which of these is a no-code solution that lets you build your own machine learning models on vertex ai through a point-and-click interface?

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What is Google Cloud Vertex AI?

Vertex AI is an all-in-one platform for data scientists offering every single tool they need to manage, develop, deploy, interpret, and monitor the models.

What is Google Cloud Vertex AI?

by Luca Cavallin

on Jan 27, 2022 in Google Cloud Platform

Over the past few years, Google has introduced many easy-to-use tools to help data scientists and machine learning engineers. Google Colab, TensorFlow, BigQueryML, Cloud AutoML, Cloud AI, and similar other tools have been introduced by Google Cloud to make AI more accessible to organizations.

However, when there are so many AI tools readily available, it becomes a tedious job to use different tools for performing data analysis, models training, deployment, etc. To unify this process, Google has introduced "Vertex AI", which makes all its cloud offerings available under one roof. Let’s have a look at what exactly is Vertex AI, its features, and its use-cases!

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Check it out: https://www.meetup.com/nl-NL/google-cloud-platform-user-group-benelux/.

What is Vertex AI?

Vertex AI is a unified artificial intelligence platform that offers all of Google’s cloud services under one roof. With Vertex AI, you can build ML models or deploy and scale them easily using pre-trained and custom tooling. When you develop ML solutions on Vertex AI, you can leverage AutoML and other advanced ML components to greatly enhance productivity and scalability. Google also focused to make Vertex AI a friendly platform for newbies and a time-saving solution for experts. That’s the reason it can train models easily and requires 80% fewer lines of coding.

Key Features of Vertex AI

Although Vertex AI has tons of features available, here’s the look at some of its key offerings:

Entire ML workflow under one unified UI: Vertex AI provides one unified user interface and API for all AI-related Google Cloud services. For example, within Vertex AI, you can use AutoML to train and compare models and store them in a central model repository.Integrates with all open source frameworks: Vertex AI integrates with commonly used open source frameworks, such as PyTorch and TensorFlow, and it also supports other tools via custom containers.Access to pre-trained APIs for video, vision, and others: Vertex AI makes it easy to integrate video, translation, and natural language processing with existing applications. AutoML empowers engineers to train models customized to meet their business needs with minimal expertise and effort.End-To-End data and AI integration: Vertex AI is integrated with Dataproc, Dataflow and BigQuery natively through Vertex AI Workbench. You can either build/run ML models in BigQuery or you can use export data from BigQuery to Vertex AI Workbench and execute ML models from there.

Use Cases of Vertex AI

Data scientists and ML engineers can take advantage of Vertex AI in many ways. What are the use cases Vertex AI? You can:

Insert data from Cloud Storage and Big Query and use Vertex AI Data Labeling to improve prediction accuracy and interpret high-quality training data.

Use Vertex AI Feature Store (fully-managed feature-rich repository) to serve, reuse, and share ML features.

Use Vertex AI Pipelines to streamline the development and execution of ML processes.

Use Vertex AI Prediction to streamline the deployment and monitoring of models to production.

Use Vertex Explainable AI to get comprehensive model feature attributions and evaluation metrics.

Furthermore, the managed APIs (Vision, Video, NLP…) make it easy for teams without in-depth ML knowledge or dedicated machine learning engineers to add AI capabilities to their applications.

Summary

In a nutshell, Vertex AI is an all-in-one platform for data scientists offering every single tool they need to manage, develop, deploy, interpret, and monitor the models. Without any formal ML training, newbies and experts can quickly start using Vertex AI right away.

Would you like to learn more? Check out this explanatory video by Priyanka Vergadia, Lead Developer Advocate at Google.

by Luca Cavallin

Luca is a Software Engineer and Trainer with full-stack experience ranging from distributed systems to cross-platform apps. He is currently interested in building modern, serverless solutions on Google Cloud using Golang, Rust and React and leveraging SRE and Agile practices. Luca holds 3 Google Cloud certifications, he is part of the Google Developers Experts community and he is the co-organizer of the Google Cloud User Group that Binx.io holds with Google.

linkedin.com/in/https://www.linkedin.com/in/lucavallin/

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MLOps with Vertex AI

In this lab, you will need to automate the model building, training, and deployment to the model endpoint by creating a Vertex training pipeline.

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Deploying Computer Vision Models: Tools & Best Practices

Computer vision models have become insanely sophisticated with a wide variety of use cases enhancing business effectiveness, automating critical decision systems, and so on. But a promising model can turn out to be a costly liability if the model fails to perform as expected in production. Having said that, how we develop and deploy computer…

MLOps Blog

Deploying Computer Vision Models: Tools & Best Practices

25 min Arun C John 24th January, 2023 Computer Vision MLOps

Computer vision models have become insanely sophisticated with a wide variety of use cases enhancing business effectiveness, automating critical decision systems, and so on. But a promising model can turn out to be a costly liability if the model fails to perform as expected in production. Having said that, how we develop and deploy computer vision models matters a lot!

Machine learning engineers are slowly embracing DevOps practices in their model deployment systems, but it doesn’t end there! We also need to consider several aspects like code versioning, deployment environment, continuous training/retraining, production model monitoring, data drift & quality, model features & hyperparameters, and so on. But some of these practices are specific to machine learning systems.

In this article, we will take a look at how we should deploy Computer Vision models while keeping the above-mentioned aspects in mind.

Computer vision model lifecycle

Deploying a computer vision model or rather any machine learning model is a challenge in itself, considering the fact that only a few of the developed models go to continuous production environments.

The CV model lifecycle starts from collecting quality data to preparing the data, training and evaluating the model, deploying the model, and monitoring and re-training it. It can be visualized through the chart shown below:

Computer vision model lifecycle | Source: Author

In this article, we will focus on the deployment phase of computer vision models. You will learn key aspects of model deployment including the tools, best practices, and things to consider when deploying computer vision models.

Learn more

The Life Cycle of a Machine Learning Project: What Are the Stages?

CV model deployment modes, platforms & UI

In this section let’s dive into different ways you can deploy and serve Computer Vision models. The key elements that need to be considered here are:

Deployment modes (with REST/RPC endpoints, on the edge, hybrid)

How they are served to the end-user

Ease of access to hardware and scalability of the deployment platform

Deployment modes (with REST/RPC endpoints, on the edge, hybrid)

Machine learning models hosted on on-premise/cloud platforms are often deployed or rather accessed through API endpoints. APIs such as REST/RPC essentially provide the language and a contract for how two systems interact.

Another mode is to deploy models on edge devices, where the consumption of data through CV/cognitive applications happens at the point of origin. Oftentimes the deployment can go hybrid too, i.e a combination of API end-points as well as edge devices.

How are they served to the end-user

Depending on how the end-user will consume the model, the interfaces can vary. Models may be served to some users through a simple bash command-line interface while others could consume it through an interactive web-based or app-based UI. In most cases, the model can be served through an  API and a downstream application consumes the results.

Ease of access to hardware and scalability of the deployment platform

Just like the options available in terms of UI/UX, a multitude of options are available with the platforms or the hardware where we can deploy the production models. That includes the laptop where the developer often carries out code development, a remote machine, remote Virtual Machines, remote servers where jupyter notebooks are hosted, containers with orchestrators deployed in cloud environments, and so on.

Each of the points mentioned above is elaborated on in the following sections.

CV deployment through API (REST/RPC) endpoints

REST stands for “representational state transfer” (by Roy Fielding). In a nutshell, REST is about a client-server relationship, where data is made available/transferred through simple formats, such as JSON/XML. The “RPC” part stands for “remote procedure call,” and it’s often similar to calling a function in JavaScript, Python, etc. Please read this article for a detailed understanding of REST/RPC protocols.

Source

When an ML deployment API interacts with another system, the touchpoints of this communication are happening through REST/RPC endpoints. For APIs, typically an endpoint consists of a URL of a server or service. It is like a software intermediary that allows two applications to exchange data with each other. It’s also a set of routines, protocols, and tools for building software applications. Some of the popular pretrained CV cloud services are completely served on API endpoints, for example, Google Vision API.

REST simply determines how the API architecture looks. To put this in a simple way, it is a set of rules that developers follow when they create APIs.

Usually, each URL is called a request(API request) while the data sent back to you(mostly as a JSON) is called a response.

sumber : neptune.ai