Computer Vision is one of the hottest research fields within Deep Learning at the moment. As Computer Vision represents a relative understanding of visual environments and their contexts, many scientists believe the field paves the way towards Artificial General Intelligence due to its cross-domain mastery. Why study Computer Vision? The most obvious answer is that there’s a fast-growing collection of useful applications derived from this field of study. Here are the 5 major computer vision techniques as well as major deep learning models and applications using each of them. They can help a computer extract, analyze, and understand useful information from a single or a sequence of images.
Central Securities Depositories (CSDs), in the post-trade environment, are changing their mind on new technologies and on their future position in the blockchain world. Increasing regulation, legacy systems, and costs pressures, are drivers for CSDs to at least embrace some aspects of the blockchain. They are increasingly considering them as an enabler of more efficient processing of existing and new services, instead of a threat to their existence. CSDs are likely to play an integral role in any blockchain environment. But they will look quite different from we know them today. Yes. CSDs are not expected to disappear in a blockchain world.
Apache Spark is now becoming the big-data platform of choice for enterprises. It is a powerful open source engine that provides real-time stream processing, interactive processing, graph processing, in-memory processing as well as batch processing with very fast speed, ease of use and standard interface. Sooner or later, your company or your clients will be using Spark to develop sophisticated models that would enable you to discover new opportunities or avoid risk. Spark is not hard to learn, if you already know Python and SQL. learn how to build a binary classification application using PySpark and MLlib Pipelines API.
There is a bounty of business use cases from which the business can choose in order to monetize their IOT efforts. The best approach is to build out your IOT Business Strategy with one use case at a time. In this manner, not only do you incrementally build out your IOT analytic, data, technology and architecture capabilities, but this enables the organization to build upon the work of previous use cases – to capture, share and refine the IOT data and analytic assets that are key drivers to IOT monetization.
Success requires a direct link between analytics and positive process outcomes. This requires a closed-loop process. One that enhances application intelligence based on analytic insights. This is what I call Prescriptive Applications. Prescriptive Applications integrate analytics to dynamically change application content, access, and workflow. If something broke, fix it. But be aware that the process can be a complex journey. Prescriptive Applications enable a closed loop process to optimize outcomes based on integrated insights from analytics-descriptive, diagnostic, predictive, and prescriptive. So, Prescriptive Applications provide a complete solution. While IIoT faces many challenges, could Prescriptive Applications be a key missing piece?
Enterprises are experiencing a rapid evolution in technology that is challenging traditional security systems and infrastructures. The arrival of business-oriented IoT devices creates even more demand for secure connectivity. To avoid security breaches, IT departments must secure connections for more users and devices than ever before. Providing encryption for the communications of many thousands of devices, virtual private networks (VPNs)are a tried-and-tested way to ensure sensitive company information remains secure and private. When deploying VPNs for a multitude of devices, a large-scale management solution is essential that can greatly enhance the productivity of IT departments and keep corporate networks secure.
Existing data architectures are at the breaking point with a large amount of data, velocity of data ingestion, and variety of data they need to process and store. Industry analysts are predicting that up to 80% of the new data will be semi-structured and unstructured. Modern Data Architecture addresses the business demands for speed and agility by enabling organizations to quickly find and unify their data across hybrid data storage technologies. The Modern Data Architecture stores data as is; it does not require pre-modeling. It handles the volume, velocity, and variety of big data.
What are the common mistakes made in building ML products? The goal of the note is to provide someone with limited ML understanding a general sense of the common pitfalls so that you can have a conversation with your data scientists and engineers about these. Many companies wanted to use ML and had built up ‘smart software’ strategies but didn’t have any data. You cannot use machine learning if you have no data. You can apply ML on small data sets too, but you have to be very careful so that the model is not affected by outliers and that you are not relying on overcomplicated models.
Of late, IoT as a technology has seen quite a few changes, and Internet of You (IoY) could be the latest. With IoY, you will no longer have to stop, think, and decide to take your phone out of your pocket, find and launch the app you want, then take action. Instead, our data and services will come to us when we need them and in the context in which we are currently in. ‘Internet of Things’ is relevant to the users only when it is centred around them, which combined with semantic web technology transforms into IoY.
A Blockchain Gateway platform should serve as an extra layer between existing enterprise applications and blockchain(s). The Blockchain Gateway offers seamless, easy, fast and secure integration of their existing applications and devices with one or more blockchain networks. The intuitive user interface makes it easy for corporates to configure all business critical configurations with low development efforts without the need of specific blockchain expertise. The Blockchain Gateway may bring real value to multiple parties within blockchain networks by simplifying integration of existing applications with one or more blockchain networks, speeding onboarding of participants and delivery of blockchain projects.
Some of the fastest applications that will be dominated by IoT are healthcare devices, wearable devices to enhance currently restricted capabilities, energy saving devices and smart devices for inter-connectivity in real time use. We will soon have smart homes, connected mobility (transportation), smart cities, and even smart forest zones and marine life to create better air, water and live a healthier life. We need devices that will bridge the gap between physical and digital world to improve the quality and productivity of life, society and support the eco-system.
Data-driven culture is about setting the foundation for the habits and processes around the use of data. Data-driven companies establish processes and operations to make it easy for employees to acquire the required information, but are also transparent about data access restrictions and governance methods. So, why is it important to build a data-driven culture in your organization? The data can only take an organization so far. The real drivers are the people and hence building the culture around data is important. An organization can work upon to build data-driven culture.
IIoT project failures are rampant. For many, failures are a direct result of poor information sharing and inefficient collaboration. Frustration builds with customer and partners. Leading to cost increases, limited adoption, and reduced potential ROI. The promise of IIoT requires frictionless (but secure) ecosystem access to information. When information flows within digital ecosystems, each business entity wins. The new digital world is complex. It challenges many long held beliefs about competitors, data sharing, and value propositions. Your new digital relationships will be different than prior relationships. Be open and transparent. Engage your customers and partners, and everybody wins.
What many businesses forget is that IoT devices are not typically secure. In fact, they are designed to remain cheap and lightweight, which makes them difficult to manage once they are connected to the network. There are several reasons why IoT devices create a visibility challenge. As device manufacturers aren’t required by consumer protection laws to integrate security features, now is the time for enterprises to focus on gaining complete IoT visibility. It starts with understanding the inventory of connected devices and ends with segmenting those devices into areas of the network with limited access according to their needs.
Adoption of IoT is going to continue to drive an adoption of hybrid cloud. As a result, the solution to these problems cannot simply be providing a managed service for an incredibly complex product. The product itself needs to be simple and easy to use, yet highly scalable, so that it can be deployed and managed directly on the edge and in the cloud with the same level of effort and skill set. In order to continue to drive and support innovation, the data management industry needs to design and build the traditional big data architectures required to consume IoT data and transform that data into valuable insights, identify patterns and make it actionable.
IIoT is truly ubiquitous. It impacts everything from factories to construction equipment to the plants in our office. It turns out the commercial landscaping maintenance management market is a big business. Some might ask, why all this greenery? Studies show interior office plants contribute to employee productivity and satisfaction, not to mention the fact that greenery can increase commercial property value up to 15%. Green walls and roofs, commercial landscapes, and interior plantscapes are like other commercial assets in that they need regular maintenance.
There’s a lot of data, and with more on the way, lots of data scientists are needed to make use of it all. Data science is only growing more critically important as our technology advances. It will be key in all businesses in the future and there looks to be a massive shortage of data pros, a job that pays well and is fun. It helps us tell important stories. I don’t need to produce an infographic to make this tale any more compelling. Data science deserves a spot in every STEM education curriculum.
What is data? How to define data from different viewpoints? What are tools in Data Technology & what to use when? How to apply Data Governance & build Data Strategy? And finally, how every aspect mentioned above fits together in business & technology ecosystem? Data at the fingertips of almost every professional can be truly transformational. So building Data-Driven Culture is the most challenging yet the most rewarding aspect. And to create a Data-Driven Culture, first and foremost thing is to make every employee, every professional data literate.
Today, we are siloed in how we think about IoT. We develop solutions for the sake of technology and continue to think in small incremental steps about the data we are collecting. It’s relatively easy and cheap to deploy a connected sensor and collect data, but it’s the easy way out and everyone is doing it. The industry is missing a critical link: the marketplace for IoT to use the data collectively and build an ecosystem for distributed monetization of data. This is where AI comes in. The convergence of AI and IoT can change this by creating a connected system of things that can be used in everyday life.
Companies are under increasing pressure to constantly innovate, often guided by a digital transformation or corporate innovation charter that is mandated by the C-suite, supported by middle management guidance and executed by grassroots “intra-preneurs.” This mounting pressure can serve as both a blessing and a curse for survival. Change agents strive to not only brainstorm the next big idea that will push the company into a new era of technology revolution, but also simultaneously hide their efforts from colleagues and other departments in order to get the glory of being the smartest person in the room.
Cloud services now provide the capability to both store and compute vast amounts of data. As both public and private clouds have quickly become a business necessity with the explosion in the availability of data in recent years, the evolution of Datacenter depends on a well-designed cloud-based architecture that is capable of flawless delivery, and must include five major processes - Visualize, Consolidate, Integrate, Automate, Federate. Indeed, cloud computing is widely leveraged across a variety of problem domains ranging from movie recommendation systems to unraveling the mysteries of the universe.
One of the main problems with organizations attempting digital transformation is an embedded complexity in their processes. This complexity has usually arisen from being product-focused rather than customer-focused. While tackling the process innovation, it is not something that should be delayed. With two-speed IT, one now has to introduce a whole new IT model for the agile development, which includes more new processes, instead of striving for simplicity. The short-term goal of IT business units should be to move to the agile philosophy, which is a milestone on the roadmap to continuous delivery and implementing DevOps.
IIoT projects are change agents. They help create new digital ecosystems and value chains. Participation in these value chains can be the difference between success and failure. How will IIoT drive more sales? This question stalls many industrial IoT projects.How will IIoT create more shareholder value? Now that is a much better question. Successful IIoT projects are customer and engagement focused, creating value through digital transformation. Well-managed IIoT projects can help transform customer, employee and partner engagements. To accomplish this, provide a clear IIoT plan that includes relationship, retention, and revenue value creation.
What is data science? Why it is important? What is the difference between Artificial Intelligence, Data Science, and Machine Learning and Deep Learning? Data Science is an amalgamation of many other fields like mathematics, technology and domain. It has its own concepts, process and tools. It’s really tough to know each and everything related to the subject unless you have really worked on complex data science problems in the industry for a couple of years. You can learn the data science concepts like types of learning and when to use which kind of learning algorithms?