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Sofian Hamiti
Sofian Hamiti

286 Followers

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Published in Towards Data Science

·Dec 16, 2022

Boost Your ML Team’s Productivity with Container-Based Development in the Cloud

A simple approach to run VS Code in a container, on SageMaker — Earlier this year, I published 2 guides (here, and here) to hosting code-server on SageMaker. It shows how you can run VS Code on scalable cloud compute, and code from anywhere without worrying about local setup. All you need is an internet connection. In this post, we will go a…

Docker

5 min read

Boost Your ML Team’s Productivity with Container-Based Development in the Cloud
Boost Your ML Team’s Productivity with Container-Based Development in the Cloud
Docker

5 min read


Published in Towards Data Science

·Sep 21, 2022

5 Simple Steps to MLOps with GitHub Actions, MLflow, and SageMaker Pipelines

Kick-start your path to production with a project template — Earlier this year, I published a step-by-step guide to automating an end-to-end ML lifecycle with built-in SageMaker MLOps project templates and MLflow. It brought workflow orchestration, model registry, and CI/CD under one umbrella to reduce the effort of running end-to-end MLOps projects. In this post, we will go a step…

Machine Learning

11 min read

5 Simple Steps to MLOps with GitHub Actions, MLflow, and SageMaker Pipelines
5 Simple Steps to MLOps with GitHub Actions, MLflow, and SageMaker Pipelines
Machine Learning

11 min read


Published in Geek Culture

·May 19, 2022

Labeling data with Label Studio on SageMaker

Step-by-step guide to deploying Label Studio on a Notebook Instance — Joint post with Phil Meakins. Few years ago in my data scientist days, I worked on a computer vision project in which my team spent weeks tweaking NN layers and hyperparameters in order to get better performance. It turned out that simply improving the quality of dataset labels was the…

Machine Learning

5 min read

Labeling data with Label Studio on SageMaker
Labeling data with Label Studio on SageMaker
Machine Learning

5 min read


Published in Towards Data Science

·May 2, 2022

Hosting VS Code on SageMaker

Step-by-step guide to set it up in your environment — Joint post with Prayag Singh and Phil Meakins. ML teams need the flexibility to choose notebooks or full-fledge IDE when working on a project. They may even use multiple IDEs in the same project. It’s a bit like climbing a mountain with the appropriate equipment. …

AWS

6 min read

Hosting VS Code on SageMaker
Hosting VS Code on SageMaker
AWS

6 min read


Published in CodeX

·Mar 30, 2022

VS Code in Studio Lab

Setup VS Code in 5 mins in your environment — Amazon SageMaker Studio Lab gives you free access to AWS compute resources to quickly start learning and experimenting with ML. It is simple to setup and allows you to run notebooks on CPU or GPU instances. Studio Lab is based on JupyterLab but you can also run VS Code in…

AWS

3 min read

VS Code in SageMaker Studio Lab
VS Code in SageMaker Studio Lab
AWS

3 min read


Published in Towards Data Science

·Mar 27, 2022

Scaling MLOps with resilient pipelines

Making SageMaker Pipelines more resilient with retry policies — Yesterday I used SageMaker Pipelines to automate the workflow of a forecasting project. I launched 3 concurrent pipeline executions to train the model on different time horizons. Think predicting at 1 day, 1 week, 1 month. After a while, 2 executions failed. …

AWS

4 min read

Scaling MLOps with resilient pipelines
Scaling MLOps with resilient pipelines
AWS

4 min read


Published in Towards Data Science

·Nov 18, 2021

Scaling Enterprise ML Platforms with Modern Cloud Operations

Step-by-step guide to scaled ML environment provisioning on AWS — Joint post with Nivas Durairaj The Gutenberg printing press was revolutionary in its time. Suddenly, publishers could print thousands of book pages per day when compared to a few handwritten pages. It enabled a rapid dissemination of knowledge in Europe and opened the era of Renaissance. Today, large enterprises need…

AWS

7 min read

Scaling Enterprise MLOps with Modern Cloud Operations
Scaling Enterprise MLOps with Modern Cloud Operations
AWS

7 min read


Published in Towards Data Science

·Oct 12, 2021

Industrializing an ML platform with Amazon SageMaker Studio

Steps and considerations when rolling out Studio in an enterprise — Often in large enterprises, ML platform admins need to balance governance and compliance requirements with the need for ML teams to quickly access working environments, and scaffolding to operationalize their solutions. In SageMaker terms, this translates into accessing secure, well-governed working environments with Studio, and provisioning templated MLOps projects with…

AWS

9 min read

Industrializing an ML platform with Amazon SageMaker Studio
Industrializing an ML platform with Amazon SageMaker Studio
AWS

9 min read


Published in Towards Data Science

·Jul 25, 2021

MLOps with MLFlow and Amazon SageMaker Pipelines

Step-by-step guide to using MLflow with SageMaker projects — Earlier this year, I published a step-by-step guide to deploying MLflow on AWS Fargate, and using it with Amazon SageMaker. This can help streamline the experimental phase of an ML project. In this post, we will go a step further and automate an end-to-end ML lifecycle using MLflow and Amazon…

AWS

6 min read

MLOps with MLFlow and Amazon SageMaker Pipelines
MLOps with MLFlow and Amazon SageMaker Pipelines
AWS

6 min read


Published in Towards Data Science

·Jun 13, 2021

Enabling self-service provisioning of Amazon SageMaker Studio resources

Step-by-step guide to SageMaker Studio management with the AWS Service Catalog Factory — In most large enterprises, standardizing, provisioning, and ensuring governance of ML environments is the responsibility of central IT teams. I have recently published a guide to continuous delivery of custom images IT teams can use when setting up SageMaker Studio for their end-users. In this post, we will go a…

AWS

5 min read

Enabling self-service provisioning of Amazon SageMaker Studio resources
Enabling self-service provisioning of Amazon SageMaker Studio resources
AWS

5 min read

Sofian Hamiti

Sofian Hamiti

286 Followers

ML@AWS. Let’s connect! linkedin.com/in/sofianhamiti

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