Custom training

Qwiklabs-Courses

4 May 202301:17

Summary

TLDRCustom Training with Vertex AI Workbench allows developers to code their own machine learning models, controlling the entire ML development process from data preparation to deployment. It offers two environment options: pre-built containers with all necessary dependencies and libraries, or custom containers where developers define their tools. This flexibility caters to various ML training needs, making it ideal for those who require specific platforms like TensorFlow, PyTorch, or scikit-learn.

Takeaways

💡 Custom Training allows for full control over the ML development process from data preparation to deployment.
🛠️ You can code your own machine learning model using custom Training with Vertex AI Workbench.
💻 Vertex AI Workbench provides a single development environment for the entire data science workflow.
📈 It supports activities from exploring data to training and deploying machine learning models with code.
🏗️ Before coding, you must decide on the environment for your ML training code: pre-built or custom container.
🏠 A pre-built container is like a fully furnished kitchen with all necessary dependencies and libraries.
🔧 A custom container is like an empty room, allowing you to define the exact tools needed for the job.
🌐 If your ML training requires platforms like TensorFlow, PyTorch, Scikit-learn, or XGBoost, a pre-built container is recommended.
🛠️ Custom containers give you the flexibility to include only the necessary tools and dependencies for your specific ML tasks.
🔑 The choice between pre-built and custom containers depends on the specific needs and complexity of your ML project.

Q & A

What is custom training?
-Custom training is the process of coding your own machine learning environment to control the entire machine learning development process, from data preparation to model deployment.
What is the purpose of using Vertex AI Workbench for custom training?
-Vertex AI Workbench provides a single development environment for the entire data science workflow, from exploring data to training and deploying machine learning models with code.
What are the two options available for the environment in custom training?
-The two options available for the environment in custom training are using a pre-built container or a custom container.
What is a pre-built container in the context of machine learning training?
-A pre-built container is a fully furnished environment with all the necessary dependencies and libraries, like a kitchen with cabinets and appliances, suitable for ML training that requires platforms such as TensorFlow, PyTorch, Scikit-learn, or XGBoost.
What does a pre-built container include?
-A pre-built container includes all the dependencies, libraries, and tools needed for machine learning training, such as cookware in a kitchen, representing the necessary tools to complete the job.
What is a custom container?
-A custom container is an empty environment where you define and include only the exact tools and dependencies required for your specific machine learning training job.
Why would someone choose a custom container over a pre-built one?
-A custom container would be chosen when the user has specific requirements that are not met by the pre-built container, allowing for a tailored environment to fit their unique needs.
How does the analogy of a kitchen relate to machine learning training environments?
-The analogy of a kitchen is used to describe the completeness of a pre-built container, where everything is provided like in a fully furnished kitchen, whereas a custom container is likened to an empty room where you bring in only what you need.
What are some examples of platforms that might be included in a pre-built container?
-Examples of platforms that might be included in a pre-built container are TensorFlow, PyTorch, Scikit-learn, and XGBoost.
What is the first step before coding begins in custom training?
-The first step before coding begins in custom training is to determine the environment that the ML training code will use, choosing between a pre-built container or a custom container.
What is the role of Python code in custom training with a pre-built container?
-Python code plays a crucial role in custom training with a pre-built container, as it is used to work with the included machine learning platforms and to develop the training scripts.

Outlines

00:00

💻 Custom Training with Vertex AI Workbench

Custom Training is a feature that allows users to code their own machine learning environment, controlling the entire ML development process from data preparation to model deployment. It is ideal for those who want to code their ML models. Vertex AI Workbench serves as a single development environment for the entire data science workflow, including exploring, training, and deploying ML models with code. Before coding, users must choose between a pre-built container or a custom container for their ML training code. A pre-built container is like a fully furnished kitchen with all necessary dependencies and libraries, suitable for ML training platforms like TensorFlow, PyTorch, Scikit-learn, or XGBoost. A custom container, on the other hand, is like an empty room where users can define the exact tools needed to complete their ML tasks.

Mindmap

Keywords

💡Custom Training

Custom Training refers to the process where developers build and manage their own machine learning (ML) environment. In the context of the video, it allows for full control over the entire ML development pipeline, from data preparation to deployment. This is a solution for those who wish to code their ML models and requires a hands-on approach, compared to using pre-built ML solutions.

💡ML Environment

An ML (Machine Learning) environment is the infrastructure in which machine learning models are developed, trained, and deployed. In the video, it refers to the customizable or pre-built environments provided by Vertex AI Workbench, where developers can choose what tools, libraries, and dependencies they want to use for their training process.

💡Vertex AI Workbench

Vertex AI Workbench is a unified development environment offered by Google Cloud for the entire data science workflow. It integrates data exploration, model training, and deployment in a single platform, as mentioned in the video. This workbench simplifies ML development by providing tools and support for both code-based and no-code options.

💡Pre-built Container

A pre-built container is a ready-made environment equipped with the necessary dependencies and libraries (like TensorFlow, PyTorch, etc.) to develop machine learning models. In the video, it is compared to a fully furnished kitchen with all necessary appliances, making it ideal for users who need a standardized environment to start training models quickly.

💡Custom Container

A custom container is an empty environment where developers can define and install their specific tools and dependencies for machine learning tasks. It gives full control over the setup, similar to an empty kitchen where users bring in their own appliances and cookware, as explained in the video. This is useful for those who need a highly customized ML environment.

💡TensorFlow

TensorFlow is a popular open-source machine learning framework used for developing deep learning models. In the context of the video, TensorFlow is one of the platforms that can be used within pre-built containers for training machine learning models on Vertex AI Workbench. It offers ease for those who prefer standard ML frameworks.

💡PyTorch

PyTorch is another widely used open-source machine learning library, particularly favored for its ease of use and flexibility in building complex neural networks. Similar to TensorFlow, it is mentioned as one of the pre-installed libraries available in pre-built containers within Vertex AI Workbench.

💡Scikit-learn

Scikit-learn is a Python library used for classical machine learning algorithms like classification, regression, and clustering. In the video, it is referenced as one of the pre-built platforms that can be selected in the ML environment on Vertex AI Workbench, making it suitable for those working with simpler machine learning tasks.

💡XGBoost

XGBoost is a machine learning algorithm known for its efficiency and performance, especially in structured data tasks. In the video, it is mentioned as part of the pre-built container options, providing a powerful tool for developers who need to build models that handle large, complex datasets efficiently.

💡Model Deployment

Model deployment refers to the process of putting a trained machine learning model into production where it can make predictions on new data. In the video, it is highlighted as one of the final steps of the ML development process that can be managed using the Vertex AI Workbench, ensuring a streamlined transition from training to production.

Highlights

Custom training allows for coding your own ML environment.

It controls the entire ML development process from data preparation to deployment.

You can code your machine learning model using custom training.

Vertex AI Workbench is a single development environment for data science workflows.

Workbench supports exploring, training, and deploying ML models with code.

You must determine the environment for your ML training code before coding.

There are two options for the environment: pre-built container or custom container.

A pre-built container is like a fully furnished kitchen with all necessary dependencies.

Pre-built containers include libraries like TensorFlow, PyTorch, scikit-learn, or XGBoost.

A custom container is like an empty room where you define the exact tools needed.

Custom containers allow for precise control over the ML training environment.

The choice between pre-built and custom containers depends on ML training needs.

Pre-built containers are suitable for platforms like TensorFlow, PyTorch, etc.

Custom containers are ideal for specific or unique ML training requirements.

Vertex AI Workbench streamlines the entire data science workflow.

Custom training solutions enable full control over the ML development process.

The environment setup is crucial for the success of ML training.

Custom training offers flexibility in choosing the right tools and libraries.