TIL https://www.jailbreakchat.com/ is a website that collects prompt injection attacks for LLMs, i.e. getting the language model to do stuff that is not allowed by inserting malicious prompts.
Bash is a useful language for automating processes on the command line and has a lot of applications from IT to MLOps. The Bash Scripting on Linux course by Jay LaCroix is an intro course for Bash. The course focuses on the foundation of Bash scripting, and it covers the following topics:
โ Working with variables
โ If-Else statements
โ Loops
โ Functions
โ Arguments
โ Scheduling
In the past few months, I created a bunch of Docker ๐ณ tutorials covering random topics, from a fun setting for a Python ๐ environment on the CLI to advanced topics such as multi-stage builds ๐๏ธ. I organized all the tutorials under one folder, and I plan to keep updating this folder with future-related ones ๐.
Currently on my Docker tutorial TODO list:
โก๏ธ Docker ENTRYPOINT vs CMD
โก๏ธ Docker multi-architecture build
๐งโ๐ป New video! Walk through the "whole game" of #MLOps with #rstats:
๐ Data prep with #tidyverse
๐ง Model training & eval with #tidymodels
โ Deployment with #vetiver in #Docker ๐ณ on @huggingface ๐ค
๐ Monitoring with #pins
My thinking here is that @huggingface is an acquisition target for #NVIDIA because they don't have an #MLOps platform offering - I also wonder where #GitLab sits in all this too ...
Are you planning to learn a new data science or engineering skill as your New Yearโs resolution ? Here is a collection of random open and free courses and resources I came across during the past year covering various topics, including deep learning, NLP, Python, statistics, and more.
(1/2) MLflow for Machine Learning Development ๐
The MLflow for Machine Learning Development course by Manuel Gil provides a great introduction to the MLflow Python library ๐. The course focuses on the MLflow core functionality and workflow and covers the following topics:
โ Setting MLflow
โ Creating and working with experiences
โ Logging metadata (parameters, score, etc.)
โ Model registry
โ Model tuning
โ MLflow project demo
(1/2) I recently posted a few posts about Rust ๐ฆ and my intention to leverage it for data science applications. Multiple people asked if Rust is a substitute for R or Python, and the short answer (in my opinion) is no. I see Rust as a complementary or supporting language that could make languages like R and Python faster.
Polaris ๐ปโโ๏ธ is one example of a Python ๐ application that uses Rust on the backend. ๐งต๐๐ผ
Getting started with the Dev Containers extension ๐๐๐ผ
The Dev Containers extension is the main reason I moved to VScode, as it provides a native and seamless integration of Docker ๐ณ. 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;
Production Monitoring & Automations of LLM with LangSmith ๐ฆ๐๐ผ
LangChain released a crash course for LangSmith, their DevOps platform for deploying LLM applications into production. The course covers topics such as:
โ LLM applications monitoring
โ Setting automation
โ Performance monitoring
FreeCodeCamp released today a new course on building RAG from scratch with LangChain. The course, which is by Lance Martin from LangChain, focuses on the foundations of Retrieval Augmented Generation (RAG).
RAG From Scratch - Langchain Tutorial ๐ฆ๐๐ผ
The RAG From Scratch is a crash course by Lance Martin from LangChain focusing on the foundations of Retrieval Augmented Generation (RAG). This tutorial covers the process of index, retrieval, and generation of a query from scratch ๐.
(1/2) Setting A Dockerized ๐ณ Python ๐ Environment โ The Elegant Way
A few weeks ago, I created a short tutorial about setting up a dockerized ๐ณ Python ๐ environment via the CLI, or the hard way. The second tutorial on this topic provides a more elegant and robust approach for setting up a Python dockerized development environment with VScode and the Dev Containers extension ๐.
(1/2) I created the second tutorial on the series of running RStudio inside a container ๐. This tutorial focuses on formalizing the run command from the first tutorial with Docker Compose using the Rocker RStudio image ๐ณ ๐๐ผ
Setting and running RStudio inside a containerized environment is easier than it seems, thanks to the Rocker project.
(1/3) I created a step-by-step tutorial for launching and customizing the RStudio server in a container using the Rocker RStudio image ๐ณ and the run command ๐ ๐๐ผ
Setting and running RStudio inside a containerized environment is easier than it seems, thanks to the Rocker project. This tutorial mainly focuses on the docker run command.
(3/3) This is the first tutorial out of a sequence. The next ones are going to cover:
โก๏ธ Formalizing the run command with Docker Compose
โก๏ธ Customizing the Rocker image with additional requirements
โก๏ธ Create a template
โก๏ธ Mount databases (e.g., Postgres, etc.)
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 ๐.