This section includes relevant posts for familiarising with the dev environment on Google Cloud. Some of these posts are also to some extent, agnostic to a particular cloud service provider, and do translate well into other services and use cases.
For example, Keras and TensorFlow sections are not necessarily tied to Google Services, but they are for sure recommended by Google in their online courses.
This is a summary of the list of products and services, provided by Google Cloud Provider (GCP). They will be categorized into different categories, depending on their use case. Storage Data Processing Development Machine Learning Products Notable ones. Bigquery, DataFlow, Pubsub.
It is important to understand the differences between the service models, in order to figure out how the available software or cloud services work at their fundamentals and who will utilize them, and for what purposes. Basic Service Models There are four main service models: 1. Infrastructure as a Service (IaaS) : The Cloud Service … Read more
To understand the differences between the Database offerings, one has to have a basic understanding of how Databases work. I have narrowed it down to be able to grasp the following bullet points: What is SQL? Serverless vs. Managed Instance For Managed vs. Server Instance, I’ll just drop this image here:
Here I am listing the material needed to be able to complete the MLE exam. Most of these include an understanding of Google Cloud Products and Services, and when to use them. Hence, I will do concepts and product breakdowns. I hope this will help you on your way! NOTE, it is important understand the … Read more
This is how I practice for the GCP Machine Learning Engineer (MLE) certification. To Start, there are a multitude of sources out there that can help you get started with practicing. Before we dive into the practical steps, I’d like to first highlight why this certification is useful. Why MLE? Machine Learning Engineering is one … Read more
Kubeflow is a standardized platform for building ML pipelines. It’s an open source ML toolkit, built on top of Kubernetes. Essentially, it translates Data Science work into Kubernetes jobs, and provides a cloud-native, multi-cloud, interface for ML libraries, frameworks, pipelines, and notebooks. This makes it simple to deploy ML pipelines via the containerized approach. The … Read more
Takes into account the model complexity when calculating the error. It’s a major field of ML research, but we are going to focus on L1 and L2 regularization. L2 vs L1 Regularization L1, and L2 regularization are so called parameter norm penalties. They both aim to penalize the loss function by introducing coefficients with different … Read more
Keras is a deep learning API developed by Google for implementing Neural Networks (NNs). It’s a high level Python API, meant to simplify NN implementation. Kerals calls itself “an API for human beings”, meaning that it aims to be intuitive to humans. More specifically, the Keras framework is implemented with the purpose of minimizing the … Read more
Introduction TensorFlow is an open-source, high performance library for numerical computation that uses directed acyclic graphs (DAGs). DAGs are simply graphs that flows in one direction, and can be used to represent various computations, such as addition, subtraction etc. A tensor is an N-dimensional array of data. It’s currently the most popular ML library on … Read more