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Why application modernization is important for your business and how to get started
2021-09-08 17:14:39| The Webmail Blog
Why application modernization is important for your business and how to get started nellmarie.colman Wed, 09/08/2021 - 10:14 Many organizations are looking to modernize applications to ensure future resiliency. But the complexities of cloud technology and architecture can act as blockers. So, where do you begin on your application modernization journey? Which approach will best fit your organization and meet your goals? In our latest webinar, Amir Kashani, VP, Cloud Native Development & IoT, Chris O'Malley, Senior Practice Manager, Cloud Native Development and Jim Rosser, Customer Solutions Architect will help you identify which application modernization approach is the best fit for your organization. Watch the on-demand webinar to hear about the following: How modern application capabilities such as automation can power innovation An explanation of the pathways to modernizing applications with benefits and use cases Customer case studies including a pharmaceutical company that adopted a serverless platform How cloud native replatforming enables scaling and high availability Why serverless refactoring can be a good starting point for application modernization How containers can reduce operating costs enabling monitoring and scaling Amir shares some pertinent points on application modernization. You need to leverage people, processes and technology to build applications with people and process being the most important aspect. Application modernization is not just about the cloud or technology but also involves organizational changes and upskilling teams to build modern applications. Application modernization is interwoven with cloud native, DevOps and cloud-first, as they are all slices in the same pie. You have to use them all. Amir describes how application modernization enables innovation. Leading companies favor experimentation over elaborate planning, customer feedback over intuition and iterative design over traditional big design. A new feature, product or service is put out into the world for feedback and iteration. The key isn't success, as often experiments fail, but those failures are lessons. And the faster you can take ideas to experimentation, feedback and back around the innovation flywheel the quicker you reach the right answer. But how does application modernization benefit businesses? Amir shares a customer case study of a US airline that used Kubernetes. Airlines don't operate at a consistent workload 24/7. So being able to have infrastructure that allowed them to scale up and down was great, with unplanned failures going down by 40%. Its a success story on leveraging containers in a highly regulated industry. Jim Rosser explains one of the ways application modernization is achieved. Whenever we build modern applications, IT operations are embedded and considered first. We want to ensure that we're exposing the right metrics, we're exploring and tracking the right KPIs so that the application is not only functioning generally, but also functioning for what the business needs. The three routes to application modernization are cloud replatforming, container adoption and serverless refactoring as Jim describes. Cloud native replatforming has the lowest barrier to entry. Its using replacements for what you already have on-premises or elsewhere and moving it all to managed services. This lowers TCO without having to change a huge amount of code. Container adoption, which has been the talk of the town for the last five to eight years, involves putting applications in containers, making them portable and testable across environments. Serverless refactoring involves a fully managed infrastructure that can scale and has high availability. It provides a quicker time to market because youre focused on the core business without having to worry about things like OS. Its consumption-based, meaning you only pay when someone hits your website. Each of the pathways has benefits, with serverless refactoring opening up new opportunities, as Jim explains. Customers using serverless refactoring are data-driven. They are focused on understanding how to use data for innovation and business growth. It's extremely exciting to see customers starting to focus on building smart applications with AI and machine learning because theyre no longer just focusing on business logic. The are many use cases for application modernization, as Chris O'Malley describes how a SaaS transformation benefitted a customer. As you'd expect migrating from on-premises to AWS with a serverless approach meant cost went down dramatically. The developers became much more efficient at releasing new features. We were also able to leverage serverless features to satisfy the customers security posture. This was all done alongside training the customer on how best to operate, maintain and build cloud native applications. In another example, Chris shares how an incremental modernization helped a customer. Their business-critical application can now scale and is resilient. In fact, after testing we saw performance improvement approaching 400%. It was a dramatic and much-needed improvement. Its a common pattern that when we introduce new CI/CD processes we see a massive improvement in developer productivity. With new features and bug fixes being delivered more efficiently than previously. Why application modernization is important for your business and how to get startedLearn how you can unlock the full value of the cloud with serverless refactoring, container adoption and cloud native replatforming. Identify the right application modernization approachhttps://www.brighttalk.com/webcast/17680/495173/identify-the-right-application-Watch the webinar
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'I spent 70,000 on pilot training but can't get a job'
2021-06-27 03:46:34| BBC News | Business | UK Edition
Newly qualified pilots are indebted and jobless as airlines reel from the pandemic, says the industry.
BE READY Do Not Get Ready
2021-04-06 08:00:00| Waste Age
As we enter the second quarter of 2021, businesses all across the land are excited at the prospect of COVID restrictions being reduced or eliminated and businesses returning to full operating capacity. We are all excited by the potential return
AI and machine learning are revolutionizing modern businesses heres how to get ahead
2021-02-04 22:08:23| The Webmail Blog
AI and machine learning are revolutionizing modern businesses heres how to get ahead nellmarie.colman Thu, 02/04/2021 - 15:08 Fierce competition means every business must adapt to succeed. AI and machine learning have emerged as modern, vital ways for organizations to get ahead. Many businesses today prioritize data, analytics and AI/machine learning projects to power new business models, enhance product and service offerings, improve efficiency, drive revenue and deliver superior customer experiences. But analyst figures on project implementation make for sobering reading. Gartner predicts that under half of modern data analytics and machine learning projects will be successfully deployed in production by 2022. Less than a fifth will move piloted AI projects into production without delays caused by a range of problems from technical skills gaps and lack of IT/business process maturity, to insufficient organizational collaboration. For example, these businesses may not have expertise in mathematics, algorithm design or data science and engineering. Or their data may not be in a unified data lake infrastructure for ready access. These conditions create challenges for any organization looking to advance in the market and derive value from AI and machine learning. This combination of pressure and challenges can overwhelm your business, especially if youre at the start of your AI and machine learning journey. So lets dig into why your business should make the effort and how doing so might require different skills sets and data from what you might think. What are AI, machine learning and deep learning? Lets start with the basics. When a machine completes tasks based on a set of stipulated rules that solve problems, were into the realm of artificial intelligence. This might include understanding and interpreting natural language, recognizing when objects move and providing intelligent answers. Business benefits follow, such as analyzing data sets that are too large for humans to process, answering questions in real time that draw from existing data and experiences, and automation that can reduce costs and boost productivity. Machine learning is a discipline within the AI domain. It enables machines to learn by themselves using data. They use this knowledge to make increasingly accurate predictions and drive actions. For this to happen, you need a model thats trained on existing data, after which point it can process additional data and make predictions. Throughout the process, its important to track and understand your model, building quality and eliminating bias. Finally, deep learning is a subfield of machine learning. It structures algorithms in layers to create an artificial neural network that can learn and make intelligent decisions on its own. The many use cases of AI and machine learning Weve so far explored AI, machine learning and deep learning in the abstract, but in what specific ways can they benefit your business? Answering questions, thereby improving customer support and buying journeys, through faster, higher-quality answers and experiences. Speech recognition, including text-to-speech and speech-to-text translation, enabling you to work with voice/audio data more widely and productively. Document summaries that effectively extract key concepts to use in countless ways, improving productivity and document data use. Image recognition for biotech, satellite/drone imagery and face recognition, to quicken emergency responses and prevent crime. Image processing to improve the presentation and utilization of images through enhanced resolution and colorization. Data classification in medicine, to yield better diagnoses, faster and more targeted treatments and health preservation. Superior search to get customers to what they want more rapidly, whether thats a product recommendation or a web page. Strategic analysis that can be a boon to the games industry, driving more challenging and educational entertainment. Financial and logistical forecasting to improve financial management, planning and resource allocation/utilization. AI and machine learning require skills and data, but not what you think If youre looking to machine learning and deep learning but have concerns about your existing data, be mindful that they dont always need massive data sets. While completely new models with no data nor training do require tens of thousands to millions of data points, trained models exist that can give a project leader a head start. Even if you have just 100 or so examples for a specific use case, building on a general models foundation could yield more accurate results than human experts would provide. Additionally, its worth thinking differently about hiring for the delivery of AI/machine learning enabled applications and solutions. Theres an assumption you need PhD-level data scientists. Although they do add value and can be necessary in some circumstances, existing staff can often be trained in about 100 hours, building on high-school math and a year of coding experience. With modern tools on AWS or Google Cloud including AutoML, they can build the solutions you need. In all, its as much about changing your mindset as anything else. You must think about what AI and machine learning can bring to your business and the most effective way to achieve that, thereby keeping your company ahead. Machine learning is today driving change in thinking of data as code where machine learning uses data to write the program, which is the output. This methodology coupled with the tools and education I mentioned earlier set the stage for many more people collaborating to fashion a new generation of intelligent solutions that will revolutionize business for years to come. For more information on AI and machine learning, check out our panel discussion, which dives deep into these topics. The discussion covers: toolsets and methodologies; capabilities and constraints; data, computer and expertise requirements; examples of successful applications; and how to get started. AI and machine learning are revolutionizing modern businesses heres how to get aheadDiscover why AI and machine learning are worth the effort, and why they require different skills sets and data from what you might think.Discover how businesses are using AI and machine learning.https://www.brighttalk.com/webcast/17680/420320Watch the presentation
Working from bed: 'I don't even have to get dressed'
2021-01-29 23:08:34| BBC News | Business | UK Edition
A lot of us are tempted to spend January in bed but some people have turned it into their office.