Build a custom ML model with Vertex AI

Google Cloud Tech
25 Jun 202110:55

TLDRIn this informative video, Priyanka Vergadia guides viewers through the process of building a custom machine learning model with Vertex AI. The focus is on assisting Fuel Symbol's team to predict vehicle fuel efficiency. The video covers creating a Python training application or custom container, utilizing pre-built containers for popular ML frameworks, and leveraging Vertex AI's hyperparameter tuning service. It also discusses compute resource requirements, model deployment, and making predictions using the Vertex AI Python SDK. The demonstration showcases the flexibility of Vertex AI in training models with various frameworks and deploying them for real-world applications.

Takeaways

  • ๐Ÿš€ **Custom ML Model Building**: The video discusses building a custom machine learning model for Fuel Symbol to predict vehicle fuel efficiency.
  • ๐Ÿ› ๏ธ **Python Training Application**: To create a custom job, a Python training application or custom container with training code and dependencies is required.
  • ๐Ÿ“ฆ **Pre-built Containers**: Pre-built containers for TensorFlow, scikit-learn, XG Boost, or PyTorch are available for running the training code.
  • ๐Ÿ”ง **Custom Containers**: Building custom containers is an option for specific requirements, which can be stored in a container or artifact registry.
  • ๐Ÿ” **Hyperparameter Tuning**: Vertex AI offers hyperparameter tuning services to find the best combination of hyperparameters for model training.
  • ๐Ÿ’ป **Compute Resources**: Training jobs require compute resources, which can range from single-node to multi-worker pools with options for machine types, CPUs, disk sizes, and accelerators.
  • ๐Ÿš€ **Model Deployment**: After training, the model can be served using pre-built containers or custom containers for predictions.
  • ๐Ÿ“ˆ **TensorFlow Model**: The example uses TensorFlow to build the model and packages the training code in a Docker container.
  • ๐Ÿ”— **Google Cloud Services**: The process involves using Google Cloud services like Vertex AI API, Compute Engine, and Container Registry.
  • ๐Ÿ–ฅ๏ธ **JupyterLab Environment**: A new notebook instance with TensorFlow Enterprise is used for creating and testing the Docker container locally.
  • ๐Ÿข **Endpoint for Predictions**: Once the model is trained, it's deployed to an endpoint for making predictions using the Vertex AI Python SDK.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is building a custom machine learning model using Vertex AI and walking through the process of training and deploying the model.

  • Who is the host of the video?

    -The host of the video is Priyanka Vergadia.

  • What does the Fuel Symbol team aim to predict?

    -The Fuel Symbol team aims to predict the fuel efficiency of a vehicle using a custom machine learning model.

  • What are the pre-built containers available for running the training code in Python?

    -The pre-built containers available for running the training code in Python include TensorFlow, scikit-learn, XG Boost, and PyTorch.

  • How can one utilize Vertex's hyperparameter tuning service?

    -Vertex's hyperparameter tuning service can be utilized by creating trials of the training job with different sets of hyperparameters and searching for the best combination across those trials.

  • What type of compute resources are needed for training?

    -For training, one can choose between single-node or multiple worker pools for distributed training, including the selection of machine types, CPUs, disk sizes, disk types, and accelerators like GPUs.

  • How is the trained model served for predictions in Vertex AI?

    -The trained model is served for predictions in Vertex AI by using pre-built containers that support runtime such as TensorFlow, scikit-learn, and PyTorch, or by building a custom container.

  • What is the purpose of the Docker file in the custom training process?

    -The Docker file is used to create a custom container for the training code, setting up the entry point for the training code and including necessary dependencies and frameworks.

  • How can the custom container be tested before deploying it for training?

    -The custom container can be tested locally by building and running it within a notebook environment to ensure it works correctly before pushing it to Google Container Registry for cloud deployment.

  • What is the role of the Cloud Storage bucket in the training process?

    -The Cloud Storage bucket is used to store the trained TensorFlow model as artifacts, which Vertex AI will then use to read the imported model assets and deploy the model.

  • How can predictions be made using the trained model?

    -Predictions can be made using the trained model by deploying it to an endpoint and making predictions through the Vertex AI Python SDK or any other preferred environment.

Outlines

00:00

๐Ÿš€ Introduction to Custom Machine Learning Models in Vertex AI

This paragraph introduces Priyanka Vergadia, the host of AI Simplified, and sets the stage for the episode focused on custom machine learning models in Vertex AI. It recaps previous episodes on creating datasets and using auto ML for model training. The main objective is to assist Fuel Symbol in developing a custom machine learning model to predict vehicle fuel efficiency. The paragraph explains the prerequisites for creating a custom training job, such as a Python training application or custom container, and the use of pre-built containers for popular ML frameworks like TensorFlow, scikit-learn, XG Boost, and PyTorch. It also touches on the importance of Cloud Storage for model output artifacts and the potential use of Vertex AI's hyperparameter tuning service. The need for compute resources and the option to serve the trained model for predictions are also discussed.

05:02

๐Ÿ› ๏ธ Setting Up Custom Training with Docker and Google Cloud

This section delves into the specifics of setting up a custom training environment using Docker and Google Cloud services. It begins by explaining the creation of a Docker file and the use of TensorFlow Enterprise Docker images, which come preloaded with common ML and data science frameworks. The paragraph details the process of setting up a Cloud Storage bucket for exporting the trained TensorFlow model and the creation of a 'train.py' file with adapted code from TensorFlow docks. It then guides through building and testing the container locally, pushing it to Google Container Registry, and initiating a custom model training job in Vertex AI. The options for pre-built versus custom containers, hyperparameter tuning, compute resources, and model serving are also discussed, along with the steps for deploying the trained model to an endpoint for predictions.

10:05

๐Ÿ“Š Training and Deploying a Custom Model for Fuel Efficiency Prediction

In this final paragraph, the focus is on the actual training and deployment of the custom model for predicting fuel efficiency. The paragraph describes how the custom training code in a Docker container is used with TensorFlow, and how the trained model is deployed using pre-built containers. It highlights the successful completion of training and the creation of a model endpoint for making predictions. The paragraph concludes with a brief mention of the next episode's content, which will cover building a vision model using auto ML, and encourages viewers to engage in discussions and follow the series for updates.

Mindmap

Keywords

๐Ÿ’กVertex AI

Vertex AI is a cloud-based platform designed for building and deploying machine learning models. In the video, it is the primary tool used by Fuel Symbol's team to create custom machine learning models for predicting vehicle fuel efficiency. Vertex AI offers various features such as auto ML, custom training, hyperparameter tuning, and model deployment, which are essential for developing high-quality, efficient models.

๐Ÿ’กCustom Machine Learning Models

Custom Machine Learning Models refer to models that are specifically designed and written by a team of experts to solve a particular problem or meet specific requirements. In the context of the video, Fuel Symbol's team of machine learning experts decides to write their own training code to predict the fuel efficiency of vehicles, showcasing the flexibility and tailored approach of custom models in addressing unique business needs.

๐Ÿ’กPython Training Application

A Python Training Application is a software program written in Python that is used to train machine learning models. In the video, it is mentioned that to create custom jobs in Vertex AI, one needs to develop a Python training application or custom container, which includes the training code and its dependencies. This application is crucial for running custom training in Vertex AI and is used to create and optimize the machine learning model.

๐Ÿ’กPre-built Containers

Pre-built Containers are pre-configured software environments that include all the necessary dependencies and libraries for running a specific task, such as machine learning training. In the video, it is explained that Vertex AI provides pre-built containers for popular machine learning frameworks like TensorFlow, scikit-learn, XG Boost, and PyTorch, which simplifies the process of running custom training jobs by eliminating the need to set up the environment from scratch.

๐Ÿ’กCustom Containers

Custom Containers are software packages that are tailored to meet specific requirements or include non-standard dependencies and libraries. In the video, the process of building a custom container is discussed, which allows for greater flexibility and control over the training environment. This is particularly useful when using machine learning frameworks or libraries that are not available in the pre-built containers or when additional non-ML specific dependencies are needed.

๐Ÿ’กCloud Storage Bucket

A Cloud Storage Bucket is a storage location in the cloud where data, including machine learning model artifacts, can be stored and accessed. In the video, it is mentioned that a Cloud Storage Bucket is necessary for storing the output artifacts of the model training. Vertex AI uses this bucket to import the trained model assets and for deploying the model, making it an essential component in the machine learning workflow.

๐Ÿ’กHyperparameter Tuning

Hyperparameter Tuning is the process of optimizing the performance of a machine learning model by adjusting its hyperparameters, which are the variables that govern the training process. In the video, it is explained that Vertex AI offers a hyperparameter tuning service that creates multiple trials of a training job with different sets of hyperparameters to find the best combination, thereby improving the model's predictive accuracy.

๐Ÿ’กCompute Resources

Compute Resources refer to the hardware and software resources required to perform computations, such as processing power, memory, and storage. In the context of the video, compute resources are crucial for training machine learning models, as they can be configured to include single or multiple worker nodes, CPUs, disk sizes, and accelerators like GPUs, depending on the complexity and scale of the model training task.

๐Ÿ’กModel Endpoint

A Model Endpoint is the address or location where the trained machine learning model is deployed and can be accessed to make predictions. In the video, it is discussed that after training the model, an endpoint needs to be created to serve the model for predictions. The endpoint can be deployed using pre-built containers in Vertex AI, and it allows for the model to be used in real-time or batch predictions.

๐Ÿ’กTensorFlow

TensorFlow is an open-source machine learning framework used for numerical computation and large-scale machine learning. In the video, TensorFlow is the chosen framework for building the custom model to predict fuel efficiency. It is highlighted for its capabilities in creating complex models and its compatibility with Vertex AI, which simplifies the process of training and deploying models.

๐Ÿ’กDocker Container

A Docker Container is a lightweight, standalone, and executable package of software that includes everything needed to run an application, such as a machine learning model. In the video, the training code is packaged into a Docker container, which is then pushed to Google Container Registry for Vertex AI to access. This approach allows for training models built with any framework and ensures that the environment in which the model runs is consistent and controlled.

Highlights

Learn to build custom machine learning models with Vertex AI.

Discover how Fuel Symbol uses custom ML models to predict vehicle fuel efficiency.

Understand the requirements for creating a custom training job in Vertex AI.

Explore the use of pre-built containers for Python training applications.

Get insights on building your own custom containers for unique training code.

Learn how to utilize Vertex AI's hyperparameter tuning service for optimized model training.

Find out how to select compute resources for your training jobs.

Discover how to use pre-built containers for serving your trained models.

Get a step-by-step guide on creating a Docker container for your TensorFlow model.

Understand the process of deploying your trained TensorFlow model from Cloud Storage.

Learn how to run custom training jobs on Vertex AI using Google Container Registry.

Explore the option of using custom containers for model deployment beyond common ML frameworks.

Find out how to set up and deploy an endpoint for making predictions with your model.

See a demonstration of making predictions using the Vertex AI Python SDK from a notebook.

Get a preview of the next video where a vision model will be built using auto ML.

Engage with the community and discuss the video content in the comments section.

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