Written by Hammad UmerSoftware Engineer
The MLOps acts as a bridge between data scientists and the production team. It is applied and designed to eliminate all the waste and make the machine learning system more scalable by providing automation and producing highly consistent insights from the ML model. While many different platforms can help achieve MLOps, Vertex AI, the GCP Tool, is the best feature to deploy it.
In this blog, you'll learn how to use Vertex AI — Google Cloud's newly announced managed ML platform to build end-to-end ML workflows. In addition, you'll gain insight into going from raw data to a deployed model and leave this workshop ready to develop and produce your ML projects with Vertex AI. Read ahead!
Vertex AI helps bring AutoML and AI Platform together to make a unified API, user interface, and client library. The AutoML also helps train models on image, tabular, text, and video datasets without writing code. Another interesting feature to note is that AI Platform training enables you to run custom training code that increases the efficiency of manifolds. With Vertex AI, AutoML training and custom training are available. Duirng training, you can save models, deploy models, and request predictions with Vertex AI.
For those already familiarized with the AI Platform, Vertex AI is a rebranding of the AI Platform. Additionally, Vertex AI adds new operational features, including Vertex Experiments to track, analyze and discover ML experiments for automated selection of best model candidates, and more. We have heard from the customers that they're interested in an ML Platform where they can manage datasets, models, retrain models using an automated ML pipeline, deploy model versions in a scalable way and split traffic depending on specific requirements. So then, if you are also interested in one of these features, give it a try on Vertex AI.
You can use Vertex AI to manage the following stages in the ML workflow:
Create a dataset and upload data
Train an ML model on your data
Train the model
Evaluate model accuracy
Deploy your trained model to an endpoint for serving predictions
Send prediction requests to your endpoint
Specify a prediction traffic split in your endpoint
Manage your models and endpoints
Vertex AI supports managed data sets for vision, text, speech, forecasting, bot, and more. The Vertex AI enables users to choose the type of data, and each category has multiple sub-categories. In all, it eases the process of uploading the data and organizing it on the cloud.
One can do dataset creation, model training, endpoint instantiation, model deployment. However, it is better to construct a pipeline that does all these jobs consistently. In addition, AutoML also likely guarantees for you to have the top model.
In this blog, we have explained the functions of Vertex AI. You may be interested to know how we can achieve Machine Learning Operations (MLOps) with Kubeflow, watch this Webcast to know more. Royal Cyber experts have in-depth experience to help you with CI/CD pipeline and automate machine learning model deployment. With unparalleled support, our experts can set up an automated pipeline for model retraining. For more information on MLOps and its components, reach out to our MLOps experts.