Identifying a model or strategy to apply to produce maximum positive results is imperative. Consider our research is centered on emerging and existing technologies integrating the best processes. To optimize outcomes, the authors recommend a strategic framework in which to operate within. Below are two models many industrial and systems engineers will be familiar with, the PDCA and CRISP-DM. The third component is a focus on governance of data, configuration management, certification of data structures, and processes around presenting the integrity of the data science practice. The Plan, Do, Check, Act (PDCA) model is a suitable framework in which to manage solutions for business process, optimization, and platforms. Originating with Dr. William Edwards Deming in the 1950’s, the PDCA model gives us a continuous feedback loop. The “planning” phase centers on problem definition and identifying the data sources required, and affected processes. In the “do” phase, we build and deploy the solution. This may be a limited pilot, minimal viable product, prototype, or phased deployment with limited production impact. In the “check” phase, we assess the expectations, early results and have the opportunity to adjust. Many call this the “study” phase where we apply new techniques, alter the approach. Finally, the “act” phase completes the deployment and sets the new baseline. All four phases of the PDCA model have a continuous feedback loop to improve outcomes. The CRISP-DM, which stands for Cross-Industry Standard Process for Data Mining, is an industry-proven way to guide data mining efforts. It combines business understanding, data understanding, data preparation, modeling, evaluation and deployment. The CRISP-DM model can be tailored and fit for use. Its strengths include a focus on business comprehension and how respective data sets align to business processes. It also identifies potential data specific problems such as missing data, error types, measurement errors, coding dependencies and inconsistencies, as well as metadata mismatches. Governance of the data, data structures, models, and runs will be required for preserving integrity of the solution over the life cycle of the system. One key characteristic of the governance model should be a “data lineage” feature to track the source data across languages, file systems, databases, platforms. While the discipline of data governance is new to many organizations, it is particularly well-suited for industrial and systems engineers to define the process, lean it out, and provide measurable insights.
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Mr. Crump is my vice president and he shared a generous announcement on the Dallas 500 list. It has been absolutely fantastic working for Mr. Crump.
He is a visionary that has a talent for developing high potential leaders. He allows me to innovate, apply new techniques, and investigate emerging technologies applicable to the Aerospace & Defense, Industrial Manufacturing, and Technology Services industries. His team has developed revolutionary services in robotic process automation, distributed ledger technology (blockchain), artificial intelligence, enterprise resource planning, human resources, cloud computing, and manufacturing systems. I am thankful to be part of his team and play a part in re-engineering our business, the A&D industry, and have a global impact. Artificial Intelligence is one of the top new skills to learn and apply. Some experts suggest that we will rewrite the majority of applications to include components of artificial intelligence in the coming years. We have more platforms for learning and practical labs than ever before. Given these “MOOC” platforms have been available for a number of years, it is sometimes difficult to find appropriate content that is not “marchitecture.” According to Kaggle’s 2020 “State of Data Science and Machine Learning Survey”, 63% of data scientists look to Coursera for ongoing education, while 35% use Udemy and 30% use DataCamp. I wanted to share my favorite and meaty forums to learn, apply, and practice AI. Course Criteria
The Courses
Do you have other recommendations? Please share! In my next post, I will be sharing AI certifications. As technologists, we need to do better. Surprisingly, the 1,246 pg UK Brexit deal includes references to Netscape 4.x, SHA-1, JPEG2000, & non-cloud infrastructure models. It's mix of outdated file standards, tools, and design practice. It's easy to dismiss the fact the authors suggest aged cybersecurity encryption algorithms and applications from the 1990's. The real question is why? The problem we are facing is much larger than a "cut and paste" error. The pace of technology is increasing, but we must bring non-technologists along with us. As professional engineers, cybersecurity scholars, cloud computing professionals, and technology practitioners, the latest standards, versions, and solution stacks are intuitive to us. They are second nature to us, we regularly apply emerging technology to solve problems. We are the creative class Richard Florida described, knowledge workers defined by Tom Peters. We use the latest package managers, version controlled repositories, and libraries in our designs, deployments, and daily builds. We design for resiliency using agile approaches with our product teams and apply DevSecOps techniques for our Software Factories. While we advocate for simple concepts like encryption at rest, encryption in transit, loosely coupled architectures, it is clear non-technologists are not processing what we are saying. How do we know this? As evidenced in the Brexit deal, we see plain text connections in the diagram, product versions from decades ago, and design patterns that will not scale. Perhaps what is most alarming is the absence of approaches such as application programming interfaces, distributed ledger technology, zero-trust concepts, identity protections, access controls, governance models. The list goes on and on. But who is to blame for these seemingly glaring omissions? Some suggest it's a cut and paste clerical error, but even at that I would expect a reviewer to question the ciphers for securing DNA profiles, fingerprint data, and vehicle registrations. The combination of SHA-1 (outdated since 2015) + Netscape (h/t Web Design Museum) + DNA profile should make us cringe. As technologists, we need to do better. Communicate differently, collaborate in new ways, share the vision with new techniques, demonstrate improved methods. I am convinced the solution, in part, lies with engineers, technologists, researchers, professors, and practitioners. I recommend the Brexit authors start with core foundational standards to design modern #cloud #architecture for #scaling & #security. There are so many helpful resources, I included 4 that focus on cloud, cloud cybersecurity, re:invent conference proceedings, and even a lab with hands-on instruction to build a distributed ledger to manage vehicle titles! In additional to the technology standards, we need to appropriately address the ethical use of the data collected. It's time for the non-technologists to treat technology as essential to security and design of systems, not merely an "after-thought." The challenge before us is to step out of our technology domain and work across boundaries to improve the way we are working, the concepts we are advocating, and created scalable, secure, safe platforms on which future communities will build. References:
As we approach the close of 2020, we reflect on a year that tested our community of global leaders in health, security, and productivity like no other time in recent memory. While the past 12 months have proved to be tumultuous, volatile, and ambiguous, I would like to share some of the lessons we learned in hopes they may be applied to the following year. As we usher in 2021 with the inspiration of hope and promise, 3 leadership lessons from 2020 are apparent: technology advantage, demand for leadership, and integration of processes and platforms. Leaders who focus on these areas will successfully guide their organizations, product teams, and companies. 1. Technology Advantage
In 2020, we proved the human spirit endures. From gratitude, to humble engagement, listening with empathy, and servant leadership, we endure. Focusing on the technology advance, leadership approach, and integration, we will make a positive difference in 2021. We are seeing an increasing volatile, uncertain, complex, and ambiguous (VUCA) global environment. Countries and organizations that excel will invest in automation and integration of platforms and systems.
As technologists, we have identified 21 emerging technology trends to watch in 2021.
I just found out a new book by S Sambhi, S Sambhi, VS Bhadoria leverages my research in IoT, Distributed Ledger Technology/Blockchain, Cloud Computing, and Artificial Intelligence. I am looking forward to reviewing the final publication!
IoT-Based Optimized and Secured Ecosystem for Energy Internet: The State-of-the-Art - Internet of Things In India, the concept of smart cities and Internet of Everything is taking shape with a faster pace, so the dependence on electrical energy is increasing day by day. Renewable energy is being adopted rapidly to overcome the power shortages and to meet the power demand. I am thrilled to join you for the Winter 2021 session of CST 620 9042 Prevention of Cyber Attack Methodologies (2211). Although we officially begin on 12 January, 2021 I wanted to simply say hi and welcome each of you :) What to expect:
Have a wonderful holiday. I hope you can enjoy some downtime before we bring on 2021! Be well, Jeff Professor, UMGC @jeffdaniels ...The big military funding bill contains a lot of AI items… The United States is about to get a bunch of new AI legislation and government investment, thanks to a range of initiatives included in the National Defense Authorization Act (NDAA), the annual must-pass fund-the-military bill that winds its way through US politics. For those of us who lack the team to read a 4,500 page bill (yes, really), Stanford HAI has done us a favor and gone through the NDAA, pulling out the relevant AI bits. What the US military is doing about AI:
Source: James Clark AI Blog |
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