Creative AI - Using ML to Create Art, Music, and Jokes // Suyash Joshi // MLOps Meetup #113

MLOps.community · Intermediate ·🎨 Image & Video AI ·3y ago
MLOps Community Meetup #113! On October 19 we talked to Suyash Joshi, a Creative Technologist & Artist. //Abstract Can AI be truly creative? Can model ML models augment human creativity? How do we leverage ML for creating works of Art, Music, and even Jokes? This presentation answers these questions and more by carefully looking at various open-source ML Models and understanding their strengths, drawbacks, and societal implications. It also includes live demos with code walkthroughs of creative projects using open-source MusicVAE, GAN Art, Neural StyleTransfer, SketchRNN, StableDiffusion, Open AI Whisper, and transformer-based models for joke generation. // Bio Suyash Joshi is a UX engineer and designer based out of London. He brings extensive experience in web, mobile, and ml engineering and enjoys making artful projects with code. He also performs sleight-of-hand magic and is currently pursuing a postgraduate degree at Goldsmiths, University of London. // Jobs board https://mlops.pallet.xyz/jobs // Related links Suyash created this app as part of the Google Magenta & Gray Area Music AI Hackathon with two other friends and we won the award for the same: https://magenta.tensorflow.org/dear-diary StyleGAN paper: https://paperswithcode.com/method/stylegan Artificial Intelligence Distinguished Lecture: Allison Parrish - https://vectorinstitute.ai/event/artificial-intelligence-distinguished-lecture-allison-parrish/ https://www.youtube.com/watch?v=Lx8PYRt5Irw MLOps Community Newsletter: https://airtable.com/shrx9X19pGTWa7U3Y ----------- ✌️Connect With Us ✌️------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, Feature Store, Machine Learning Monitoring, and Blogs: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Suyash on LinkedIn: https://www.linkedin.co
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from MLOps.community · MLOps.community · 0 of 60

← Previous Next →
1 Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
MLOps.community
 2 Remote Collaboration as a Data Scientist
Remote Collaboration as a Data Scientist
MLOps.community
 3 MLOps Manifesto with Luke Marsden from Dotscience
MLOps Manifesto with Luke Marsden from Dotscience
MLOps.community
 4 MLOps lifecycle description
MLOps lifecycle description
MLOps.community
 5 What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2
What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2
MLOps.community
 6 Life purpose and too many spreadsheets
Life purpose and too many spreadsheets
MLOps.community
 7 Explainability, Black boxes and EU white paper on reproducibility
Explainability, Black boxes and EU white paper on reproducibility
MLOps.community
 8 Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
MLOps.community
 9 Automatically Retrain Machine Learning Models? Are best practices worth it?
Automatically Retrain Machine Learning Models? Are best practices worth it?
MLOps.community
 10 Building an MLOps Team? Key ideas to keep in mind
Building an MLOps Team? Key ideas to keep in mind
MLOps.community
 11 Hierarchy of MLOps Needs
Hierarchy of MLOps Needs
MLOps.community
 12 Bare necessities for getting an ML model into production
Bare necessities for getting an ML model into production
MLOps.community
 13 MLOps and Monitoring
MLOps and Monitoring
MLOps.community
 14 How Phil Winder got into Data Science and Software Engineering
How Phil Winder got into Data Science and Software Engineering
MLOps.community
 15 Provenance and Reproducibility in Machine Learning; what is it and why you need it?
Provenance and Reproducibility in Machine Learning; what is it and why you need it?
MLOps.community
 16 Friction Between Data Scientists and Software Engineers
Friction Between Data Scientists and Software Engineers
MLOps.community
 17 MLOps Problems in different size companies
MLOps Problems in different size companies
MLOps.community
 18 ML tooling in large companies
ML tooling in large companies
MLOps.community
 19 ML Platforms - The build vs buy question
ML Platforms - The build vs buy question
MLOps.community
 20 ML Services Gateway at SurveyMonkey
ML Services Gateway at SurveyMonkey
MLOps.community
 21 Message buses, Async and sync architecture
Message buses, Async and sync architecture
MLOps.community
 22 MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
MLOps.community
 23 Hybrid Data Science Teams @SurveyMonkey
Hybrid Data Science Teams @SurveyMonkey
MLOps.community
 24 How do you handle ML version control at SurveyMonkey
How do you handle ML version control at SurveyMonkey
MLOps.community
 25 Doing ML with Personal Information
Doing ML with Personal Information
MLOps.community
 26 Evolution of the ML feature store @SurveyMonkey
Evolution of the ML feature store @SurveyMonkey
MLOps.community
 27 Developing a Machine Learning Feature Store
Developing a Machine Learning Feature Store
MLOps.community
 28 Auto retrain ML models is not the question
Auto retrain ML models is not the question
MLOps.community
 29 3 key parts to Machine Learning monitoring
3 key parts to Machine Learning monitoring
MLOps.community
 30 MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
MLOps.community
 31 MLOps meetup #5 High Stakes ML: Active Failures, Latent Factors with Flavio Clesio
MLOps meetup #5 High Stakes ML: Active Failures, Latent Factors with Flavio Clesio
MLOps.community
 32 MLOps: Airflow Pros and Cons
MLOps: Airflow Pros and Cons
MLOps.community
 33 Specific challenges in Machine Learning
Specific challenges in Machine Learning
MLOps.community
 34 Current State Of Machine Learning
Current State Of Machine Learning
MLOps.community
 35 Humans in the Loop are a defining factor in Machine Learning
Humans in the Loop are a defining factor in Machine Learning
MLOps.community
 36 Learning from real life Machine Learning failures
Learning from real life Machine Learning failures
MLOps.community
 37 Survivorship Bias in machine learning tutorials
Survivorship Bias in machine learning tutorials
MLOps.community
 38 Swiss Cheese model in Machine Learning
Swiss Cheese model in Machine Learning
MLOps.community
 39 Resume driven development in Machine learning & software engineering
Resume driven development in Machine learning & software engineering
MLOps.community
 40 Who has the highest standards in ML?
Who has the highest standards in ML?
MLOps.community
 41 Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning
Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning
MLOps.community
 42 Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data
Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data
MLOps.community
 43 Speed, Trust, Evolution and Scale in MLOps
Speed, Trust, Evolution and Scale in MLOps
MLOps.community
 44 More difficult transition for data scientists to become ML engineers
More difficult transition for data scientists to become ML engineers
MLOps.community
 45 How many models in prod til I need a dedicated ML platform?
How many models in prod til I need a dedicated ML platform?
MLOps.community
 46 Deeper thinking from data scientists around platform blackholes
Deeper thinking from data scientists around platform blackholes
MLOps.community
 47 Checkpointing, metadata, and confidence in your data
Checkpointing, metadata, and confidence in your data
MLOps.community
 48 Adjacent usecases and multistep feature engineering
Adjacent usecases and multistep feature engineering
MLOps.community
 49 Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali
Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali
MLOps.community
 50 Reproducability flaws in end to end Machine Learning debugging
Reproducability flaws in end to end Machine Learning debugging
MLOps.community
 51 3rd wave of data scientists
3rd wave of data scientists
MLOps.community
 52 MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
MLOps.community
 53 MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
MLOps.community
 54 Are Kubeflow and Airflow complementary?
Are Kubeflow and Airflow complementary?
MLOps.community
 55 Why Kubeflow gained so much traction=open community
Why Kubeflow gained so much traction=open community
MLOps.community
 56 Who decides the dirrection of Kubeflow
Who decides the dirrection of Kubeflow
MLOps.community
 57 What do Kubeflow and Arrikto do and how do they work together?
What do Kubeflow and Arrikto do and how do they work together?
MLOps.community
 58 Versioning your ML steps with Kubeflow
Versioning your ML steps with Kubeflow
MLOps.community
 59 Machine Learning Lifecycles//Perception vs Reality
Machine Learning Lifecycles//Perception vs Reality
MLOps.community
 60 Kubeflow vs SageMaker in Machine Learning
Kubeflow vs SageMaker in Machine Learning
MLOps.community

Related AI Lessons

⚡
I built a Stable Diffusion playground in 200 lines and zero API keys. Here's how.
Build a Stable Diffusion playground in under 200 lines of code without needing API keys, and explore AI image generation
Dev.to · Devanshu Biswas
 ⚡
What makes an AI image workflow useful for real commercial output?
Learn how to create a useful AI image workflow for commercial output, focusing on repeatability, versatility, and clarity
Dev.to AI
 ⚡
How to Write Better AI Image Prompts for Midjourney (With Examples That Actually Work)
Learn to write effective AI image prompts for Midjourney with actionable examples and techniques
Medium · ChatGPT
 ⚡
Image to Video AI: The Complete Workflow Playbook That Actually Produces Results
Learn a step-by-step workflow for image-to-video AI that produces results, from preparation to delivery
Medium · AI
Up next
Krea 2 makes Diffusion FUN Again!
MattVidPro
Watch →