It’s 2021, everything is getting replaced by a technologically emerged ecosystem, and mobile apps are one of the best examples to convey this message.
Though bypassing times, the development structure of mobile app has also been changed, but if you still follow the same process to create a mobile app for your business, then you are losing a ton of opportunities by not giving top-notch mobile experience to your users, which your competitors are doing.
You are about to lose potential existing customers you have, so what’s the ideal solution to build a successful mobile app in 2021?
This article will discuss how to build a mobile app in 2021 to help out many small businesses, startups & entrepreneurs by simplifying the mobile app development process for their business.
The first thing is to EVALUATE your mobile app IDEA means how your mobile app will change your target audience’s life and why your mobile app only can be the solution to their problem.
Now you have proposed a solution to a specific audience group, now start to think about the mobile app functionalities, the features would be in it, and simple to understand user interface with impressive UI designs.
From designing to development, everything is covered at this point; now, focus on a prelaunch marketing plan to create hype for your mobile app’s targeted audience, which will help you score initial downloads.
Boom, you are about to cross a particular download to generate a specific revenue through your mobile app.
#create an app in 2021 #process to create an app in 2021 #a complete process to create an app in 2021 #complete process to create an app in 2021 #process to create an app #complete process to create an app
Unbounded data refers to continuous, never-ending data streams with no beginning or end. They are made available over time. Anyone who wishes to act upon them can do without downloading them first.
As Martin Kleppmann stated in his famous book, unbounded data will never “complete” in any meaningful way.
“In reality, a lot of data is unbounded because it arrives gradually over time: your users produced data yesterday and today, and they will continue to produce more data tomorrow. Unless you go out of business, this process never ends, and so the dataset is never “complete” in any meaningful way.”
— Martin Kleppmann, Designing Data-Intensive Applications
Processing unbounded data requires an entirely different approach than its counterpart, batch processing. This article summarises the value of unbounded data and how you can build systems to harness the power of real-time data.
#stream-processing #software-architecture #event-driven-architecture #data-processing #data-analysis #big-data-processing #real-time-processing #data-storage
Working with natural language data can often be challenging due to its lack of structure. Most data scientists, analysts and product managers are familiar with structured tables, consisting of rows and columns, but less familiar with unstructured documents, consisting of sentences and words. For this reason, knowing how to approach a natural language dataset can be quite challenging. In this post I want to demonstrate how you can use the awesome Python packages, spaCy and Pandas, to structure natural language and extract interesting insights quickly.
spaCy is a very popular Python package for advanced NLP — I have a beginner friendly introduction to NLP with SpaCy here. spaCy is the perfect toolkit for applied data scientists when working on NLP projects. The api is very intuitive, the package is blazing fast and it is very well documented. It’s probably fair to say that it is the best general purpose package for NLP available. Before diving into structuring NLP data, it is useful to get familiar with the basics of the spaCy library and api.
After installing the package, you can load a model (in this case I am loading the simple Engilsh model, which is optimized for efficiency rather than accuracy) — i.e. the underlying neural network has fewer parameters.
import spacy nlp = spacy.load("en_core_web_sm")
We instantiate this model as nlp by convention. Throughout this post I’ll work with this dataset of famous motivational quotes. Let’s apply the nlp model to a single quote from the data and store it in a variable.
#analytics #nlp #machine-learning #data-science #structured natural language processing with pandas and spacy #natural language processing
With great power comes great responsibility.
More and more organisations are moving towards a DevOps based organisational model, putting more and more responsibility into the hands of the teams delivering software. As part of that change - and the need due to the markets moving faster and faster - more and more organisations are investing into means to release more milestones into production faster. Therefore one of the main goals within these organisations is to automate, audit, secure and ensure correct repeatability of actions.
Barriers to creating a harmonious flow are found in organizations that require more stringent verification methods on their software release mechanisms. One of the more common requirements is that of the four-eyes principle, requiring extra approval controls before release.
Let’s look at defining and implementing the four-eyes principle in a DevOps automation process.
If we look around the world we’ll find the four-eyes principle as an integral part of many business domains. Before we look closer at implementing the solution for this principle, let’s take a look at it’s definition by the United Nations Industrial Development Organization.
_The four-eyes principle means that a certain activity, i.e. a decision, transaction, etc., must be approved by at least two people. This controlling mechanism is used to facilitate delegation of authority and increase transparency. The processes in UNIDO’s new business model are based on the four-eyes principle, which are facilitated by electronic approvals and workflows in the ERP system. This approach not only ensures the efficiency of processes by enabling fast decision-making while ensuring effective control and monitoring, but also brings about cultural change. Staff members are able to perform these processes irrespective whether they are at Headquarters or in the field. _
There are two really interesting (highlighted in bold text) fragments in this definition that we’ll be applying in our implementation example:
Both of these aspects, automated approval using a rule based system and process automation workflows, can be applied to our software DevOps delivery model.
#tutorial #devops #jboss #red hat #developer #operations #process automation #workshop #devops process #devops processes
Gone are the days when the word “DATA” meant a structured set of information in terms of numbers, categories, names, etc represented in tabular format and monopoly of Relational Database Management Systems (RDBMS). Advent in technology has caused a torrent of unstructured data such as audio files/signals, videos, text, images, and much more and that has led to the discovery of new-age data processing techniques and algorithms.
The Plethora of data sources offers unprecedented opportunities to acquire a deeper and holistic understanding of various concepts and make informed decisions.
Our world is now “digitized” and “datafied”. Whether you like it or not, the Internet might know you better than your loved ones.
Statistics mentioned below should be able to give you a feel of the volume of data we are generating and the immense opportunities and challenges it offers.
Analyzing text data is now the cornerstone of analytics in all domains of industry. For e.g. analyzing customer reviews/feedback on platforms such as Facebook, twitter, blogs, websites , etc offers crucial information on customer sentiments and it might even inspire initiate new service or a product.
“ My objective, through this article is to pique your interest in NLP and inspire you to explore the depth of concepts such as Vectorization, Topic modeling and feature engineering, etc. ”
Prediction using unstructured data can get pretty complex process and its hard to cover all topics in a single article, therefore I would be focusing on the per-processing phase, for now. Topics such as vectorization, Topic modeling, etc. would be covered in my upcoming articles.
By the end of this article, you should -
A) Understand concept of Natural Language Processing.
B) Learn basics of Spacy and NLTK library in python.
C) Learn techniques of text cleaning and Exploratory Data Analysis (EDA) of Text data.
Concepts discussed in the article will largely be based on the below topics -
According to projections by IDC, 80 % of data generated by 2025 will be in unstructured format, which means it would be text heavy and does not have any predefined data model. That’s where NLP comes into play to give context to massive unstructured data, which helps find the needle of insight in the haystack of information.
“Natural Language Processing refers to the host of techniques adopted to ingest and transform text data to a shape and form which computers can process.”
#nlp #text-processing #textblob #spacy #naturallanguageprocessing #data science