Creating Your First ML Model with Google Cloud AutoML
- 1. Creating your First ML model with Google Cloud AutoML Rithvik K
- 3. Table of Contents -Introduction -About ML domain -Tools required(Kaggle,Google collab,Jupyter) -Learn how to train using Titanic data set and analyse -About Vertex ai -what is AutoML -Train and evaluate using Vertex AI and evaluate using AutoML
- 4. About me!!! Hey!! Myself Rithvik and enthusiast and prominent contributor in ML domain , Awarded as the Top ML Voice on LinkedIn and Google Arcade Facilitator’24 Would love to connect with you all and contribute to projects and solve complex problems Introduction
- 5. So what is ML-Machine learning?? So ML is basically is domain that allows the AI models to Learn and improvise on its own self without the need of direct programming the AI model About ML
- 6. Basic things/Tools that’s needed for a model Kaggle- Kaggle is a platform with huge datasets that Engineers can use to train their models based on this Jupyter-Jupyter is a basically computing platform that one can use for a basic use Google Collab – A hosted website by Jupyter that doesn’t require any installation, setup and free to use platform Tools for the day!
- 7. Training Using Titanic Data set
- 8. Running the data set on JUPYTER/GOOGLE COLAB
- 9. Vertex AI Vertex AI studio is a Google Cloud Console tool for rapidly prototyping and testing of models Vertex AI involves supervised learning tasks to achieve a chosen outcome. The specifics of the algorithm and training methods change based on the data type and use case. There are many different subcategories of machine learning, all of which solve different problems and work within different constraints. So what is Vertex AI
- 10. Vertex AI workflow Vertex AI uses a standard machine learning workflow: • Gather your data: Determine the data you need for training and testing your model based on the outcome you want to achieve. • Prepare your data: Make sure your data is properly formatted and labeled. • Train: Set parameters and build your model. • Evaluate: Review model metrics. • Deploy and predict: Make your model available to use.
- 11. Training Model
- 12. AutoML(Automated Machine Learning) AutoML can help make machine learning more accessible to people with little or no experience in data science. It can also help organizations build machine learning models more efficiently and with higher productivity. AutoML platforms can be built in-house or acquired from a third-party vendor. Some examples of AutoML platforms include:
- 14. THANK YOU!!! Connect with me on LinkedIn