Les news de la curation sur des nouveautés et l’humour
B2C is (one of) Microsoft’s offering to allow us programmers to pass the business of managing logins and users over to people who want to be bothered with such things. This post contains very little code, but lots of pictures of configuration screens, that will probably be out of date by the time you read it.
A B2C set-up starts with a tenant. So the first step is to create one.
The Hasura platform’s data microservice provides an HTTP API to query Postgres using GraphQL or JSON in a permission safe way.
You can exploit foreign key constraints in Postgres to query hierarchical data in a single request. For example, you can run this query to fetch “albums” and all their “tracks” (provided the “track” table has a foreign key to the “album” table):
The principle of least privilege is key when it comes to securing your infrastructure on AWS. For example, an engineer should only be able to control EC2 instances that are in scope for their day-to-day work. But how do you make sure an engineer is only allowed to …
As illustrated in the following figure you can restrict access to EC2 instances, EBS volumes, and EBS snapshots by making use of …
The series so far:
We all know the practice of continuous integration.
One of the common pitfalls of CI is that the build status is not monitored and not treated as one of the top priorities for the team.
Planning for the launch of your website is an exciting time, but it can also be stressful if you start thinking about what could go wrong. The best way to make sure your first impression stays in people’s heads for the right reasons is to prepare.
First things first, you’ll want to access your website behind a firewall, on a staging server, or locally with CrossBrowserTesting’s secure tunnel technology to make sure what you’re testing is for your eyes only.
I am excited to share my experience with Spark Streaming, a tool which I am playing with on my own. Before we get started, let’s have a sneak peak at the code that lets you watch some data stream through a sample application.
from operator import add, sub
from time import sleep
from pyspark import SparkContext
from pyspark.streaming import StreamingContext # Set up the Spark context and the streaming context
sc = SparkContext(appName="PysparkNotebook")
ssc = StreamingContext(sc, 1) # Input data
rddQueue = []
for i in range(5): rddQueue += [ssc.sparkContext.parallelize([i, i+1])] inputStream = ssc.queueStream(rddQueue) inputStream.map(lambda x: "Input: " + str(x)).pprint()
inputStream.reduce(add) .map(lambda x: "Output: " + str(x)) .pprint() ssc.start()
sleep(5)
ssc.stop(stopSparkContext=True, stopGraceFully=True)Spark Streaming has a different view of data than Spark. In non-streaming Spark, all data is put into a Resilient Distributed Dataset, or RDD. That isn’t good enough for streaming. In Spark Streaming, the main noun is DStream — Discretized Stream. Thats basically the sequence of RDDs. The verbs are pretty much the same thing — the way we have actions and transformations with RDDs, we also have actions and transformations with DStreams.
According to Gartner, smart cities will be using about 1.39 billion connected cars, IoT sensors, and devices by 2020. The analysis of location and behavior patterns within cities will allow optimization of traffic, better planning decisions, and smarter advertising. For example, the analysis of GPS car data can allow cities to optimize traffic flows based on real-time traffic information. Telecom companies are using mobile phone location data to provide insights by identifying and predicting the location activity trends and patterns of a population in a large metropolitan area. The application of Machine Learning to geolocation data is being used in telecom, travel, marketing, and manufacturing to identify patterns and trends for services such as recommendations, anomaly detection, and fraud.
In this article, we discuss using Spark Structured Streaming in a data processing pipeline for cluster analysis on Uber event data to detect and visualize popular Uber locations.
Machine vision, or computer vision, is a popular research topic in artificial intelligence (AI) that has been around for many years. However, machine vision still remains as one of the biggest challenges in AI. In this article, we will explore the use of deep neural networks to address some of the fundamental challenges of computer vision. In particular, we will be looking at applications such as network compression, fine-grained image classification, captioning, texture synthesis, image search, and object tracking.
Even though deep neural networks feature incredible performance, their demands for computing power and storage pose a significant challenge to their deployment in actual application. Research shows that the parameters used in a neural network can be hugely redundant. Therefore, a lot of work is put into increasing accuracy while also decreasing the complexity of the network.
In this article, we will study a comparison between Deep Learning and Machine Learning. We will also learn about them individually. We will also cover their differences on various points. Along with a Deep Learning and Machine Learning comparison, we will also study their future trends.
Generally, to implement Artificial Intelligence, we use Machine Learning. We have several algorithms that are used for Machine Learning. For example:
