the integrity and security of these systems is paramount. A breach or a flaw in an ML pipeline can lead to compromised data, erroneous decision-making, and potentially catastrophic consequences for businesses and individuals alike. This section will delve into why securing ML pipelines is crucial, highlighting the potential risks and impacts of security lapses. Short Introduction to Amazon SageMaker Amazon SageMaker is a fully managed service that provides every developer and [...]

Securing Machine Learning Pipelines: Best Practices in Amazon SageMaker2024-01-17T00:45:41+00:00

How I Prepared for the AWS Cloud Practitioner CLF-C02 Exam as a Data Scientist

2023-12-03T18:07:52+00:00

In the ever-evolving landscape of technology, the pursuit of knowledge and skills is not just a choice but a necessity, especially for professionals like data scientists. This realization led me to embark on a journey towards achieving the AWS Certified Cloud Practitioner CLF-C02 certification, a decision that not only expanded my technical horizons but also underscored the importance of continuous learning in the tech industry. As a data scientist, my daily encounters with vast datasets and complex algorithms had already established a solid foundation in analytics and machine learning. However, with the increasing integration of cloud technologies in data science, [...]

How I Prepared for the AWS Cloud Practitioner CLF-C02 Exam as a Data Scientist2023-12-03T18:07:52+00:00

Amazon AI Fairness and Explainability with Amazon SageMaker Clarify

2023-12-02T01:24:03+00:00

Introduction In the rapidly evolving domain of machine learning, ensuring fairness and explainability in model predictions has become crucial. With Amazon SageMaker Clarify, these critical aspects are not just an afterthought but integral components of the model development and deployment process. This article delves into the world of SageMaker Clarify, offering a comprehensive guide to its capabilities and practical applications. We commence our journey with a high-level understanding of what SageMaker Clarify is and its importance in the day-to-day tasks of machine learning modeling. Our exploration is anchored in a hands-on example, utilizing a specially crafted dataset that simulates loan [...]

Amazon AI Fairness and Explainability with Amazon SageMaker Clarify2023-12-02T01:24:03+00:00

Automating Binary Classification Model Building with Amazon SageMaker Autopilot

2023-11-30T10:07:06+00:00

Introduction In the ever-evolving world of machine learning, binary classification stands out as one of the most fundamental and widely used techniques. At its core, binary classification involves categorizing data into one of two groups based on certain features. This method is crucial in various applications, such as spam detection, medical diagnosis, and customer churn prediction. However, building an effective binary classification model can be a complex and time-consuming process, requiring extensive knowledge in data preprocessing, feature engineering, model selection, and optimization. Enter Amazon SageMaker Autopilot – a powerful service designed to automate the process of building, training, and tuning [...]

Automating Binary Classification Model Building with Amazon SageMaker Autopilot2023-11-30T10:07:06+00:00

A Compact Guide to Building Your First DAG with Amazon Managed Workflows for Apache Airflow

2023-11-30T05:46:03+00:00

In the vast realm of data processing, orchestrating workflows becomes crucial to ensure tasks run efficiently and reliably. Apache Airflow has revolutionized this aspect, providing a flexible platform to define, schedule, and monitor workflows. Combining this with Amazon's managed service, we can supercharge our workflow setup without the overhead of manual maintenance. In this guide, we dive deep into constructing your first Directed Acyclic Graph (DAG) using Apache Airflow, elucidating each component and step involved. Directed Acyclic Graph (DAG) At its core, a DAG is a collection of vertices and edges, where each edge has a direction, and there are [...]

A Compact Guide to Building Your First DAG with Amazon Managed Workflows for Apache Airflow2023-11-30T05:46:03+00:00

Serverless Model Deployment in AWS: Streamlining with Lambda, Docker, and S3

2023-11-30T06:08:01+00:00

Welcome back to our series on model deployment in AWS! In the fast-paced world of machine learning and data science, the ability to deploy models efficiently and reliably is crucial. This is where AWS services, with their vast array of tools and capabilities, come into play. In this second installment, we will delve into the potent combination of AWS Lambda and Docker, coupled with the convenience of storing models in S3. This trio offers a scalable, cost-effective, and streamlined solution for deploying machine learning models in a production environment. If you recall, in the first part of our series, we [...]

Serverless Model Deployment in AWS: Streamlining with Lambda, Docker, and S32023-11-30T06:08:01+00:00

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