In this blog, I am going to explain some of the best practices for building a machine learning system in Google Cloud Platform.  We'll start by showing how to understand and formulate the problem and end with tips for training and deploying the model. We'll also discuss required GCP tools, data processing in GCP, and data validation.

Most tech savvy people these days know about the potential for cloud computing technology and how the cloud has already affected businesses by effectively storing data and balancing the existing workloads. Google Cloud Platform has been adopted by many organizations for building AI solutions, mostly because it originates with one of the most prominent IT companies - Google. In part 1 of this series, I provided a review of some of Google's AI products. Today, we'll review the remainder.

Nowadays, using the cloud to develop artificial intelligence apps is common for medium and large-sized companies, and compared to other cloud service providers, Google has a big share of the market.

In part 1 of this tutorial, we explained the advantages of and proposed a methodology for using DCNNs for time series analysis by converting time series into gray-scale images. In part 2, we defined a Python class and various methods to perform data processing. In our third and final part, we will explain the topology of our model.

In our last post, we discussed the importance of developing a strong forecasting engine to predict future energy consumption based on data from past energy consumption. We then discussed the suitability of using a Deep Convolutional Neural Network (DCNN) to form accurate predictions and examined how to use this model by converting a series of load data into images.

Have you ever wondered how to convert a prediction problem into a new format so that you can solve it using available strong forecasting engines? In Part I of this tutorial, I will discuss how to solve one of the most challenging forecasting problems--the next state forecasting trend of electricity consumption--by using a Deep Convolutional Neural Network (DCNN) to process a series of load data that’s been converted into images.

From controlling smart cars on earth to forecasting solar flares in space, machine learning technologies are being more and more widely implemented. Today, we’ll continue last week’s discussion on machine learning applications in retail settings, including their uses in securing payment transactions, improving customer experiences, predicting customer demand, and automating customer service.