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?
Learn the process of defining intents and entities and building a dialog flow for your chatbot to respond to customer queries. You define an intent for each type of user request you want your application to support. You list the possible values for each entity and synonyms that users might enter. You will learn how to enable Speech to Text and Text to Speech services for easy interaction with the Android app. Also, track the app’s usage metrics through Mobile Analytics service.
One of the most obvious developments that have taken place in the world is in the field of medical science. Radiology has allowed medical professionals to pinpoint the causes of symptoms of a patient. Reconstructive surgery has enabled breast cancer survivors to have the choice of rebuilding the look and shape of their breasts.
Robotics, Machine Learning (ML) and AI is starting to dominate the enterprise, service providers and consumer worlds for decades to come. We are entering to perhaps another major showdown for use of technology using Artificial Intelligence and Robotics with massive amount of sensors for years to come and I predict number of sensors in entire world economy will exceed 1T by end of 2030 time-frame and this will generate level of innovation and growth in enterprises, consumers and governments which we had not seen except for industrial revolution in 20th century.
Machine learning has been redefining how even the basics of operational tasks are done across industries. The financial industry is no different. While some of the applications of machine learning in finance are clearly visible to us - like mobile banking apps and chatbots, the technology is now being gradually used for drawing out accurate historical data of customers and predicting their future needs as well.
The eCommerce industry is growing by manifolds across the world. From what started as a few stores that enabled online shopping; today, the smallest of brands are able to take their products online and market them to a large consumer base. Call it the ease of technology and the ability to use data, almost every eCommerce store is able to capture a segment of the consumer market - despite the rising competition.
RPA software has proven to reliably reduce costs by removing manual work from various business workflows and processes. But is RPA adoption by all enterprises need to automate their business processes? What else does process automation have in store other than RPA? To answer these questions, it helps to understand where RPA technologies came from and at what capabilities they now offer. Using machine-learning platforms to also incorporate new information gathered from background collection of workflow exceptions is the most practical next step to achieving full automation. We have far to go before RPA fulfills its “robotic” mission of removing the human element.
You should know about Artificial Intelligence and Machine Learning in the healthcare industry and how it will impact our future. These technologies WILL dramatically change the way we work in healthcare. As the use of Machine Learning grows in healthcare, continue to obsess over the privacy of your customer data. Making “cool” innovations in Artificial Intelligence or Machine Learning won’t work if not coupled with a relentless pursuit to serve the customer. These endeavors are expensive, so spend your IT budget wisely, ensuring new innovation creates true value and is easy for the end user.