Soon, you might not need anything more specialized than a readily accessible touchscreen device and any existing data sets you have access to in order to build powerful prediction tools. A new experiment from MIT and Brown University researchers have added a capability to their ‘Northstar’ interactive data system that can “instantly generate machine-learning models” to use with their exiting data sets in order to generate useful predictions.
One example the researchers provide is that doctors could make use of the system to make predictions about the likelihood their patients have of contracting specific diseases based on their medial history. Or, they suggest, a business owner could use their historical sales data to develop more accurate forecasts, quickly and without a ton of manual analytics work. Read More
Tag Archives: DevOps
7 Trends That Will Define The Future Of Web Development
Summary of the Article
- Role of Artificial Intelligence (AI) in Web Design and Development
- Role of Progressive Web Apps (PWA) in Web Design and Development
- Role of Virtual Reality in Web Design and Development
- Role of the Internet of Things (IoT) in Web Design and Development
- Impact of IoT on Web Design and Development –
- Role of Motion UI in Web Design and Development
- Role of JavaScript in Web Design and Development
- Conclusion
Automation as a Mindset: “Building Down” in the 21st Century
Automation isn’t essentially about technology. It’s a mindset. Technologies are the toolkits with which we automate. In the end, “Building Down” means focusing less on climbing up what’s above you than on re-architecting and re-engineering what’s already in front of and below you so that you can do more with less.
In the digital era, the way we build organizations and grow as individuals, teams, and units within them is changing. In the past, we would build an organization that contains business units. It scaled upward and outward. People joined generally at the bottom of “the ladder” and, with hard work and time, they climbed up that ladder and took on more responsibility across the unit and/or organization.
A mindset shift is in order. Technologies like IoT and AI change what organizational growth and management* means. It’s no longer just about what people can do with other people. Neither is it instead about what people can do with machines. It’s about what people can do with people and with technology. Read More
Microsoft Offers 'Premade' No-Code Artificial Intelligence
Big software vendors like to feather out their nest with a bed of ancillary services and functions designed to position themselves as one-stop-shop solution providers. Where successful, this means that customers can potentially avoid software integration and update issues that might otherwise hamper their day-to-day operations. It is also meant to provide customers with a no-brainer approach to staying on that vendor’s platform and roadmap, which (in theory at least) avoids other incompatibilities created when customers bring about in house customizations.
In reality, almost every medium-sized business (and bigger) will always operate with a mix of technology platforms, different databases and device form factors — but aiming for Nirvana isn’t a bad idea, even if most of us never get there. Read More
What is DataOps and Why It’s Critical to the Data Monetization Value Chain
In my previous blog “How DevOps Drives Analytics Operationalization and Monetization”, I discussed the critical and complementary role of DevOps to operationalize and monetize the analytics that came out of the Data Science development process. While the combination of Design Thinking and Data Science accelerate the creation of more effective, more predictive analytic modules (where analytic modules are packaged, reusable and extensible analytic modules), it’s the combination of Data Science and DevOps that drives analytic model operationalization and monetization. Read More
Building The Analytics Team At Wish
When I first joined Wish two and half years ago, things were going well. The Wish app had reached top positions on both iOS and Android app stores, and was selling over two million items a day.
Very few people believed that a large business could be built from selling low priced products. Using data, Wish has been able to test and challenge these assumptions. Being data driven was in the company DNA.
But from the company’s massive growth were huge growing pains on the analytics side. Every team needed urgent data support and had a lack of visibility into their ownership areas. But Wish’s analytics capabilities were still in its infancy and couldn’t keep up with the demand. Read More
How DevOps Drives Analytics Operationalization and Monetization
I recently wrote a blog “Interweaving Design Thinking and Data Science to Unleash Economic V…” that discussed the power of interweaving Design Thinking and Data Science to make our analytic efforts more effective. Our approach was validated by a recentMcKinsey article titled “Fusing data and design to supercharge innovation” that stated:
“While many organizations are investing in data and design capabilities, only those that tightly weave these disciplines together will unlock their full benefits.”
I even developed some Data Science playing cards that one could use to help guide this Design Thinking-Data Science interweaving process. Read More
DataOps is NOT Just DevOps for Data
One common misconception about DataOps is that it is just DevOpsapplied to data analytics. While a little semantically misleading, the name “DataOps” has one positive attribute. It communicates that data analytics can achieve what software development attained with DevOps. That is to say, DataOps can yield an order of magnitude improvement in quality and cycle time when data teams utilize new tools and methodologies. The specific ways that DataOps achieves these gains reflect the unique people, processes and tools characteristic of data teams (versus software development teams using DevOps). Here’s our in-depth take on both the pronounced and subtle differences between DataOps and DevOps. Read More
7 Steps to Go From Data Science to Data Ops
The DataOps Manifesto
Whether referred to as data science, data engineering, data management, big data, business intelligence, or the like, through our work we have come to value in analytics:
Individuals and interactions over processes and tools
Working analytics over comprehensive documentation
Customer collaboration over contract negotiation
Experimentation, iteration, and feedback over extensive upfront design
Cross-functional ownership of operations over siloed responsibilities
Read More
Rick's Cafe AI 