Over the next few weeks on the podcast, we’re bringing you volume 2 of our AI Platforms series. You’ll recall that last fall we brought you AI Platforms Volume 1, featuring conversations with platform builders from Facebook, Airbnb, LinkedIn, Open AI, Shell and Comcast. This series turned out to be our most popular series of shows ever, and over 1,000 of you downloaded our first eBook on ML platforms, “Kubernetes for Machine Learning, Deep Learning & AI.” Well now it’s back, and we’re sharing more experiences of teams working to scale and industrialize data science and machine learning at their companies. Read More
Tag Archives: DevOps
TWiML Presents AI Platforms, Vol 1
As many of you know, part of my work involves understanding the way large companies are adopting machine learning, deep learning and AI. While it’s still fairly early in the game, we’re at a really interesting time for many companies. With the first wave of ML projects at early adopter enterprises starting to mature, many of them are asking themselves how can they scale up their ML efforts to support more projects and teams
Part of the answer to successfully scaling ML is supporting data scientists and machine learning engineers with modern processes, tooling and platforms. Now, if you’ve been following me or the podcast for a while, you know that this is one of the topics I really like to geek out on.
Well, I’m excited to announce that we’ll be exploring this topic in depth here on the podcast over the next several weeks. Read More
Container technologies promise more agility for big data apps
First developed to make applications easier to deploy, manage and scale, container technologies nonetheless have seen limited use in big data systems due to earlier struggles managing application state and data. But all that is beginning to change, promising more agility and flexibility for these systems.
Containers can be viewed as part of a continuum of infrastructure simplification situated between traditional monolithic infrastructure and serverless functions, said John Gray, CTO at Infiniti Consulting, an InterVision company. Compared to monolithic infrastructure deployments, serverless infrastructure could provide more agility and reduce costs in the short run, while greatly easing management tasks in the long run. Read More
MIT's new interactive machine learning prediction tool could give everyone AI superpowers
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
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
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