Building ML models with Vertex AI
Summary
TLDRGoogle'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
- đ€ **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.
- đ **AI Across Google**: Google applies AI technologies in various products like Gmail, Google Maps, Google Photos, and Google Translate.
- đ **Challenges in ML**: Traditional challenges in machine learning include handling large data, choosing the right model, and computing power.
- đ **Production Challenges**: Challenges in getting ML models into production include scalability, monitoring, and continuous integration and deployment.
- đ **Gartner's Insight**: According to Gartner, only half of Enterprise ML projects make it past the pilot phase.
- đ ïž **Vertex AI Solution**: Vertex AI addresses production and ease of use challenges by providing a unified platform for ML workflows.
- đ§ **Unified Platform Definition**: Vertex AI offers a single digital experience for creating, managing, and deploying ML models at scale.
- đ **Data and Feature Readiness**: Users can upload data and create features using the feature store for model input.
- đŻ **Training and Tuning**: Vertex AI facilitates training, hyperparameter tuning, and experimentation with different models.
- đ **Deployment and Monitoring**: The platform supports setting up pipelines for model deployment, monitoring, and continuous improvements.
- đ©âđ» **Two Approaches**: Vertex AI allows building ML models through AutoML (no-code) for ease of use, or custom training for full control.
- đ **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
đ€ 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.
Mindmap
Keywords
đĄMachine Learning
đĄVertex AI
đĄData
đĄModel
đĄComputing Power
đĄProduction
đĄGartner
đĄEase of Use
đĄUnified Workflow
đĄAutoML
đĄCustom Training
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
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