Experfy Insights

If you look out at the world of platform companies, you will quickly find that use of AI for curation is a hallmark of the outperforming platform.  If your organization wants to enter adopt a platform strategy and begin taking advantage of the networks effects it offers, you had better recognize that curation is an essential part of the journey and make sure you have the machine learning competency needed to make it happen.

The combination of blockchain technology and Artificial Intelligence is still a largely undiscovered area. Even though the convergence of the two technologies has received its fair share of scholarly attention, projects devoted to this groundbreaking combination are still scarce. Putting the two technologies together has the potential to use data in ways never before thought possible. Data is the key ingredient for the development and enhancement of AI algorithms, and blockchain secures this data, allows us to audit all intermediary steps AI takes to draw conclusions from the data and allows individuals to monetize their produced data.

The FinTech products come with a complexity which can be difficult to compete with, due to the various limitations falling under this domain. Python is seen as the ideal technology for building FinTech products. The language offers a lot of advantages over other languages like clear programming syntax, greater development strategies, and user-friendliness. In addition, Python can also boost the availability of several frameworks and libraries which can enhance the overall development process of any FinTech product.

Despite the variety of applications of AI in the clinical studies and healthcare services, they fall into two major categories: analysis of structured data, including images, genes and biomarkers, and analysis of unstructured data, such as notes, medical journals or patients’ surveys to complement the structured data. The former approach is fueled by Machine Learning and Deep Learning Algorithms, while the latter rest on the specialized Natural Language Processing practices. At present, advances in AI and NLP, and especially the development of Deep Learning algorithms have turned the healthcare industry to using AI methods in multiple spheres.

If you haven’t been paying attention to the world of enterprise IT infrastructure, you may have missed the sudden rise of Kubernetes to a position of absolute domination. We can attribute this rapid ascent, in fact, to a confluence of trends. Perhaps the most predictable of these is the maturation of the public cloud. The second trend that contributed to Kubernetes’ victory: DevOps. Bridging the maturation of cloud best practice and the dual roles of DevOps is perhaps the most important trend of all: cloud-native architecture.

When it comes to business, AI can be invaluable – whether it’s used to identify and target a potential customer base or streamline internal processes.   Already, a range of industries, from retail and banking to the security and legal sectors, are taking advantage of what AI can offer. The goal for future-thinking organisations is to make sure they have the right strategies in place so that they’re able to adopt these rapidly evolving AI capabilities. Here are three business needs were identified where AI could offer value.

When created from scratch, deep learning models require access to vast amounts of data and compute resources. This is a luxury that many can’t afford. Moreover, it takes a long time to train deep learning models to perform tasks, which is not suitable for use cases that have a short time budget. Fortunately, transfer learning, the discipline of using the knowledge gained from one trained AI model to another, can help solve these problems.

We can understand things in 1 dimension, 2 dimensions and 3 dimensions easily but Datasets can be very complex and hard to understand, especially if you don’t have the right tricks in your proposal. In machine learning, we sometimes need to make assumptions based on hundred or even thousand dimensions. Our brains just can’t do that, which is why machine learning helps us to recognize and learn patterns within data that humans can’t recognize. 

It’s vital to have the necessary data to make important business choices. Today’s technologies – such as artificial intelligence, business process automation, robotic process automation (RPA) and other automated tools – are creating a data-driven decision-making revolution. A growing number of businesses are taking advantage of RPA, which partially or fully automates human activities that are manual, rule-based, and repetitive, freeing up humans to focus on more pertinent tasks. RPA harnesses data so that you can take the guesswork out of your business decisions.

Introduction to Big Data provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. A solid understanding of the basic concepts, policies, and mechanisms for big data exploration and data mining is crucial if you want to build end-to-end data science projects. Many datasets are too large to fit on a single machine. Unstructured data may not be easy to insert into a database. Distributed file systems store data across a large number of servers.

Data initiatives often take too long to get off the ground, which can cause businesses to give up or change tack. In addition, the promise of value of data warehousing projects is often lost due to other factors, including the fact that planning for the required hardware based on the estimated load and usage, is often a 'thumb-suck' exercise and requires a significant upfront capital investment. Finding and retaining the appropriate database administration skills to guarantee that data is readily available when needed, and indexed accordingly, is challenging and expensive.

It’s common to see businesses of all sizes relying on people-power to complete tasks that today can and should be automated. To address this challenge, companies should consider deploying robotic process automation, or RPA. RPA uses bots to reduce manual workloads, freeing up teammates to work on more value-added tasks that ultimately enhance the customer experience and create greater job satisfaction. While RPA offers advantages, it can also present difficulties to deal with. Here is a look at the successes, challenges and best practices that other organizations may find helpful in their automation journey.

Everyone that talks about scaling should understand that they are referring to the ability of their work – whether a system, a tool or some other innovation – to cope and perform under an increased or expanded workload. Something that scales well will be able to maintain, or even increase, its performance or efficiency when tested by larger operational demands. How do I increase the impact of my work? There are typically five steps needed to scale one’s work.

The plethora of automation tools available out there can be extremely confusing for organisations wanting to embark on a digital transformation process. Two-thirds of global service organisations were engaged in digital transformation, with 16% claiming to have already completed the process. it is important for organisations to take a holistic view of what they are hoping to achieve, before deciding on which automation approach to take. This is not always easy, as enterprise architects have to choose from a confusing range of process automation options as a foundation for the transformation journey.

Choosing how to deploy a predictive model into production is quite a complex affair, there are different ways to handle the lifecycle management of the predictive models, different formats to stores them, multiple ways to deploy them and very vast technical landscape to pick from. Understanding specific use cases, the team’s technical and analytics maturity, the overall organization structure and its’ interactions, help come to the right approach for deploying predictive models to production.

The relationship between Deloitte and Experfy is an example of how dynamic marketplace conditions and talent markets encourage leading edge organizations and professionals to forge alternative work arrangements including freelance, crowdsourcing and on-demand relationships. The alliance with Boston-based Experfy, looks to accelerate Deloitte’s ability to deploy a flexible, world-class, on-demand talent strategy; and to provide its clients with the right team to meet their most challenging Analytics and AI opportunities and challenges.

Natural Language Processing helps business users sort through integrated data sources (internal and external) to answer a question in the way the user can understand and will provide a foundation to simplify and speed the decision process with fact-based, data-driven analysis. The enterprise can find and use information using natural language queries, rather than complex queries, so business users can achieve results without the assistance of IT or business analysts. NLP presents results through Smart Visualization and contextual information delivered in natural language.

 Practically speaking, it’s impossible for a data scientist to have all the skills listed in this article. But these skills are what make a rockstar data scientist different from a good data scientist, in my opinion. By the end of this article, I hope you’ll find these skills helpful throughout your career path as a data scientist. It’s perfectly fine if you’re overwhelmed by the skills needed. At the end of the day, these skills are not a must to be a data scientist, but they certainly make you different compared to other typical data scientists.

Possibly most significantly, AI will be at the forefront of creativity – the force that ultimately drives the media business. Artists equipped with an AI-enabled feedback loop based on real-time, consumption metrics will up their creative batting average, which will thus increase production and commercial ROI. AI will influence all parts of the media value chain, helping content creators to be more creative, helping content editors to be more productive, and helping content consumers to find the content that matches their interests and current situation.

Data visualization provides an important suite of tools and techniques for gaining a qualitative understanding.  To choose the most appropriate visualization technique you need to understand the data, its type and composition, what information you are trying to convey to your audience, and how viewers process visual information. Sometimes, a simple line plot can do the task saving time and effort spent on trying to plot the data using advanced Big Data techniques. Understand your data — and it will open its hidden values to you.

Interested in how to incorporate blockchain technology with the help of a dedicated blockchain unit or simply want to get to know more? To promote your knowledge of blockchain technologies or digitization in general, regardless whether it is directly for your company or your personal development, you should gather further information and deepen the understanding of how to incorporate blockchain competencies inside a corporation.The importance of investing into qualified manpower as well as a strategic foresight is going to be illustrated in the following article.

The financial service sector has undergone radical changes over the last few years. Given the prevalence of several labour-intensive processes in the banking industry, it is unsurprising that the sector has been leading in welcoming automation solutions. Banks are increasingly moving towards adopting the digital blocks of what we call as “ABCD of Digital Technologies”, A for Artificial Intelligence and Robotic Process Automation, B for Big-data and Advanced Analytics, C for Cloud computing and D for Devices in several processes including KYC procedure, cross-border payments, trade finance and smart contracts.

Management consulting tends to view itself as an elite, untouchable echelon of the business world. But it is vulnerable to the same market forces that are disrupting services everywhere. The consulting industry is at risk. With its deeply embedded business and mental models, many companies will be unable to make the jump. So how should consulting companies, or any in the services sector, adjust to this new, data- and AI-driven world? Follow the rules mentioned here.

The utilization of RPA in healthcare services can centralize and streamline different workflows. Shifting these routine tasks from human agents to bots can result in cost savings for healthcare providers. Also, automating crucial workflows will improve efficiency across the board. With this approach, healthcare professionals can spend the majority of their time on patient care and other critical activities. A major drawback of leveraging RPA in healthcare is that RPA can only process structured data and work with a rule-based approach. However, the advent of intelligent process automation (IPA) will make RPA smarter.