In this blog post I’ll iterate over a simple script and refactor it each time to improve its performance. Before diving into the details I would quote a couple of well known individuals from the GO Community. “You want to write code that is optimized for correctness. Don’t make coding decisions based on what you think might perform better. You must benchmark or profile to know if code is not fast enough.

TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks and it is used for machine learning applications such as neural networks. Tensorflow has grown to become one of the widely adopted ML platforms in the world. TensorFlow offers multiple levels of abstraction so you can choose the right one for your needs. You can build and train models by using the high-level Keras API, which makes getting started with TensorFlow and machine learning easy.

gRPC describes itself as: gRPC is a modern open source high performance RPC framework that can run in any environment. It can efficiently connect services in and across data centers with pluggable support for load balancing, tracing, health checking and authentication. It is also applicable in last mile of distributed computing to connect devices, mobile applications and browsers to backend services. This is Part 3 of a series of three articles describing gRPC functionality.

Let’s assume that the dev teams have been asked to extend the services and there are multiple teams working on it. Often, the teams prefer a specific programming language for a certain job. Luckily, gRPC is supported by a broad range of languages. We are going to extend the service to communicate to a storage service, but this time the service will be written in Python. This simple service can be written in whatever language of choice but in order to showcase the gRPC functionality in Python we could say that it is a project specification.

As we are moving more towards distributed systems and micro-services, the way the services are communicating between each other becomes increasingly important. As you can imagine, the request-response delay is multiplied by the number micro-services running in the backend. Until recently, the industry standard was JSON over REST, which is a software architectural style that defines a set of recommendations for designing loosely coupled applications that use the HTTP protocol for data transmission.

Kubernetes is a powerful and highly extensible system for managing containerized workloads and services. It has two main components, the Master Components and the Node Componenets, and extensions. The Master Components: # Kube-apiserver, the API server is the front end for the Kubernetes control plane. etcd, is a highly-available key value store used as Kubernetes’ backing store for all cluster data. kube-scheduler, is the component of the master that watches newly created pods that have no node assigned, and selects a node for them to run on.

In this article, I make the assumption that you know how to setup the DialogFlow agent by using the intents and entities. I’ll just give a short description of the DialogFlow without providing too much details on how to set it up. This is the third article and last of the series, where I show another options to consume the data from a REST service. If in the first two posts, I covered the standard web pages and the mobile application using Flutter, this time I’ll do the third option which is by Voice.