Building ML models with Vertex AI

Summary

Google's Vertex AI streamlines machine learning model development for ML developers, data scientists, and engineers. It addresses traditional challenges like data handling, model selection, and computing power. Vertex AI offers a unified platform for model creation, management, and deployment, supporting both AutoML for ease of use and custom training for full control. It simplifies the workflow, enhances scalability, ensures sustainability through artifact reuse, and speeds up model production with fewer lines of code.

Takeaways

• 🤖 **Google's AI Journey**: Google has been investing in AI since the development of scikit-learn in 2007, leading to the creation of Vertex AI.
• 🌟 **AI Across Google**: Google applies AI technologies in various products like Gmail, Google Maps, Google Photos, and Google Translate.
• 🚀 **Challenges in ML**: Traditional challenges in machine learning include handling large data, choosing the right model, and computing power.
• 🔄 **Production Challenges**: Challenges in getting ML models into production include scalability, monitoring, and continuous integration and deployment.
• 📊 **Gartner's Insight**: According to Gartner, only half of Enterprise ML projects make it past the pilot phase.
• 🛠️ **Vertex AI Solution**: Vertex AI addresses production and ease of use challenges by providing a unified platform for ML workflows.
• 🔧 **Unified Platform Definition**: Vertex AI offers a single digital experience for creating, managing, and deploying ML models at scale.
• 📈 **Data and Feature Readiness**: Users can upload data and create features using the feature store for model input.
• 🎯 **Training and Tuning**: Vertex AI facilitates training, hyperparameter tuning, and experimentation with different models.
• 🔄 **Deployment and Monitoring**: The platform supports setting up pipelines for model deployment, monitoring, and continuous improvements.
• 👩‍💻 **Two Approaches**: Vertex AI allows building ML models through AutoML (no-code) for ease of use, or custom training for full control.
• 🏆 **Benefits of Vertex AI**: The platform offers seamless, scalable, sustainable, and speedy ML model development.

Q & A

What is the main challenge in developing machine learning models?

Developing machine learning models involves challenges such as handling large quantities of data, choosing the right model to train the data, and ensuring sufficient computing power.

How does Google apply AI technologies to its products and services?

Google applies AI technologies to products and services like Gmail, Google Maps, Google Photos, and Google Translate.

What are the production challenges that ML models face?

Production challenges for ML models include scalability, monitoring, and the need for continuous integration and continuous delivery or deployment.

Why do many enterprise ML projects fail to get past the pilot phase?

According to Gartner, only half of enterprise ML projects get past the pilot phase due to challenges in scaling, monitoring, and integrating ML models into production.

What is Google's solution to the challenges of ML model production and ease of use?

Google's solution is Vertex AI, a unified platform that simplifies the machine learning ecosystem and workflow, addressing both production and ease of use challenges.

What does a unified platform mean in the context of Vertex AI?

A unified platform in Vertex AI means having a single digital experience to create, manage, and deploy models over time and at scale.

What are the stages involved in the machine learning workflow according to the script?

The stages involved in the machine learning workflow are data readiness, feature readiness, training and hyperparameter tuning, and deployment and model monitoring.

How does Vertex AI facilitate the data readiness stage?

Vertex AI allows users to upload data from various sources like cloud storage, BigQuery, or a local machine during the data readiness stage.

What is a feature store and how does it relate to Vertex AI?

A feature store is a place where users can create and share features, which are processed data inputs for models, within Vertex AI.

What are the benefits of using Vertex AI for building machine learning models?

Vertex AI offers benefits such as seamless user experience, scalability, sustainability by reusing and sharing artifacts, and speed by producing models with fewer lines of code.

What are the two main options for building machine learning models in Vertex AI?

Vertex AI allows users to build machine learning models with either AutoML, a no-code solution, or custom training, a code-based solution.

How does Vertex AI help data scientists focus on solving business problems?

AutoML in Vertex AI is easy to use, allowing data scientists to spend more time turning business problems into ML solutions.

Outlines

00:00

🤖 Introduction to Building Machine Learning Models with Vertex AI

This paragraph introduces the process of building machine learning models and how Vertex AI can simplify it for developers, data scientists, and engineers. It discusses the historical development of AI technologies by Google, starting from scikit-learn in 2007 to Vertex AI. The paragraph highlights the challenges faced in developing machine learning models, such as handling large data quantities, selecting the right model, and ensuring sufficient computing power. It also addresses the difficulties in getting models into production, including scalability, monitoring, and continuous integration and deployment. The paragraph concludes by emphasizing the ease of use challenges and how Vertex AI offers a unified platform to address these issues.

Keywords

💡Machine Learning

Machine Learning is a subset of artificial intelligence that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. In the context of the video, machine learning is the core technology that Google has been investing in and applying to its products and services. It's the fundamental process that allows for the creation of intelligent systems capable of tasks like recognizing images, translating languages, and predicting outcomes.

💡Vertex AI

Vertex AI is Google's unified platform for machine learning that simplifies the process of building, deploying, and managing AI models. It addresses various challenges faced by ML developers, data scientists, and data engineers by providing a comprehensive set of tools and services. The video emphasizes how Vertex AI can streamline the workflow, making it easier to handle data, choose the right model, and manage computing resources.

💡Data

Data is the raw material that feeds machine learning models. The video discusses the challenge of handling large quantities of data, which is essential for training accurate and effective ML models. It also mentions the data readiness stage, where users upload data from various sources to prepare it for model training.

💡Model

A model in machine learning refers to the algorithms that are trained on data to make predictions or decisions. The video discusses the importance of selecting the right machine learning model to train the data, which is a critical step in developing effective AI solutions.

💡Computing Power

Computing power is the hardware capability required to process and analyze large datasets quickly and efficiently. The video highlights the challenge of harnessing the necessary computing power for training machine learning models, which is a resource-intensive process.

💡Production

In the context of machine learning, production refers to the deployment of trained models into real-world applications. The video discusses the production challenges such as scalability, monitoring, and continuous integration and delivery, which are crucial for successful implementation of ML models.

💡Gartner

Gartner is a leading technology research and consulting firm mentioned in the video to highlight a statistic about the challenges of enterprise ML projects. It states that only half of these projects make it past the pilot phase, emphasizing the difficulties in transitioning from development to production.

💡Ease of Use

Ease of use refers to how user-friendly a tool or platform is. The video discusses the challenges many tools present to data scientists due to their requirement for advanced coding skills, which can detract from the core tasks of model configuration and development. Vertex AI aims to address this by offering a more user-friendly experience.

💡Unified Workflow

A unified workflow in the context of Vertex AI refers to an integrated set of processes and tools that allow for a seamless transition from data preparation to model deployment. The video emphasizes how Vertex AI provides a single platform to manage all aspects of the machine learning lifecycle, from data readiness to model deployment and monitoring.

💡AutoML

AutoML, or Automated Machine Learning, is a technology that allows users to develop machine learning models with minimal coding. The video mentions AutoML as an easy-to-use solution within Vertex AI that lets data scientists focus more on solving business problems and less on the technical details of model development.

💡Custom Training

Custom Training refers to the process where data scientists have full control over the development environment and can fine-tune every aspect of the machine learning model training process. The video contrasts this with AutoML, highlighting how Vertex AI caters to both users who prefer a no-code solution and those who require a more hands-on approach.

Highlights

Google has been investing in big data and AI since 2007.

Google applies AI technologies to products like Gmail, Google Maps, Google Photos, and Google Translate.

Developing AI technologies involves challenges such as handling large data, choosing the right model, and computing power.

Production challenges include scalability, monitoring, and continuous integration and deployment.

Gartner reports that only half of Enterprise ML projects get past the pilot phase.

Ease of use is a challenge as many tools require advanced coding skills.

Vertex AI is Google's solution to production and ease of use challenges in ML.

Vertex AI is a unified platform for the ML ecosystem and workflow.

Users can upload data from various sources in the data readiness stage.

Feature readiness stage allows users to create and share features using the feature store.

Vertex AI supports training, hyperparameter tuning, and experimenting with different models.

Deployment and model monitoring are facilitated by setting up pipelines for continuous improvements.

Vertex AI offers both AutoML for ease of use and custom training for full control.

The unified platform approach offers seamless, scalable, sustainable, and speedy ML model development.

Vertex AI provides a smooth user experience from data uploading to model production.

ML Ops by Vertex AI helps monitor and manage ML production, scaling storage and computing power automatically.

Artifacts and features created with Vertex AI can be reused and shared.

Vertex AI produces models with 80 percent fewer lines of code than competitors.

Transcripts

now that you've got a better

understanding of what machine learning

is let's explore what it takes to build

a machine learning model and how vertex

AI can make that process easier for ML

developers data scientists and data

engineers

for years now Google has invested time

and resources into developing big data

and AI

Google has developed key Technologies

and products from its roots in the

development of scikit-learn back in 2007

to vertex AI today

as an AI first company Google has

applied AI Technologies to many of its

products and services like Gmail Google

Maps Google photos and Google Translate

but developing these Technologies

doesn't come without challenges

especially when it involves developing

machine learning models and putting them

into production

some traditional challenges include

determining how to handle large

quantities of data determining the right

machine learning model to train the data

and harnessing the required amount of

computing power

then there are challenges around getting

ml models into production

production challenges require

scalability monitoring and continuous

integration and continuous delivery or

deployment

in fact according to the technological

research and consulting firm Gartner

only half of Enterprise ml projects get

past the pilot phase

and finally there are ease of use

challenges

many tools in the market require

Advanced coding skills which can take a

data scientist's Focus away from model

configuration

and without a unified workflow data

scientists often have difficulties

finding tools

Google's solution to many of the

production and ease of use challenges is

vertex AI a unified platform that brings

all the components of the machine

learning ecosystem and workflow together

so what exactly does a unified platform

mean

in the case of vertex AI it means having

one digital experience to create manage

and deploy models over time and at scale

for example during the data Readiness

stage users can upload data from

wherever it's stored cloud storage

bigquery or a local machine

then during the feature Readiness stage

users can create features which are the

process data that will be put into the

model and then share them with others by

using the feature store

after that it's time for training and

Hyper parameter tuning this means that

when the data is ready users can

experiment with different models and

adjust hyper parameters

and finally during deployment and model

monitoring users can set up the pipeline

to transform the model into production

by automatically monitoring and

Performing continuous improvements

and to refer back to the different

options that we explored earlier vertex

AI allows users to build machine

learning models with either automl and

no code solution or custom Training a

code-based solution

automill is easy to use and lets data

scientists spend more time turning

business problems into ml Solutions

while custom Training enables data

scientists to have full control over the

development environment and process

being able to perform such a wide range

of tasks in one unified platform has

many benefits

this can be summarized with four s's

it's seamless vertex AI provides a

smooth user experience from uploading

preparing data all the way to model

training and production

it's scalable the machine learning

operations ml Ops provided by vertex AI

helps to Monitor and manage the ml

production and therefore scale the

storage and computing power

automatically

it's sustainable all of the artifacts

and features created with vertex AI can

be reused and shared and it's Speedy

vertex AI produces models that have 80

percent fewer lines of code than

competitors