Big Data, Cloud & DevOps

While neural networks are responsible for recent breakthroughs in problems like computer vision, machine translation and time series prediction — they can also combine with reinforcement learning algorithms to create something astounding like AlphaGo. Deep reinforcement learning (DRL) is a machine learning method that extends reinforcement learning approach using deep learning techniques. Recent advances in Deep learning area has also fueled in Reinforcement learning as it doesn’t need hand-engineered features any more because of this ability. After appropriate many backpropagations, deep neural network knows which information is important to do the task.

Natural Language Processing (NLP) is a field at the intersection of computer science, artificial intelligence, and linguistics. The goal is for computers to process or “understand” natural language in order to perform tasks that are useful, such as Performing Tasks, Language Translation, and Question Answering. It is certainly one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence.  This 2-part series shares the 7 major NLP techniques as well as major deep learning models and applications using each of them.

Specific knowledge about fintech operations is a must for efficient fintech app development that includes compliance aspects, understanding how different types of fintechs operate, background in finance and banking, and more. Also to ensure growth, fintechs need a reliable and easily scalable platform built in compliance with the best industry practices. Here are 15+ rules for fintech app developers and grouped them according to 4 underlying principles. In Part 1, we focused on security and compliance, and API-led connectivity. In part 2, we dwell on the rules related to software infrastructure scalability, and specific domain expertise.

Realizing the growing security risks in the legally complex and increasingly regulated global economy, software development outsourcing companies put a lot more emphasis on complying with industry regulations, policies, methodologies, and technologies used to protect data. They conclude well-thought-out service-level agreements (SLAs) with their clients and look for more efficient solutions for responding to potential vulnerabilities in the development process and tackling the security challenges. We understand the importance of information security when working with international clients. Therefore, we’d like to share our knowledge and experience in the most effective information security procedures when outsourcing software development.

The digital revolution can be brutal, and companies that fail to keep pace with it will quickly find themselves replaced by one of their more technology-savvy competitors. That means companies need to step up their IT activities to avoid a competitive disadvantage — and they need to find the right technologies and ensure they remain secure along the way. Organizations must be prepared for worst-case scenarios while pursuing their digital transformation objectives. Cloud technologies offer a golden opportunity for any business that aims to fuel its digital transformation process.

Fintechs currently face the challenges of growing and scaling. Yet most will likely fail because: they could not find the right product-market fit, the high cost of scaling up, inability to find the right partner, and the struggle to create, launch, and quickly gain market share for a differentiated product that cannot be replicated. And to overcome those hurdles, they, first of all, need a reliable and robust platform built in compliance with the best industry practices. We outline15+ time-tested rules of fintech app development.

Data is one of the most important assets that any company has Today, there are new and changing uses of data in the digital economy. The big questions however are, who is winning with data, where is this data being kept, what makes new data different, when should data be kept, moved, deleted or transformed, how should data be valued, and why data is so much more important than it used to be? Once we accept the premise that data value should be measured, what would we do with this measure? 

As smaller organizations move to public cloud, the remaining private datacenters are also getting much larger.   A big driver for this scale is data leading to a completely new set of storage architectures that can operate a large scale and require very little management of the data. A new class of storage vendor has emerged, whose solutions accomplish this goal through a combination of 1) software defined storage 2) commodity building block hardware componentry 3) distributed scalable storage architectures and 4) application awareness. Let’s look at each of these solution characteristics and how they make large scale datacenter operations cost effective.

Data is clearly not what it used to be! Organizations of all types are finding new uses for data as part of their digital transformations. New data is transactional and unstructured, publicly available and privately collected, and its value is derived from the ability to aggregate and analyze it. We can divide this new data into two categories: big data and fast data. The big data–fast data paradigm is driving a completely new architecture for data centers.I will cover each of the top five data challenges presented by new data center architectures

Data has become important for everyone like never before, because it makes us to take informed decisions, improve operations. We can only improve things & activities which we can measure, and when we measure anything, it is described in a form of data. If you want to leverage and operationalize data proactively, you need to invest in your underlying data architecture and compile the information map for your organization. Solid information architecture will also set up your foundation for a data governance program. You have to know what the data is and assign business meaning to it, with the proper terminology. 

Today we understand our customers better than ever. The data we gather and analyze determines the success of our business. Business Intelligence, big data, data science, and data analytics provide companies with illuminating insights. Those who adopt these technologies early, gain huge competitive advantages. Therefore, the demand for professional data analysts is far exceeding the supply. To deal with the scarcity of specialists and soaring prices for their services, many business leaders consider data analysis outsourcing. But how to make it right? Our article will highlight the most important aspects of data analysis outsourcing and tips on choosing the best data analysts.