ServiceNow and NVIDIA Announce Partnership to Build Generative AI Across Enterprise IT

Generative Workflow

ServiceNow and NVIDIA Announce Partnership to Build Generative AI Across Enterprise IT

Built on ServiceNow Platform With NVIDIA AI Software and DGX Infrastructure, Custom Large Language Models to Bring Intelligent Workflow Automation to Enterprises

May 17, 2023

ServiceNow and NVIDIA Announce Partnership to Build Generative AI Across Enterprise IT

Knowledge 2023—ServiceNow and NVIDIA today announced a partnership to develop powerful, enterprise-grade generative AI capabilities that can transform business processes with faster, more intelligent workflow automation.

Using NVIDIA software, services and accelerated infrastructure, ServiceNow is developing custom large language models trained on data specifically for its ServiceNow Platform, the intelligent platform for end-to-end digital transformation. 

This will expand ServiceNow’s already extensive AI functionality with new uses for generative AI across the enterprise — including for IT departments, customer service teams, employees and developers — to strengthen workflow automation and rapidly increase productivity. 

ServiceNow is also helping NVIDIA streamline its IT operations with these generative AI tools, using NVIDIA data to customize NVIDIA® NeMo™ foundation models running on hybrid-cloud infrastructure consisting of NVIDIA DGX™ Cloud and on-premises NVIDIA DGX SuperPOD™ AI supercomputers.

“IT is the nervous system of every modern enterprise in every industry,” said Jensen Huang, founder and CEO of NVIDIA. “Our collaboration to build super-specialized generative AI for enterprises will boost the capability and productivity of IT professionals worldwide using the ServiceNow platform.”

“As adoption of generative AI continues to accelerate, organizations are turning to trusted vendors with battle-tested, secure AI capabilities to boost productivity, gain a competitive edge, and keep data and IP secure,” said CJ Desai, president and chief operating officer of ServiceNow. “Together, NVIDIA and ServiceNow will help drive new levels of automation to fuel productivity and maximize business impact." 

Harnessing Generative AI to Reshape Digital Business 

ServiceNow and NVIDIA are exploring a number of generative AI use cases to simplify and improve productivity across the enterprise by providing high accuracy and higher value in IT. 

This includes developing intelligent virtual assistants and agents to help quickly resolve a broad range of user questions and support requests with purpose-built AI chatbots that use large language models and focus on defined IT tasks. 

To simplify the user experience, enterprises can customize chatbots with proprietary data to create a central generative AI resource that stays on topic while resolving many different requests.

These generative AI use cases are also applicable to customer service agents, allowing for case prioritization with greater accuracy, saving time and improving outcomes. Customer service teams can use generative AI for automatic issue resolution, knowledge-base article generation based on customer case summaries, and chat summarization for faster hand-off, resolution and wrap-up. 

In addition, generative AI can improve the employee experience by helping identify growth opportunities. For example, delivering customized learning and development recommendations, like courses and mentors, based on natural language queries and information from an employee’s profile. 

Full-Stack NVIDIA Generative AI Software and Infrastructure Fuel Rapid Development

In its generative AI research and development, ServiceNow is using NVIDIA AI Foundations cloud services and the NVIDIA AI Enterprise software platform, which includes the NVIDIA NeMo framework. 

Included in NeMo are prompt tuning, supervised fine-tuning and knowledge retrieval tools to help developers build, customize and deploy language models for enterprise use cases. NeMo Guardrails software is also included and enables developers to easily add topical, safety and security features for AI chatbots.

Made Me Think: About SynthAI

Still thinking this, Like to test its usefulness, efficiency.  Join me.

For B2B Generative AI Apps, Is Less More?   by Zeya Yang and Kristina Shen in Andreessen Horowitz

AI, machine & deep learning  enterprise & SaaS  Generative AI

Table of contents

Wave 1: Crossing the bridge from consumer to enterprise

What’s the cost (or benefit) of disrupting the workflow?

Wave 2: Converging information for improved decision making

Implementing SynthAI

A battle to own the workflow

We’ve watched large language models (LLMs) become mainstream over the past few years and have studied the implementations in the context of B2B applications. Despite some enormous technological advances and the presence of LLMs in the general zeitgeist, we believe we’re still only in the first wave of generative AI applications for B2B use cases. As companies nail down use cases and seek to build moats around their products, we expect a shift in approach and objectives from the current “Wave 1”  to a more focused “Wave 2.”

Here’s what we mean: To date, generative AI applications have overwhelmingly focused on the divergence of information. That is, they create new content based on a set of instructions. In Wave 2, we believe we will see more applications of AI to converge information. That is, they will show us less content by synthesizing the information available. Aptly, we refer to Wave 2 as synthesis AI (“SynthAI”) to contrast with Wave 1. While Wave 1 has created some value at the application layer, we believe Wave 2 will bring a step function change.

Ultimately, as we explain below, the battle among B2B solutions will be less focused on dazzling AI capabilities, and more focused on how these capabilities will help companies own (or redefine) valuable enterprise workflows. ... '      (charts at the link at Andreessen)

ML Physics Platform NVIDIA Modulus is Open

 New things out of NVIDIA Resources, Fascinating,  Sample Uses?

Machine Learning Platform NVIDIA Modulus Is Now Open Source

By Bhoomi Gadhia, Ram Cherukuri and Kristen Perez

Physics-informed machine learning (physics-ML) is transforming high-performance computing (HPC) simulation workflows across disciplines, including computational fluid dynamics, structural mechanics, and computational chemistry. Because of its broad applications, physics-ML is well suited for modeling physical systems and deploying digital twins across industries ranging from manufacturing to climate sciences.

NVIDIA Modulus is a state-of-the-art physics-ML platform that blends physics with deep learning training data to build high-fidelity, parameterized surrogate models with near-real-time latency. The surrogate models built using NVIDIA Modulus help a wide range of solutions including weather forecasting, reducing power plant greenhouse gasses, and accelerating clean energy transitions.

NVIDIA Modulus customer success stories are proving the platform’s incredible utility across industries. However, physics-ML is a relatively new field in the deep learning arena, with significant challenges both at the research level as well as at the application front. This is due to the unique requirements needed to satisfy physics-ML rules:

The need for a deep learning model to obey the governing principles of a physical system.

The need for new deep learning model architectures for a specific class of problems, such as those that can satisfy fluid mechanics laws.

The need for generalizable model architectures and algorithms that can serve across different applications.

These challenges require innovation and research across several domains. More importantly, these problems require a strong collaboration between respective domains, industries, and deep learning experts. This level of collaboration requires tools and technologies that remove barriers between researchers, teams, and even industries, to enable the community to build on each other’s work.

Because simulations are critical to these disciplines and the industries that employ them, there is a demand for building and demonstrating confidence that AI can meet and surpass the current simulation approaches. This requires transparency so research can identify the limitations and provide breakthroughs to enable more transformative technologies.

Now, to facilitate the collaboration, transparency, and accountability needed, NVIDIA Modulus has become an open-source platform available for physics-ML.

Physics-ML open-source workflows

This new open-source environment provides significant benefits for AI developers and domain experts across industries in several ways:

Collaboration: An open-source workflow enables you to collaborate more easily with colleagues and share your work with a wider community. By making data, code, and methods openly accessible, you can work together more effectively to address complex physics-ML questions.

Transparency: Open-source workflows can increase the transparency and reproducibility of physics-ML research. By publishing code and data, you can enable other researchers to verify and replicate your results, which can help to build greater trust in the scientific findings.

Innovation: Open-source workflows can facilitate innovation by enabling you to build on other researchers’ work more easily. By providing access to a shared repository of tools and techniques, open-source workflows can help to accelerate the pace of discovery and enhance the quality of research outputs.

Accessibility: Open-source workflows can help to make research more accessible to a wider range of stakeholders, including drug development managers, national lab directors, policymakers, journalists, and members of the public. By providing clear, accessible information about physics-based modeling research, open-source workflows can help build greater awareness and understanding.

Overall, an open-source workflow can help AI developers and engineering and science domain experts to work more collaboratively, transparently, innovatively, and with greater accessibility to enhance the impact and relevance of their research.

Accessing NVIDIA Modulus open-source software

NVIDIA Modulus is available as open-source software (OSS) under the simple Apache 2.0 license. .... ' 

Arterys: The future of precision medicine

Brought to my Attention , aimed at healthcare practices...

Arterys:  The future of precision medicine   that only human + AI can achieve. 

The Arterys platform extracts actionable insights from medical images to add clinical value, improve diagnostic decision making, efficiency and productivity.

Arterys is the medical imaging AI platform allowing you to weave leading AI clinical applications directly into your existing PACS or EHR driven workflow to make it a natural extension of what you already do.

We are making AI real by improving physician experience, accuracy of diagnosis and treatment, financial performance and outcomes that matter to patients and providers.

Accessible anywhere from any validated device via the cloud for faster performance, ease of deployment with no PHI exchange and completely secure.

See how Arterys is transforming healthcare through deep learning and AI

History:  It started at Stanford

Before we founded Arterys, we were graduate students, searching for a way to make medicine better. Faster. More precise.

We pushed the boundaries of our disciplines until they finally overlapped, intersecting cloud computing with cutting edge medical image acquisition.

Soon after, Arterys was founded on our shared beliefs:

Faster, smarter diagnosis where it counts the most

We started by improving diagnoses for newborns and kids with heart defects. At the time, pediatric cardiovascular disease was diagnosed with ultrasound, which only offers a partial view of the heart with no blood flow quantifications. Or an MRI captured over grueling hours on the scanning table.

Without accurate quantification, cardiac medicine was an educated guess with a high error rate.

The solution was obvious: 4D Flow technology to visualize and quantify blood flow in mere minutes.

But image archiving servers in hospitals couldn’t read 4D Flow’s big data files. So we applied a system that could: cloud computer processing.

With cloud computing, we could put those life-saving 4D Flow images into radiologists hands. In mere moments, physicians could diagnose and make accurate treatment decisions.

Anyone with access to a web browser could access Arterys to quantify regurgitant flow and determine if a child with heart defects needed surgery.

We were helping to save kids’ lives with better diagnoses.

But we weren’t satisfied.

Even though we put 4D Flow at radiologists’ fingertips, we still saw physicians manually drawing contours to quantify the size of cardiac ventricles.

Artificial Intelligence gets real

We decided to power Deep Learning AI with cloud computing GPUs to automatically quantify and segment ventricles as accurately as manual measurements by experienced physicians. In 2017, our technology received the first ever US FDA clearance for leveraging cloud computing and deep learning in a clinical setting.

But we were still not satisfied.

Physicians around the world approached us, asking if we could apply the same AI cloud computing approach to image processing and analysis to cancer patients. Then to liver patients. Then lung, breast, brain, and everything in between.

The word was getting out that radiologists could receive automatic accurate measurements through deep learning powered in the cloud.

All of your data, all in one place. Simple.

Despite our breakthroughs, existing radiology workflows put AI image acquisition and analysis out of reach. Working outside of a unified platform, physicians switched interfaces multiple times an hour to access different AI imaging tools. This stole vital time that could be spent with a patient. It was time to transcend silos with one united AI web platform.

Soon after, we received FDA clearance for our Arterys web-based AI imaging platform.

Despite these successes, we are still not satisfied.

We want to push medicine to make it even faster and more precise. We’ve just gotten started  .... ..'

Amazon Launches tools to Manage Workflows

We were mainly looking at how to manage work flows in general,  but the same goal really.    Replace some of the resource components with robots, people, analytics external flows.  Like to see the examples of how and measures of results.  Reinforcement training would seem to be a natural approach.  

Amazon launches reinforcement learning tools to manage robots’ workflows  By Kyle Wiggers  @Kyle_L_Wiggers  in Venturebeat

Amazon today launched SageMaker Reinforcement Learning (RL) Kubeflow Components, a toolkit supporting the company’s AWS RoboMaker service for orchestrating robotics workflows. Amazon says that the goal is to make it faster to experiment and manage robotics workloads from perception to controls and optimization, and to create end-to-end solutions without having to rebuild them each time.

Robots are being used more widely for purposes that are increasing in sophistication, like assembly, picking and packing, last-mile delivery, environmental monitoring, search and rescue, and assisted surgery. In China, Oxford Economics anticipates 12.5 million manufacturing jobs will become automated, while in the U.S., McKinsey projects machines will take upwards of 30% of such jobs. As for reinforcement learning, it’s an emerging AI technique that can help develop solutions for the kinds of problems that are increasingly cropping up in robotics.  ... "

CAS Signs Enterprise Agreement with P&G

Considerable agreement for data and process workflow.

CAS announces signing of multi-year enterprise agreement with P&G
Agreement expands long-term partnership to support CPG leader’s commitment to disruptive innovation.

Global access to leading scientific information solutions portfolio informs business strategy and fuels efficient R&D to deliver superior consumer product performance  

Columbus, Ohio - September 1, 2020 – CAS, a division of the American Chemical Society specializing in scientific information solutions, today announced they have signed a multi-year agreement with global consumer packaged goods leader The Procter & Gamble Company (P&G) providing P&G enterprise-wide access to a comprehensive portfolio of CAS scientific information solutions.

P&G is recognized as a leading innovator in the CPG industry across multiple product categories including beauty care, paper products, fabric care, home care, and health. The company was recently named one of Forbes Most Innovative Companies and led the HolyGrail project that won the Best Sustainable Packaging Award for 2019. “Comprehensive and efficient access to research data are critical for our innovation process,” said Gerard Baillely, Senior Vice President of Research & Development, Corporate Functions for P&G. “CAS’s expanding portfolio of products and services helps us make better decisions throughout our R&D workflow from early-stage research to commercialization so we can develop better, safer solutions for our consumers that improve their quality of life.” 

P&G has relied on CAS information solutions for decades. This new agreement expands that commitment by opening access to CAS’s most advanced solutions to enhance productivity across many sectors. R&D teams will benefit from additional content, workflow features, and predictive design capabilities that have been proven to reduce the time spent on key research tasks by at least 50%. .... " 

Platforms for Data Science

Good piece in O'Reilly about this question,  largely-non technical and useful thoughts. Passing it on ...

Why Best-of-Breed is a Better Choice than All-in-One Platforms for Data Science
All-in-one platforms built from open source software make it easy to perform certain workflows, but make it hard to explore and grow beyond those boundaries.

By Matthew Rocklin and Hugo Bowne-Anderson,  in O' Reilly 

Do you buy a solution from a big integration company like IBM, Cloudera, or Amazon?  Do you engage many small startups, each focused on one part of the problem?  A little of both?  We see trends shifting towards focused best-of-breed platforms. That is, products that are laser-focused on one aspect of the data science and machine learning workflows, in contrast to all-in-one platforms that attempt to solve the entire space of data workflows.

This article, which examines this shift in more depth, is an opinionated result of countless conversations with data scientists about their needs in modern data science workflows.  ... " 

(Much more at the link) 

Evolution of Distributed Systems on Kubernetes

Ultimately in delivery,  workflow design is key, here a presentation on the topic.

Kubernetes  is an open-source container-orchestration system for automating application deployment, scaling, and management ... 

The Evolution of Distributed Systems on Kubernetes

Bilgin Ibryam takes us on a journey exploring Kubernetes primitives, design patterns and new workload types.

Bio
Bilgin Ibryam is a product manager and a former architect at Red Hat. In his day-to-day job, he works with customers from all sizes and locations, helping them to be successful with adoption of emerging technologies through proven and repeatable patterns and practises. His current interests include enterprise blockchains, cloud-native data and serverless.

About the conference
Software is changing the world. QCon empowers software development by facilitating the spread of knowledge and innovation in the developer community. A practitioner-driven conference, QCon is designed for technical team leads, architects, engineering directors, and project managers who influence innovation in the  ... " 

Zapier for Automating Workflows

Had looked at this some time ago, interesting approach.

Connect Your Apps and Automate Workflows
Easy automation for busy people. Zapier moves info between your web apps automatically, so you can focus on your most important work.
More Than 1,500 Apps, Better Together

Stick with the tools that work for you. Zapier connects more web apps than anyone, and we add new options every week. We integrate with apps such as Facebook Lead Ads, Slack, Quickbooks, Google Sheets, Google Docs & many more!

Anyone Can Build Workflows With A Few Clicks
Our editor was made for do-it-yourself automation. Set up Zaps without developer help.  Enrich your workflows with Built-In Apps

Zapier's Built-in Apps come with every account. Use them to create powerful workflows without using separate services.

FREE FOREVER
Start with the basics as long as you need. Grab free tools that help you do more with your most-used apps.

Build one-to-one connections with your apps
Automate basic tasks
Get help from our top-notch support team

PREMIUM PLANS
Get premium tools to build advanced workflows. Our Premium Plans give you the tools to automate more, so you can work less. ...... " 

DAML: Contract Language of Distributed Ledgers

Quite an interesting piece on contract languages and 'Smart Contracts".  A recent proposal made me look deeper into this idea, especially as it might connect to supply chain and procurement efficiencies.  Considerable 7-page piece, admittedly technical here, but worth taking a close look.  And of  course this gets to workflow, always a particular angle of interest of mine.  Below just a few excerpts.

Case Study
DAML: The Contract Language of Distributed Ledgers
A discussion between Shaul Kfir and Camille Fournier

When Shaul Kfir cofounded Digital Asset in 2014, he was out to prove something to the financial services industry. He saw it as being not only hamstrung by an inefficient system for transaction reconciliation, but also in danger of missing out on what blockchain technology could do to address its shortcomings.

Since then, Digital Asset has gone to market with its own distributed-ledger technology, DAML (Digital Asset Modeling Language). And that does indeed take advantage of blockchain—only not in quite the way Kfir had initially intended. He and Digital Asset ended up taking an engineering "journey" to get to where they are today.

Kfir readily admits his own background in cryptography and cryptocurrency—both as a researcher (at Technion and MIT) and as a cryptocurrency entrepreneur in Israel—had more than just a little to do with the course that was originally charted. As for lessons learned along the way, Camille Fournier, the head of platform development for a leading New York City hedge fund, helps to elicit those here. She brings to the exercise her own background in distributed-systems consensus (as one of the original committers to the Apache Zookeeper Project) and financial services (as a former VP of technology at Goldman Sachs).  ....  "

" .... To clarify, think about how large technology companies use their infrastructure to achieve greater agility. Most of them today have some logically centralized infrastructure that includes a central code repository and a CI/CD [continuous integration/continuous delivery] system. If these ideas can be expanded to an industry level in the sense that you can start rolling out workflows as smart contracts that are written only once and then made available for everyone to build upon, that's clearly more efficient than leaving it to each organization to write its own workflows. ... " 

Webinar: Driving Business Outcomes with AI

Leaders in the area of machine learning and AI talk goals and workflow:

On-Demand Webinar

Enterprises understand that driving business outcomes with machine learning and AI will soon become a critical driver for success. Yet, many struggle to connect together siloed data pipelines and artisanal data science experiments into agile and repeatable processes to drive scale and impact.

Hear Nielsen’s Chief Research Officer Mainak Mazumdar and Forrester Senior Analyst guest speaker Kjell Carlsson, PhD share experiences and perspectives into unifying data science and engineering with business needs. Learn how teams operationalize machine learning models and AI more rapidly, with insights into:

- Improving model development performance from 1 week to less than 2 hrs 
- Transforming data science workflows and deepening team collaboration
- Accelerating the end-to-end machine learning lifecycle  .... " 

Analytics for Management

Good piece in the ACM, full text linked to below. And further it is not only about classical analytics but also about the emergence of cognitive aspects of AI in this space.    These approaches are more closely connected to the actual decisions that managers make. And how those decisions link together into a decision process.   We are not completely there yet, but approaching.  Management decisions are always a sequence of decisions, by multiple people,  in context.  That's not expressed enough in the below.

Analytics for Managerial Work  By Vijay Khatri, Binny M. Samuel 
Communications of the ACM, April 2019, Vol. 62 No. 4, Page 100
10.1145/3274277

A 2014 IDC report predicted that by 2020, the digital universe—the data we create and copy annually—will reach 44 zettabytes, or 44 trillion gigabytes.10 With the explosive growth in organizational data, there is increasing emphasis on analytics that can be used to uncover the "hidden potential" of data. A 2014 Society for Information Management survey found analytics/business intelligence to be #1 among the top 15 most significant IT investments in the prior five years.12 It is not surprising that business analytics is increasingly central to managerial decision making within business functions: finance, marketing, human resources, and operations. For example, cash-flow analytics, shareholder-value analytics, and profit/revenue analytics are increasingly important aspects of the finance function. A 2017 survey of chief marketing officers found companies spend 6.7% of their marketing budgets on analytics and expect to spend 11.1% over the next three years.16 A 2017 Deloitte survey of HR managers found over 71% of the surveyed companies see people analytics as a high priority.3 Analytics is increasingly used in operations management for demand forecasting, inventory optimization, spare parts optimization, warranty management, and predictive asset maintenance. Acknowledging extreme deficiency of data literacy among today's managers, by 2020, 80% of organizations will embark on data-literacy initiatives.  .... "

Digital Assistants at Work

Not unexpected that we will see these kinds of capabilities to augment people in the workplace.

Digital workplace technologies promise a personal digital assistant for all employees PRNewswire

ISG Provider Lens™ Digital Workplace Archetype Report looks at how the services of 22 vendors fit into the business models of five types of clients

STAMFORD, Conn., March 7, 2019 /PRNewswire/ -- As enterprises move toward digital workplace technologies, all of their employees will be able to have their own personal digital assistants, according to a new report published today by Information Services Group (ISG) (Nasdaq: III), a leading global technology research and advisory firm.

The ISG Provider Lens™ Digital Workplace Archetype Report sees enterprises adopting such virtual assistants, including advanced agents that can book meetings on behalf of employees or suggest product changes based on end-user chat sessions. Digital workplace technologies can also offer remote device support through augmented or virtual reality and can provide smart offices and meeting rooms, among other benefits.

"With digital and automation technologies, having a personal assistant is no longer a luxury that only senior executives can afford," said Esteban Herrera, partner and global leader of ISG Research.

"Modern digital workplace technologies provide every worker with a personal digital assistant that can take over mundane tasks to help the user explore value-added work and advance their careers."

In addition to automation, digital workplace technologies can assist enterprises with the consumerization of IT environments, the report said. Employees who have long used smartphones now expect cloud- and app-based services in the workplace, and automated solutions like chatbots connect with channels like Amazon Alexa, Microsoft Cortana and Google Assistant to provide a consumer-like experience at work.  ...." 

Assessing Progress in Automation Technologies

Useful coverage of automation.  I like that this is defined broadly.

Assessing progress in automation technologies  In O'Reilly by Ben Lorica.
When it comes to automation of existing tasks and workflows, you need not adopt an “all or nothing” attitude.

In this post, I share slides and notes from a keynote Roger Chen and I gave at the Artificial Intelligence conference in London in October 2018. We presented an overview of the state of automation technologies: we tried to highlight the state of the key building block technologies and we described how these tools might evolve in the near future. .... " 

Watson Compare and Comply for Contracts

Also brought to my attention while looking at 'agreements' and 'contracts' as they integrate with work process flow, value generation and risk.      IBM Watson's look at this kind of problem

Watson Compare and Comply
Contract governance got you down?
AI can help streamline contract workflows to save time and improve accuracy. Learn more in the webinar at the link.

Introducing Watson Compare & Comply ...

Wal-Mart's New Productivity Apps

Whats especially interesting here is what Wal-Mart is emphasizing for the workflow of their employees. includes description and detail of their operation.  Nice effort.

Walmart's new apps help employees complete tasks  in ProgressiveGrocer

Walmart is releasing a string of in-house apps for mobile devices to make work smoother and more efficient for its associates. The apps include an information hub and assistance with receiving, stocking and price changes.  ... 

Includes mention of  In-store virtual reality training through Oculus Go headsets.  ...

Watson AI for Industries

Watson AI aimed at a number of industries, aiming at key workflows for each, much more below

Watson AI helps your industry do more
Watson AI is at work globally – in farms, factories and offices – streamlining workflows, increasing productivity, and freeing workers up for higher-value tasks.

Industry Solutions

Advertising
Customer Engagement
Education
Financial Services
Health
IoT
Media
Talent
Work ... 

Intelligent Workflow Drones

Logical, first I had seen this stated this way. But then anything that does work to perform useful tasks or gather data should ideally be integrated into a workflow. Formally or informally.

Intelligent Workflow Drones  by Biren Gandhi   in Cisco Blog

Commercial drones are moving from a novelty item to an indispensable business tool, with PwC pegging the potential opportunity size at $127B in its report, “Clarity from Above”. However, there is a key element that is needed before drones can be successful in enterprise applications: they must be integrated into regular workflow systems, instead of existing in their own silos.

It’s like saying “let’s talk” instead of “let’s talk over a telephone” in the world of voice communication.

An example of this is showcased below. A few months ago, Cisco’s Enterprise Routing and Mobility team partnered with FlytBase and AeroTestra to showcase end-to-end enterprise workflows involving fleets of drones. Cisco’s Spark, WebEx and Drone ASAP products were integrated with Built.io’s Digital Transformation Platform to create this unique solution .... "

Transitioning to Industrial AI

Some quite useful thoughts here, about work flow and production versus testing, retesting and retraining systems. Ultimately we learn this, but sometimes painfully.  Not different from implementing any kind of analytics or solution improvement based system.  Its fairly rare that it is rigorously done.

Transitioning from Academic Machine Learning to AI in Industry
Jeremy Karnowski and Emmanuel Ameisen, Insight AI  .... " 

Development Workflows for Data Science

Nicely done free Book/PDF on the topic.  My experience is most people don't consider workflow at all, so even an overview of a resource like this is worthwhile.  Consider setting up a standard for methods in your company.  I like the fact that several options were considered.   Requires minimal registration.

" ... Development Workflows for Data Scientists

GitHub partnered with O’Reilly Media to examine how data science and analytics teams at several data-driven organizations are improving the way they define, enforce, and automate development workflows—including:

Defining team structure and roles
Asking interesting questions
Examining previous work
Collecting, exploring, and modeling data
Testing, documenting, and deploying code to production
Communicating the results

This illuminating report shows how, even though the pace of change is rapid and the desire for the knowledge and insight from data is ever growing, the dual disciplines of software engineering and data science are up for the task. .... "