Real-world ways developers are using Marimo

Weights & Biases · Intermediate ·🎨 Image & Video AI ·1y ago

Key Takeaways

Marimo is being used by developers to create reusable apps for data exploration, internal analytics tools, and managing EKS clusters, streamlining workflows for both data scientists and software engineers.

Full Transcript

well look I want to ask you uh I mean are you aware of some of the real world use cases I mean it seems like one of the really cool things what you're doing is it's so broad like what are some of the interesting things that um people are doing today with marimo there's a lot that that is one of the fun things because it is so broad totally so you talked about Anthony Goldblum he like identifies I think one Persona and there's many people like him who do this who first use a Mara notebook to like explore some data but ultimately what they end up with is like a little mini reusable app like a tool that they can use on a daily basis to do like some analytics work or some kind of internal task so one of them is and that maybe that's like sort of the streamlet style uh application of I'm creating tools for myself and for my company to do analytics and other things what's interesting is that they're not just like Anthony's case they they typically interact with data but I've also had people reach out to me saying like I'm a software engineer I've never used notebooks before I never wanted to but marimo appeals to me because of the developer experience and I'm using it to manage and uh build a dashboard for my eks cluster or something like that

Original Description

Akshay Agrawal, Co-Founder of Marimo, shares exciting real-world use cases where developers are turning data exploration into powerful, reusable apps. From internal analytics tools to managing EKS clusters, Marimo is helping both data scientists and software engineers streamline their workflows and build impactful AI solutions. Discover how Marimo empowers creators to go from ideas to deployment seamlessly. #shorts
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from Weights & Biases · Weights & Biases · 0 of 60

← Previous Next →
1 0. What is machine learning?
0. What is machine learning?
Weights & Biases
2 1. Build Your First Machine Learning Model
1. Build Your First Machine Learning Model
Weights & Biases
3 Intro to ML: Course Overview
Intro to ML: Course Overview
Weights & Biases
4 2. Multi-Layer Perceptrons
2. Multi-Layer Perceptrons
Weights & Biases
5 3. Convolutional Neural Networks
3. Convolutional Neural Networks
Weights & Biases
6 Weights & Biases at OpenAI
Weights & Biases at OpenAI
Weights & Biases
7 Why Experiment Tracking is Crucial to OpenAI
Why Experiment Tracking is Crucial to OpenAI
Weights & Biases
8 4. Autoencoders
4. Autoencoders
Weights & Biases
9 5. Sentiment Analysis
5. Sentiment Analysis
Weights & Biases
10 6. Recurrent Neural Networks [RNNs]
6. Recurrent Neural Networks [RNNs]
Weights & Biases
11 7. Text Generation using LSTMs and GRUs
7. Text Generation using LSTMs and GRUs
Weights & Biases
12 8. Text Classification Using Convolutional Neural Networks
8. Text Classification Using Convolutional Neural Networks
Weights & Biases
13 9. Hybrid LSTMs [Long Short-Term Memory]
9. Hybrid LSTMs [Long Short-Term Memory]
Weights & Biases
14 Toyota Research Institute on Experiment Tracking with Weights & Biases
Toyota Research Institute on Experiment Tracking with Weights & Biases
Weights & Biases
15 Weights and Biases - Developer Tools for Deep Learning
Weights and Biases - Developer Tools for Deep Learning
Weights & Biases
16 Introducing Weights & Biases
Introducing Weights & Biases
Weights & Biases
17 10. Seq2Seq Models
10. Seq2Seq Models
Weights & Biases
18 11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
Weights & Biases
19 12. One-shot learning for teaching neural networks to classify objects never seen before
12. One-shot learning for teaching neural networks to classify objects never seen before
Weights & Biases
20 13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
Weights & Biases
21 14. Data Augmentation | Keras
14. Data Augmentation | Keras
Weights & Biases
22 15. Batch Size and Learning Rate in CNNs
15. Batch Size and Learning Rate in CNNs
Weights & Biases
23 Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
Weights & Biases
24 Grading Rubric for AI Applications with Sergey Karayev  (2019)
Grading Rubric for AI Applications with Sergey Karayev (2019)
Weights & Biases
25 16. Video Frame Prediction using CNNs and LSTMs (2019)
16. Video Frame Prediction using CNNs and LSTMs (2019)
Weights & Biases
26 Image to LaTeX - Applied Deep Learning Fellowship (2019)
Image to LaTeX - Applied Deep Learning Fellowship (2019)
Weights & Biases
27 17.  Build and Deploy an Emotion Classifier (2019)
17. Build and Deploy an Emotion Classifier (2019)
Weights & Biases
28 Applied Deep Learning - Data Management with Josh Tobin (2019)
Applied Deep Learning - Data Management with Josh Tobin (2019)
Weights & Biases
29 Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
Weights & Biases
30 Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
Weights & Biases
31 Troubleshooting and Iterating ML Models with Lee Redden (2019)
Troubleshooting and Iterating ML Models with Lee Redden (2019)
Weights & Biases
32 Designing a Machine Learning Project with Neal Khosla (2019)
Designing a Machine Learning Project with Neal Khosla (2019)
Weights & Biases
33 Lukas Beiwald on ML Tools and Experiment Management (2019)
Lukas Beiwald on ML Tools and Experiment Management (2019)
Weights & Biases
34 Building Machine Learning Teams with Josh Tobin (2019)
Building Machine Learning Teams with Josh Tobin (2019)
Weights & Biases
35 Pieter Abeel on Potential Deep Learning Research Directions  (2019)
Pieter Abeel on Potential Deep Learning Research Directions (2019)
Weights & Biases
36 Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
Weights & Biases
37 Five Lessons for Team-Oriented Research with Peter Welder (2019)
Five Lessons for Team-Oriented Research with Peter Welder (2019)
Weights & Biases
38 Applied Deep Learning - Rosanne Liu on AI Research (2019)
Applied Deep Learning - Rosanne Liu on AI Research (2019)
Weights & Biases
39 Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
Weights & Biases
40 Organizing ML projects — W&B walkthrough (2020)
Organizing ML projects — W&B walkthrough (2020)
Weights & Biases
41 Brandon Rohrer — Machine Learning in Production for Robots
Brandon Rohrer — Machine Learning in Production for Robots
Weights & Biases
42 Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
Weights & Biases
43 My experiments with Reinforcement Learning with Jariullah Safi
My experiments with Reinforcement Learning with Jariullah Safi
Weights & Biases
44 Applications of Machine Learning to COVID-19 Research with Isaac Godfried
Applications of Machine Learning to COVID-19 Research with Isaac Godfried
Weights & Biases
45 Testing Machine Learning Models with Eric Schles
Testing Machine Learning Models with Eric Schles
Weights & Biases
46 How Linear Algebra is not like Algebra with Charles Frye
How Linear Algebra is not like Algebra with Charles Frye
Weights & Biases
47 Predicting Protein Structures using Deep Learning with Jonathan King
Predicting Protein Structures using Deep Learning with Jonathan King
Weights & Biases
48 Rachael Tatman — Conversational AI and Linguistics
Rachael Tatman — Conversational AI and Linguistics
Weights & Biases
49 Reformer by Han Lee
Reformer by Han Lee
Weights & Biases
50 Sequence Models with Pujaa Rajan
Sequence Models with Pujaa Rajan
Weights & Biases
51 GitHub Actions & Machine Learning Workflows with Hamel Husain
GitHub Actions & Machine Learning Workflows with Hamel Husain
Weights & Biases
52 Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye
Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye
Weights & Biases
53 Jack Clark — Building Trustworthy AI Systems
Jack Clark — Building Trustworthy AI Systems
Weights & Biases
54 Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
Weights & Biases
55 Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
Weights & Biases
56 Antipatterns in open source research code with Jariullah Safi
Antipatterns in open source research code with Jariullah Safi
Weights & Biases
57 Attention for time series forecasting & COVID predictions - Isaac Godfried
Attention for time series forecasting & COVID predictions - Isaac Godfried
Weights & Biases
58 Made with ML - Goku Mohandas
Made with ML - Goku Mohandas
Weights & Biases
59 Angela & Danielle — Designing ML Models for Millions of Consumer Robots
Angela & Danielle — Designing ML Models for Millions of Consumer Robots
Weights & Biases
60 Deep Learning Salon by Weights & Biases
Deep Learning Salon by Weights & Biases
Weights & Biases

Marimo is a powerful tool for developers to create reusable apps for data exploration and streamline workflows. It offers a broad range of use cases, from internal analytics tools to managing EKS clusters. With Marimo, developers can build dashboards, manage data workflows, and explore data with notebooks.

Key Takeaways
  1. Explore data with Marimo notebooks
  2. Create reusable apps for data exploration
  3. Build dashboards for EKS clusters
  4. Manage data workflows with Marimo
  5. Streamline workflows for data scientists and software engineers
💡 Marimo's broad range of use cases and developer-friendly experience make it an attractive tool for both data scientists and software engineers to streamline their workflows.

Related Reads

📰
50+ Sequential Images, One Prompt in Codex
Learn to generate sequential images with Codex using a single prompt and understand the limitations of this approach
Medium · ChatGPT
📰
How can I batch-generate 3D assets from prompts or images using an API, and which 3D generation APIs support batch generation?
Learn to batch-generate 3D assets from prompts or images using APIs for efficient pipeline creation
Reddit r/artificial
📰
How AI Head Swap Works: The Technology Behind Realistic AI Image Replacement
Learn how AI head swap technology works and its applications in image editing
Dev.to AI
📰
How I Built an AI Pet Portrait Generator That Turns Photos Into Art
Learn how to build an AI pet portrait generator that turns photos into art using deep learning techniques and Python libraries
Dev.to · William Li
Up next
Topview AI Features EXPLAINED: Pros & Cons HONEST Review
MaxonShire
Watch →