SaaS Archives - Artificial Intelligence

Jacobs’ Ion Industrial Internet Of Things Platform Enables A Safe Return To Worksites

aiuniverse — Sat, 01 Aug 2020 07:12:49 +0000

Source: aithority.com

Jacobs leverages the power of digitization to help facilitate the safe return to on-premise work environments providing location-aware technology and automated intelligence supporting COVID-19 contact tracing efforts in the event of an outbreak.

Jacobs’ ion is an Industrial Internet of Things (IIoT) data integration and visualization platform built on an open API framework to easily integrate with existing solutions or off-the-shelf sensors. By challenging a “business as usual” mindset, the Jacobs team leveraged the platform’s mature technical foundation to add the necessary commercial off-the-shelf sensor components to make the solution viable in an accelerated timeframe.

“This is a great example of Jacobs innovating at the speed of the market, as we leveraged our leading IIoT IP and deep domain knowledge of the clients on-premise operations to solve their toughest challenges with our ion solution and rolled out the first customer ready prototypes in less than a month,” said Jacobs Critical Mission Solutions Senior Vice President and General Manager for Advancing National Security Jennifer Richmond. “We leveraged our scale across Jacobs to rapidly deploy new proprietary features and domain expertise to quickly release new functionality into the platform and rapidly fielded the solution by collaborating with our clients and our market facing operations teams – a partnership that created an immediate feedback loop as we updated the solution in near real-time to shorten the deployment timeline.”

By implementing ion, a large global confidential client improved safety of personnel at the job site through monitoring of social/physical distancing and maximum occupancy for key areas, and by implementing an automated and robust contact tracing capability. The impact on operations has been significant, with one industry executive noting that the innovation is a game-changing solution for return-to-work scenarios in the construction and manufacturing industries in their new operational environment.

The ion platform integrates hardware, IIoT devices, analytics and applications with a robust engine for rules, events, visualization and notifications into a single tailorable platform, available as Software as a Service (SaaS). The solution uses active monitoring to enhance security and reliability and automates processes for personnel safety and accountability with location tracking, mustering and emergency notification. Commercially available wearable technology is used to monitor worker interactions, while taking care to avoid phone and GPS-based solutions that can have a negative impact on personal privacy concerns.

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Stradigi AI Joins Roland Berger’s Terra Numerata Global Network

aiuniverse — Sat, 04 Jul 2020 07:09:37 +0000

Source: aithority.com

Stradigi AI, a leading North American Artificial Intelligence software company, announced it has been selected by Roland Berger, a European-based consulting firm, to join Terra Numerata, its exclusive global network of partners. Stradigi AI’s SaaS machine learning platform, Kepler, is designed for non-technical business users and is complementary to the firm’s advisory services. Together, they will meet a growing need within Roland Berger’s clientbase for business-driving AI solutions. Joining the Terra Numerata network also provides expansion opportunities for Stradigi AI. With over 50 years of experience in implementing complex projects in a broad range of verticals including automotive, manufacturing and financial services, Stradigi AI’s clients will benefit from Roland Berger’s seasoned experts and measurable global successes.

“Partnering with Roland Berger allows a broader set of customers to benefit from advanced machine learning to gain efficiencies and drive competitiveness,” said Per Nyberg, Chief Commercial Officer at Stradigi AI. “Our AI SaaS offering, Kepler, solves business use cases in industries where Roland Berger has extensive experience and demonstrated value. Together, our organizations will be able to strategically support global clients through combined capabilities. We share the same approach to delivering business-focused solutions in an agile manner to meet the evolving needs of our clients.”

“Adding Stradigi AI to our Terra Numerata global partner network will be an exciting collaboration,” said Axelle Lemaire, Terra Numerata Global Head at Roland Berger. “Terra Numerata helps companies with their transformation to new digital business models. Stradigi AI’s Kepler platform fills a gap in our ecosystem to address the needs of companies who can benefit from machine learning, but often lack the necessary resources to get started. This collaboration will empower our clientbase to become more AI-enabled.”

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ProcessMinerTM Now Available in the Microsoft Azure Marketplace

aiuniverse — Tue, 03 Mar 2020 06:17:59 +0000

Source: aithority.com

Microsoft Azure customers worldwide now gain access to ProcessMiner’s AI platform to take advantage of the scalability, reliability and agility of Microsoft Azure to drive application development and shape business strategies.

ProcessMiner, an Artificial Intelligence platform for manufacturing, announced its platform is live in the Microsoft Commercial Marketplace. Based in Atlanta GA, ProcessMiner delivers real-time systems monitoring, predictive analytics, and recommendation solutions for manufacturers.

Continuous manufacturing processes are highly complex, and the ProcessMiner platform combines the strengths of data science and machine learning to help improve product quality. Defects, errors, scrap-rates and sub grade products are expensive to any manufacturing operations and ProcessMiner’s turn-key platform identifies and helps fix underlying causes of product quality degradation. Any manufacturer with continuous improvement goals can benefit from the information ProcessMiner delivers its operators through highly intuitive user-interfaces. Among the system’s benefits is a lower cost of operations through energy savings and reductions in raw materials. Additionally, Product Quality improvement lessens waste and improves product margins and production throughput.

Microsoft Azure is a cloud platform created by Microsoft for building, testing, deploying, and managing applications and services through Microsoft managed data centers. Commonly referred to as Cloud Computing it provides users and customers software-as-a-service (SaaS), platform-as-a-service (PaaS), and infrastructure-as-a-service (IaaS) services and supports many different programming language tools and frameworks, including both Microsoft-specific and third-party software systems. This allows customers who are interested in using ProcessMiner’s software the ability to procure the software directly in the Microsoft Commercial Marketplace.

“We are very excited about growing our operating reach with acceptance into the Azure Marketplace,” said Karim Pourak, Co-founder and CEO of ProcessMiner. “Historically, we’ve come across interested manufacturing companies looking to deploy our software for their business, but they were hamstrung because our operating environment was limited to one cloud services provider”. Pourak added, “Now that our platform is hosted with multiple cloud service providers, we’ve lifted that restriction allowing us to scale more rapidly.” he continued “Thanks to the marketing features of Azure, we expect to broaden our awareness to manufacturers shopping for Artificial Intelligence.”

“The Microsoft Commercial Marketplace lets customers worldwide discover, try, purchase and deploy software solutions that are certified and optimized to run on Azure”, said Diego Tamburini, Principal Industry Manager for Manufacturing, at Microsoft Corp. “The Microsoft Commercial Marketplace helps solutions like ProcessMiner reach more customers and markets.”

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How a SaaS provider made microservices deployment safely chaotic

aiuniverse — Sat, 12 May 2018 05:29:19 +0000

Source – techtarget.com

Chaos engineering helps enterprises expect the unexpected and reasonably predict how microservices will perform in production. One education SaaS provider embraced the chaos for its microservices deployment, but it didn’t jump in blindly.

San Francisco-based Remind, which makes a communication tool for educators, school administrators, parents and students, faced a predictability problem with its SaaS offering built on microservices. While traffic is steady during most of the year, back-to-school season is extraordinarily busy, said Peter Hamilton, software engineer at Remind.

“We [were] the No. 1 app in the Apple App Store for two weeks,” he said.

Unforeseen microservices dependencies caused performance degradations and volatile traffic patterns in production. Remind determined that a microservices architecture on cloud resources was not enough alone to scale and maintain availability.

The trade-off with microservices

Agility and cloud-native deployments rely on microservices, along with DevOps culture and CI/CDprocesses.

“Using a microservices architecture is about decoupling the application teams to better achieve the benefit of iteration [from CI/CD] using the agility that cloud provides,” said Rhett Dillingham, senior analyst at Moor Insights & Strategy. The fewer developer dependencies, the faster projects move. But speed is only half of the picture, as Remind discovered; microservices add deployment complexities.

“Once you’re at scale with multiple apps using an array of microservices as dependencies, you’re into a many-to-many relationship,” Dillingham said. The payoff is development flexibility and easier scaling than monolithic apps. The downside is significant debugging and tracing complexity, as well as complicated incident response and root cause analysis.

Expect the unexpected

Remind overhauled its microservices deployment approach with chaos engineering across the predeployment staging step, planning and development. Developers use Gremlin, a chaos engineering SaaS tool, for QA and to adjust microservices code before it’s launched on AWS. Developers from email, SMS, iOS and Android platforms run Gremlin in staging against hypothetical scenarios.

Remind’s product teams average one major release per month. Hamilton noted that requests take tens of microservices to complete. Remind uses unit and functional tests, user acceptance testing and partial release with tracking, but chaos engineering was the missing piece to simulate attacks and expose the chokepoints in the app design.

Remind’s main focus with chaos engineering is to interfere with network requests, Hamilton said. The path for requests through multiple microservices is hard to determine and plan for without heuristic testing, and Remind’s microservices deployments ran into cascading issues because any increased latency downstream causes problems, Hamilton said. Database slowdowns overload web servers, requests queue up and the product sends out error messages everywhere.

“We’re still learning how chaos affects how you do development,” he said. Gremlin recommends development teams run the smallest experiment that yields usable information. “People assume the only way to do chaos engineering is to break things randomly, but it’s much more effective to do targeted experiments where you test a hypothesis,” said Kolton Andrus, CEO of Gremlin.

Remind’s goal is to ensure its product degrades gracefully, which takes a combination of development skills and culture. Designers now think about error states, not just green operations, a mindset that emphasizes a clean user experience even as problems occur, Hamilton said.

Remind explored several options for chaos engineering, including Toxiproxy and Netflix’s Chaos Monkey. It selected Gremlin because it did not want to build out a chaos engineering setup in-house, and it wanted a tool that fit with its 12-factor app dev model.

Chaos vs. conventional testing

Chaos is about finding the unexpected, Andrus said, up a level from functional testing, which ensures the expected occurs.

Unit testing of interconnections breaks down once an application is composed of microservices, because so many individual pieces talk to each other, Andrus said. Chaos engineering tests internal and external dependencies.

Chaos engineering is the deployment and operations complement to load tests, which help tune the deployment environment with the best configurations to prevent memory issues, latency and CPU overconsumption, said Henrik Rexed, performance testing advocate at Neotys, a software test and monitoring tool vendor. In particular, load tests help teams tailor the deployment of microservices on a cloud platform to take advantage of elastic, pay-as-you-go infrastructure and cloud’s performance and cost-saving services.

Remind is particularly aware of the cost dangers from degraded performance and uses chaos engineering to model its resource consumption on AWS in failure modes. “You don’t want bad code to impact infrastructure demand,” Rexed said. And microservices are particularly vulnerable to outrageous cloud bills, because each developer’s or project team’s code is just one difficult-to-size piece of a massive puzzle.

Of course, if you really want to test end-user experience on a microservices deployment, you can do it in production. “As the more advanced development teams running microservices take more operational ownership of the availability of their apps, it is expected that they are proactively surfacing bottlenecks and failure modes,” Dillingham said. But is it worth risking end users’ experience to test resiliency and high availability?

Some say yes. “No matter how much testing you do, it’s going to blow up on you in production,” said Christian Beedgen, CTO of Sumo Logic, which provides log management and analytics tools. “If nothing is quite like production, why don’t we test [there]?”

Testing can leave teams to look all over for the wrong problem or look for the right problem but simply miss it, Beedgen said. QA and unit tests are necessary but don’t ensure flawless deployment. The goal is to put code in production with blue/green or canary deployment to limit the blast radius, monitor for deviations from known behavior and roll back as needed.

Remind is not ready to bring chaos into its live microservices deployment. “We’re conservative about the things we expose production to, but that’s the goal,” Hamilton said. Chaos in production should have limits: “You don’t want a developer to come along and trigger a CPU attack,” he said. A nightmare scenario for the SaaS provider is to huddle up all the senior engineers to troubleshoot a problem that is actually just an unplanned Gremlin attack.

Monitor microservices deployments

While Remind prefers to blow up deployments in staging rather than production, it uses the same monitoring tools to analyze attack results. Remind is rolling out Datadog’s application performance management (APM) across all services. This upgrade to Datadog’s Pro and Enterprise APM packages includes distributed tracing, which Hamilton said is crucial to determine what’s broken in a microservices deployment.

Generally, application teams depend much more on tooling to understand complex architectures, Beedgen said. Microservices deployment typically is more ephemeral than monolithic apps, hosted on containers with ever-evolving runtimes, so log and other metrics collection must be aware of the deployment environment. Instead of three defined app tiers, there is a farm of containers and conceptual abstractions, with no notion of where operational data comes from — the OS, container, cloud provider, runtime or elsewhere — until the administrator implements a way to annotate it.

Microservices monitoring is also about known relationships, Beedgen said. For example, an alert on one service could indicate that the culprit causing performance degradation is actually upstream or downstream of that service. The “loosely coupled” tagline for microservices is usually aspirational, and the mess of dependencies is apparent to anyone once enough troubleshooting is performed, Beedgen said.

Chaos engineering is one way to hone in on these surprising relationships in independent microservices, rather than shy away from them. “That was one of the original goals of testing: to surprise people,” said Hans Buwalda, CTO of software test outsourcing provider LogiGear. In an Agile environment, he said, it’s harder to surprise people and generate all-important unexpected conditions for the application to handle.

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