Welcome to our overview of Google Cloud's Vertex AI.
In this lesson, we're going to cover what vertex AI is, the key benefits, and how it's empowering
businesses to harness the power of AI without deep technical expertise.
So what exactly is vertex AI?
In a nutshell, it's a unified platform that allows you to customize and deploy generative models with
a variety of tools.
It simplifies the entire machine learning workflow from data management to model deployment and monitoring,
making AI more accessible than ever before.
What are the vertex AI?
Key features to start off, it offers access to Gemini's multimodal models, foundation models, and
others that you can find at the Modal Garden, an open and integrated AI platform.
MLOps tools model fine tuning and others for businesses looking to leverage AI.
Vertex Tax significantly reduces the complexity of building and deploying machine learning models with
vertex AI.
You can train high quality models with minimal coding.
This means your team can focus on defining the business problem and leveraging insights, rather than
getting bogged down in technical weeds.
Moreover, vertex AI enables seamless collaboration between data scientists, analysis, and business
stakeholders.
It provides a centralized platform for data management, model experimentation, and results sharing.
This streamlined workflow accelerates time to value and ensures alignment across teams.
To make the potential of vertex AI more concrete, let's look at a couple of real world use cases.
Imagine you're a content creator looking to streamline your workflow with vertex AI studio.
You can rapidly prototype and test generative AI models directly in the Google Cloud Console.
This powerful tool allows you to experiment with different prompts, fine tune foundation models, and
even convert between speech and text seamlessly.
Or, let's say you're a business analyst aiming to derive insights from large volumes of customer feedback
with vertex AI's generative AI support.
You can create sophisticated text prompts for tasks like classification, summarization, and extraction.
For instance, you can use classification to categorize customer reviews as positive or negative based
on sentiment analysis.
Summarization can help condense lengthy feedback reports into concise summaries that highlight key points.
Extraction allows you to pull out specific data points such as product features mentioned in reviews.
So how does vertex AI stand out in the field of AI and machine learning platforms?
There are five top AI cloud vendors.
Amazon SageMaker.
IBM Watson Studio.
Microsoft Azure Machine Learning.
Vertex AI, and Open source.
Vertex AI sits on top of Google Cloud, which can be compared to other cloud products such as Microsoft
Azure, AWS, etc..
A key difference from vertex AI is its emphasis on accessibility and ease of use of the most advanced
AI algorithms.
With this tool, you don't need to be a machine learning PhD to build powerful models.
The platform provides pre-built algorithms and intuitive interfaces that guide you through the model
building process.
Also, the platform stands out for its auto ML feature, something that also sets vertex AI apart is
its seamless integration with the broader Google Cloud ecosystem.
You can easily connect to tools like BigQuery or data processing, cloud storage for data management,
and cloud functions for serverless deployment.
This means you can build end to end AI solutions without the need of stitching the tools together.
Now let's explore the tools documentation.
It's essential to dive into the official documentation for in-depth learning and troubleshooting.
Also, it provides multiple resources that cover every aspect of the platform with video or step by
step tutorials.
In the overview section, we have a high level introduction to vertex AI.
Quickly access Gemini Generative AI and Model Garden.
Clicking on any of these will send you to a brief tutorial on the feature, and right below you'll find
other documentation resources.
Heading over to the Introduction to Vertex AI section, you'll find a detailed overview of vertex AI's
capabilities and how it supports end to end machine learning workflow.
It explains key concepts like AutoML, custom training, the model garden, and generative AI options.
This section also describes vertex ai's role in each stage of the ML process, from data preparation
to deployment.
Next, explore the beginner's Guide section.
Train an AI model mode tutorial covers training models for various data types including tabular, text,
image, and video.
If you're interested in custom model training, the custom model section is a must read.
It dives deep into the process of training your own models on vertex AI.
Explaining best practices for preparing training code and data.
You'll also learn about the differences between using prebuilt and custom containers for training.
To navigate the documentation effectively, use the left sidebar to explore different sections and find
relevant guides.
There are quite a few sections to explore, so take advantage of the search function to quickly find
topics or code samples.
Don't forget to bookmark key pages and tutorials for easy reference.
As you work on your own projects.
We recommend that if you want to dive deep into vertex AI after our classes, this documentation will
be your best resource.
In addition to the documentation, there are plenty of resources to support your vertex AI journey.
Google cloud offers hands on labs and sample projects that allow you to experiment with the platform
in a guided environment.
These practical exercises are a great way to reinforce your learning and explore different use cases.
There is also an active community of vertex AI users who share best practices and support each other
through these forums and events.
With vertex AI.
Google Cloud is making it easier for businesses to bring the power of machine learning, regardless
of their technical expertise, to their business by simplifying the end to end workflow, providing
accessible tools like AutoML, and seamlessly integrating with the broader Google Cloud ecosystem.
Vertex AI is empowering organizations to solve complex problems with innovation, and the good news
is, you don't need to spend tons of hours learning a new tool to start getting value out of vertex
AI.
In our next lesson, we'll dive deeper into vertex AI's features, functions, and practical use cases.