No-code machine learning (ML) is a way to build and deploy ML models without having to write any code. Low-code ML is a way to build and deploy ML models with minimal coding. Both methods can be valuable for businesses and individuals who do not have the skills or resources to develop ML models themselves.

By completing this workshop, you will develop an understanding of no-code and low-code frameworks, how they are used in the ML workflow, how they can be used for data ingestion and analysis, and for building, training, and deploying ML models.

You will become familiar with Google’s Vertex AI for both no-code and low-code ML model training, and Google’s Colab, a free Jupyter Notebook service for running Python and the Keras Sequential API, a simple and easy-to-use API that is well-suited for beginners. You will also become familiar with how to assess when to use low-code, no-code, and custom ML training frameworks.

Session Outline:
Lesson 1: Introduction to the Machine Learning Workflow. Familiarize yourself with the ML Workflow. At the end of this lesson, you will be able to comfortably explain the components of the ML workflow, and understand how No-Code and Low-Code solutions fit in this ecosystem, and understand the appropriate use cases for each framework.

Lesson 2: Using AutoML for Advertising Media Channel Sales Prediction. In this lesson, you work on a team charged with developing a media strategy for an insurance company. The team wants to develop an ML model to predict sales based on advertising spend in various media channels. You are tasked with performing exploratory data analysis and with building and training the model. You do not have an ML background or any programming knowledge. You elect to use AutoML as your ML framework. You will practice loading a dataset, analyzing data, and building and training a Supervised Learning Linear Regression model without writing a single line of code.

Lesson 3: Using Low-Code AI for Advertising Media Channel Sales Prediction. In this lesson, you use the same use case as in Lesson 2, but this time, you use a low-code AI approach. You elect to use Low-code AI to give you a bit more control over building your neural network and experimenting with improving model performance. You now use a Python Jupyter Notebook to import Pandas, load a dataset and perform exploratory data analysis. You then build and train a model using the Keras Sequential API.