It's almost exactly a month since I graduated from this program. The past 6-months have been both a fun and thrilling journey. It was indeed a rollercoaster-like experience. Nonetheless, I gained a lot of new insights, experiences, and also friends here. For those who might not know yet, Bangkit is an annual learning program initiated by Google along with its industry partners; Go-Jek, Tokopedia, and Traveloka.
It's been more than 5 years since I graduated from the Bangkit Academy 2021 program and in this article I want to tell you a little about my experience taking the Tensorflow Developer Certificate Exam until I finally passed and got the Tensorflow Developer Certificate. Until this article was published, it has been about 3 months after I got the certification. I hope this article can be useful and useful for students and the general public who want to take the Tensorflow Developer Certificate.
For the first time, Indonesia holds the presidency of the G20. President Joko Widodo received the handover from Italian Prime Minister Mario Draghi at its summit in Rome last year, but a lot has happened in the world since then. And before Indonesia's presidency ends next month, Widodo will welcome the G20 leaders in Bali. The motto for the Indonesia G20 summit is "Pulih Bersama, Bangkit Perkasa" (Recover Together, Recover Stronger). But with world powers divided over the war in Ukraine, is "togetherness" even possible?
R is one of the most widely used programming languages for statistical computing and machine learning. Many data scientists love it, especially for the rich world of packages from tidyverse, an opinionated collection of R packages for data science. Besides the tidyverse, there are over 18,000 open-source packages on CRAN, the package repository for R. RStudio, available as desktop version or on the Google Cloud Marketplace, is a popular Integrated Development Environment (IDE) used by data professionals for visualization and machine learning model development. Once a model has been built successfully, a recurring question among data scientists is: "How do I deploy models written in the R language to production in a scalable, reliable and low-maintenance way?" In this blog post, you will walk through how to use Google Vertex AI to train and deploy enterprise-grade machine learning models built with R. Managing machine learning models on Vertex AI can be done in a variety of ways, including using the User Interface of the Google Cloud Console, API calls, or the Vertex AI SDK for Python.
Vertex AI is an API developed by Google research that consists of AutoML and AI Platform in one place. As we know the AutoML that allows us to train models on different kinds of data like image, video, text data, without writing much code and in AI Platform lets you run custom training code while training the model. Vertex AI provides options for both AutoML training and custom training. We can choose options for training and can easily save and deploy the models, and request vertex AI to predict the values according to the model which we have trained.