@ramikrispin@mstdn.social
@ramikrispin@mstdn.social avatar

ramikrispin

@ramikrispin@mstdn.social

Data science and engineering senior manager at  | #rstats & #Python | 📦 dev | ❤️ time-series analysis & forecasting | Author. Opinions are my own | https://linktr.ee/ramikrispin

This profile is from a federated server and may be incomplete. Browse more on the original instance.

ramikrispin, to datascience
@ramikrispin@mstdn.social avatar

In the past few months, I created a bunch of Docker 🐳 tutorials covering random topics, from a fun setting for a Python 🐍 environment on the CLI to advanced topics such as multi-stage builds 🏗️. I organized all the tutorials under one folder, and I plan to keep updating this folder with future-related ones 😎.

Currently on my Docker tutorial TODO list:
➡️ Docker ENTRYPOINT vs CMD
➡️ Docker multi-architecture build

🔗 https://medium.com/@rami.krispin/list/docker-21408ce79e6a

Enjoy!

ramikrispin, to rust
@ramikrispin@mstdn.social avatar

I recently started to learn Rust programming, and I made a list of resources that I found useful to get started with the language 👇🏼

https://medium.com/p/928bf7b8418f

ramikrispin, to llm
@ramikrispin@mstdn.social avatar

(1/2) Prompt Fuzzer - a new open-source project for LLM security 👇🏼

Prompt Fuzzer is a new open-source project that provides a set of functions for assessing the security of GenAI applications. This CLI-based tool enables you to run and test your system prompts to identify security vulnerabilities against potential dynamic LLM-based attacks.

https://github.com/prompt-security/ps-fuzz

ramikrispin,
@ramikrispin@mstdn.social avatar

(2/2) The CLI interface has an interactive playground chat interface, giving you the chance to iteratively improve your system prompt, hardening it against a wide spectrum of generative AI attacks

License: MIT 🦄

ramikrispin, to random
@ramikrispin@mstdn.social avatar

I have a feeling it is based on someone's true story.... I could not stop laughing 😂

Credit: Unknown...

ramikrispin, to vscode
@ramikrispin@mstdn.social avatar

Getting started with the Dev Containers extension 🚀👇🏼

The Dev Containers extension is the main reason I moved to VScode, as it provides a native and seamless integration of Docker 🐳. I started to work on a sequence of tutorials focusing on the VScode Dev Containers extension. The first tutorial on the sequence focuses on getting started with the Dev Containers extension;

🔗: https://medium.com/towards-data-science/getting-started-with-the-dev-containers-extension-a5ea49abfc34

ramikrispin, to rust
@ramikrispin@mstdn.social avatar

My Saturday morning reading - The Rust Programming Language 📖

ramikrispin, to random
@ramikrispin@mstdn.social avatar

Another great reason why R users should use Docker 🐳 - Airflow 😎

ramikrispin, to llm
@ramikrispin@mstdn.social avatar

Overview of Large Language Models 👇🏼

Here is a great summary or glossary doc about LLM by Aman Chadha. This long doc provides a summary of some of the main concepts related to LLM. This includes topics such as:
✅ Embeddings
✅ Vector database
✅ Prompt engineering
✅ Token
✅ RAG
✅ LLM performance evaluation
✅ Review main LLMs

🔗 https://aman.ai/primers/ai/LLM

ramikrispin, to python
@ramikrispin@mstdn.social avatar

(1/3) New Release to NeuralForecast 🚀

Version 1.7.1 of the NeuralForecast #Python library was released last month by Nixtla. The NeuralForecast library, as the name implies, provides a neural network framework for time series forecasting. 🧵👇🏼

#deeplearning #DataScience #MachineLearning #forecasting #timeseries

ramikrispin,
@ramikrispin@mstdn.social avatar

(2/3) The release includes the support for the following new models:
✅ BiTCN - temporal convolutional networks forecasting model
✅ iTransformer - transformer-based forecasting model
✅ MLPMultivariate - an MLP model that supports multivariate tasks

In addition, the library now supports multi-node distributed training with Spark ✨ and Polars 🐻‍❄️ data frames 🚀

ramikrispin,
@ramikrispin@mstdn.social avatar

(3/3) Installation: 𝘱𝘪𝘱 𝘪𝘯𝘴𝘵𝘢𝘭𝘭 𝘯𝘦𝘶𝘳𝘢𝘭𝘧𝘰𝘳𝘦𝘤𝘢𝘴𝘵

Licenses: Apache 2 🦄

More information is available in the release notes 👇🏼
https://github.com/Nixtla/neuralforecast/releases/tag/v1.7.1

Documentation 📖: https://nixtlaverse.nixtla.io/neuralforecast/index.html

Image credit: Documentation

ramikrispin, to rust
@ramikrispin@mstdn.social avatar

Rust for Beginners - Crash Course 🚀

If you are looking to get started with Rust, you should check this crash course for beginners by Microsoft Developer. The course focuses on the foundation of Rust, from setting up Rust on your local computer to core functionality of the language, such as if/else statements, for loops, functions, and other core operators.

Course 📽️: https://www.youtube.com/playlist?list=PLlrxD0HtieHjbTjrchBwOVks_sr8EVW1x

ramikrispin, to AWS
@ramikrispin@mstdn.social avatar

Introduction to AWS 🚀

If you wanna get started with AWS, here is an intro course by Amber Israelsen 👇🏼

📽️: https://www.youtube.com/playlist?list=PLwyXYwu8kL0wg9R_VMeXy0JiK5_c70IrV

The course is beginner-level, and it covers topics such as:
✅ Setting an account and managing the cost
✅ Set EC2 instance
✅ Work with S3 objects
✅ Create AWS Lambda function
✅ Create and deploy applications

ramikrispin, to datascience
@ramikrispin@mstdn.social avatar

The talks from the Data Council Austin '24 conference are now available to watch. The Data Council Austin conference focuses on data science and engineering topics. Among the 86 talks, you can find talks about topics such as time series analysis, working with different databases, ETL, machine learning, GenAI, etc.

Talks 📽️: https://www.youtube.com/playlist?list=PLAesBe-zAQmHZg7ScLzA5fzhR3Uox1vTE

ramikrispin, to python
@ramikrispin@mstdn.social avatar

(1/3) I am excited to share that my course - Data Pipeline Automation with GitHub Actions Using R and Python 🚀, is now available on LinkedIn Learning!

The course provides an introduction to setting up automation with GitHub Actions with both R and Python. Throughout the course, we will use a real-life example by working with the U.S. Energy Information Administration (EIA) API for data automation. 🧵👇🏼

ramikrispin,
@ramikrispin@mstdn.social avatar

(2/3) This includes:
✅ Learn how to work with the EIA API
✅ Define the data pipeline scope and characteristics
✅ Set functions to pull data and metadata from the API
✅ Set data backfill and refresh process
✅ Deploy the process to GitHub Actions
✅ Create a monitoring dashboard and deploy it on GitHub Pages

The course has tracks for both R and Python using tools such as Quarto docs, Git, and Docker.

image/png

ramikrispin,
@ramikrispin@mstdn.social avatar

The course is available on the LinkedIn Learning platform 📽️: https://www.linkedin.com/learning/data-pipeline-automation-with-github-actions-using-r-and-python/

ramikrispin,
@ramikrispin@mstdn.social avatar

@transportationtalk it would through an error when the GET request is not valid, could you please open an issue over here:
https://github.com/RamiKrispin/EIAapi/issues

It would help if you could point out the series you are trying to pull from the API.

ramikrispin,
@ramikrispin@mstdn.social avatar

@transportationtalk also, please check that you have js installed locally:

https://jqlang.github.io/jq/

ramikrispin, to llm
@ramikrispin@mstdn.social avatar

It was a pleasure to present this morning at the ODSC East about data automation with LMM.

Code examples and a tutorial are available on this repo: https://github.com/RamiKrispin/lang2sql
The slides are available on this repo: https://github.com/RamiKrispin/talks/tree/main/202404%20ODSC%20East%202024%20-%20%20Data%20Automation%20with%20LLM%20

Thanks to the conference organizers for the invite and the folks attending the session! 🙏

ramikrispin, to llm
@ramikrispin@mstdn.social avatar

In case you are wondering, the new Microsoft mini LLM - phi3, can handle code generation, in this case, SQL.

I compared the runtime (locally on CPU) with respect to codellama:7B using Ollama, and surprisingly the Phi3 runtime was significantly slower.

ramikrispin, to python
@ramikrispin@mstdn.social avatar

Thanks to @medium Staff for selecting my recent article - Introduction to Multi-Stage Image Build for Python 🐍, for a boost ❤️!

This tutorial provides a step-by-step guide for converting a regular Python Dockerfile into a multi-stage build 🚀.

🔗: https://medium.com/towards-data-science/introduction-to-multi-stage-image-build-for-python-41b94ebe8bb3

ramikrispin, to python
@ramikrispin@mstdn.social avatar

(1/4) 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐌𝐮𝐥𝐭𝐢-𝐒𝐭𝐚𝐠𝐞 𝐈𝐦𝐚𝐠𝐞 𝐁𝐮𝐢𝐥𝐝 🐳 𝐟𝐨𝐫 𝐏𝐲𝐭𝐡𝐨𝐧 🐍

The size of the Docker image could quickly increase during the build time. I became more mindful of the image size when I started to deploy on Github Actions. The bigger the image size, the longer the run time and the higher the runtime cost.

This is when you should consider using a multi-stage build 🚀.

🧵👇🏼

#docker #mlops #python #DataScience #medium

ramikrispin,
@ramikrispin@mstdn.social avatar

(3/4) 𝐖𝐡𝐞𝐧 𝐬𝐡𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐮𝐬𝐞 𝐚 𝐦𝐮𝐥𝐭𝐢-𝐬𝐭𝐚𝐠𝐞 𝐛𝐮𝐢𝐥𝐝?
You should consider moving your build to a multi-stage build when the build-required dependencies are no longer needed after the build is completed. A classic example is when building a binary application. Also, this is effective when setting up a dockerized Python environment using a virtual environment.

ramikrispin,
@ramikrispin@mstdn.social avatar

(4/4) I created the following tutorial for setting up a dockerized Python environment using a multi-stage approach 👇🏼

https://medium.com/towards-data-science/introduction-to-multi-stage-image-build-for-python-41b94ebe8bb3

Happy Build! 🐳🏗️

  • All
  • Subscribed
  • Moderated
  • Favorites
  • anitta
  • thenastyranch
  • magazineikmin
  • tacticalgear
  • InstantRegret
  • ngwrru68w68
  • Durango
  • Youngstown
  • slotface
  • mdbf
  • rosin
  • PowerRangers
  • kavyap
  • DreamBathrooms
  • normalnudes
  • vwfavf
  • hgfsjryuu7
  • cisconetworking
  • osvaldo12
  • everett
  • ethstaker
  • GTA5RPClips
  • khanakhh
  • tester
  • modclub
  • cubers
  • Leos
  • provamag3
  • All magazines