<feed xmlns="http://www.w3.org/2005/Atom"> <id>https://.ryannazareth.com/</id><title>Ryan Nazareth</title><subtitle>Blogs on all things Data, ML and the Cloud</subtitle> <updated>2022-09-01T01:34:41+01:00</updated> <author> <name>Mr Ryan Nazareth</name> <uri>https://.ryannazareth.com/</uri> </author><link rel="self" type="application/atom+xml" href="https://.ryannazareth.com/feed.xml"/><link rel="alternate" type="text/html" hreflang="en" href="https://.ryannazareth.com/"/> <generator uri="https://jekyllrb.com/" version="4.2.2">Jekyll</generator> <rights> © 2022 Mr Ryan Nazareth </rights> <icon>//assets/img/favicons/favicon.ico</icon> <logo>//assets/img/favicons/favicon-96x96.png</logo> <entry><title>SQL Unit Testing with tSQLt</title><link href="https://.ryannazareth.com/posts/tsqlt-unit-test-blog/" rel="alternate" type="text/html" title="SQL Unit Testing with tSQLt" /><published>2022-08-30T00:00:00+01:00</published> <updated>2022-08-30T13:24:20+01:00</updated> <id>https://.ryannazareth.com/posts/tsqlt-unit-test-blog/</id> <content src="https://.ryannazareth.com/posts/tsqlt-unit-test-blog/" /> <author> <name>Mr Ryan Nazareth</name> </author> <category term="sql" /> <category term="testing" /> <category term="tsqlt" /> <summary> This demo will introduce a package tSQLt, whcih can be used to unit test tsqlt code [1]. All the code used in the examples is accessible from this github repository. First we will restore the Adventure Works sample database. This database is based on Adventure Works Cycles, the fictitious multinational manufacturing company created by Microsoft in 2010 [2]. The company manufactures and se... </summary> </entry> <entry><title>AWS Personalize for recommending new movies to users, based on their ratings to other similar movies</title><link href="https://.ryannazareth.com/posts/aws-personalize/" rel="alternate" type="text/html" title="AWS Personalize for recommending new movies to users, based on their ratings to other similar movies" /><published>2022-08-10T00:00:00+01:00</published> <updated>2022-08-10T00:00:00+01:00</updated> <id>https://.ryannazareth.com/posts/aws-personalize/</id> <content src="https://.ryannazareth.com/posts/aws-personalize/" /> <author> <name>Mr Ryan Nazareth</name> </author> <category term="machine learning" /> <category term="personalize" /> <category term="recommendation" /> <category term="movies" /> <summary> Amazon Personalize allows developers with no prior machine learning experience to easily build sophisticated personalization capabilities into their applications. With Personalize, you provide an activity stream from your application, as well as an inventory of the items you want to recommend, and Personalize will process the data to train a personalization model that is customized for your... </summary> </entry> <entry><title>AWS Fraud Detector for classifying fraudulent online registered accounts for simulated data from Kaggle</title><link href="https://.ryannazareth.com/posts/aws-fraud/" rel="alternate" type="text/html" title="AWS Fraud Detector for classifying fraudulent online registered accounts for simulated data from Kaggle" /><published>2022-07-27T00:00:00+01:00</published> <updated>2022-07-27T00:00:00+01:00</updated> <id>https://.ryannazareth.com/posts/aws-fraud/</id> <content src="https://.ryannazareth.com/posts/aws-fraud/" /> <author> <name>Mr Ryan Nazareth</name> </author> <category term="fraud" /> <category term="machine learning" /> <category term="cloudformation" /> <category term="api gateway" /> <summary> Amazon Fraud Detector is a fully managed service that can identify potentially fraudulent online activities. These can be situations such as the creation of fake accounts or online payment fraud. Amazon Fraud Detector automates the time-consuming and expensive steps to build, train, and deploy an ML model for fraud detection. It customizes each model it creates to your dataset, making the ... </summary> </entry> <entry><title>Forecasting American Football Fever in AWS using Wikipedia Page Hits</title><link href="https://.ryannazareth.com/posts/aws-forecast/" rel="alternate" type="text/html" title="Forecasting American Football Fever in AWS using Wikipedia Page Hits" /><published>2022-07-16T00:00:00+01:00</published> <updated>2022-07-16T00:00:00+01:00</updated> <id>https://.ryannazareth.com/posts/aws-forecast/</id> <content src="https://.ryannazareth.com/posts/aws-forecast/" /> <author> <name>Mr Ryan Nazareth</name> </author> <category term="forecast" /> <category term="machine learning" /> <category term="time series" /> <summary> Forecast is a fully managed AWS service for time-series analysis. It can select from multiple time series prediction models to find the best one for your particular data sets. Amazon Forecast automatically examines the historical data provided (including any additional features that can impact the forecast), and identify what is meaningful, and produce a forecasting model capable of making ... </summary> </entry> <entry><title>Setting up AWS Code Pipeline to automate deployment of tweets streaming application</title><link href="https://.ryannazareth.com/posts/code-pipeline/" rel="alternate" type="text/html" title="Setting up AWS Code Pipeline to automate deployment of tweets streaming application" /><published>2022-07-02T00:00:00+01:00</published> <updated>2022-08-31T20:26:12+01:00</updated> <id>https://.ryannazareth.com/posts/code-pipeline/</id> <content src="https://.ryannazareth.com/posts/code-pipeline/" /> <author> <name>Mr Ryan Nazareth</name> </author> <category term="serverless" /> <category term="lambda" /> <category term="code pipeline" /> <category term="ci-cd" /> <summary> In this example, we will configure AWS CodePipeline to build an ECR image and deploy the latest version to lambda container. The application code will stream tweets using python tweepy library. First we need to setup code pipeline and the various stages to deploy application code to lambda image which will stream tweets when invoked. Typically, a codepipeline job contains the following s... </summary> </entry> </feed>
