Jason Shepherd, Ecosystem VP, has been busy building this edge ecosystem and took time to chat with me about this news from ZEDEDA. The bullet points below summarize. First, I thought I do a reminder about “edge orchestration”, the core of ZEDEDA’s offering. Essentially edge orchestration makes it easier (on the typical engineering scale of easy) to build management and security of hardware and applications as you build out your IoT system.
Partnerships are a growing trend, and this release details several both with commercial companies and with consortia. Google Cloud makes another appearance on my blog, as well. As I’ve said, these enterprise cloud services are getting very interesting. This release focuses on the energy vertical. Look for additional focus industries coming in the future.
- Google collaboration to leverage ZEDEDA’s expertise in distributed edge computing
- Additional partnerships include Juniper Networks, Advantech, Dianomic and the IOTA Foundation
- ZEDEDA joins LF Energy, OSDU and Project Alvarium to advance open collaboration to address industry challenges
The energy industry is undergoing rapid evolution as it adjusts to sweeping changes in everything from aging infrastructure to an imbalance in traditional ways consumers use and produce energy.
To address this complex equation, ZEDEDA, the leader in orchestration for the distributed edge, today announces significant advances in three key areas—partnerships, industry consortiums and a developer program—that position open collaboration as a key enabler for the industrial market, including companies looking to address the macro trends in the energy space.
ZEDEDA will provide its zero-trust, cloud-based orchestration solution for distributed edge computing to help Google Cloud customers securely scale deployments of any edge application, including AI/ML, on choice of hardware. This is in response to the growth of cloud infrastructure in industrial markets for centralized data storage and management, cross-facility analytics and visibility, and hyperscale compute capabilities to augment workloads deployed at the edge.
Together with joint edge application partners like Dianomic, customers will be able to drive new efficiencies through insights derived from edge environments. While the solution is horizontal in nature, the partnership is placing an initial focus on the energy space with target edge assets, including wind turbines, solar farms, and more.
“We see a number of edge use cases for multiple industries, including energy, that can be addressed with this Google Cloud partnership,” said Said Ouissal, ZEDEDA founder and CEO. “Our zero-touch provisioning and simplified lifecycle management enable businesses to start realizing business value with Google Cloud together with choice of edge hardware and applications.”
“As high-speed connectivity grows, organizations with presences at the network edge stand to benefit from low-latency access to business applications and cloud capabilities that can help modernize business processes, manage data, and more,” said Tanuj Raja, Global Head, Strategic Partnerships at Google Cloud. “We’re excited that ZEDEDA will make its edge orchestration capabilities available with Google Cloud, helping enable greater access to these applications and capabilities for customers across industries.”
In addition to its recent partnership with Agora, ZEDEDA has added additional partnerships to support energy customers facing key challenges such as digitizing legacy infrastructure, remotely monitoring critical assets, and balancing the grid with unpredictable renewable energy sources. Those partnerships announced today include:
- Juniper Networks: a joint offering for secure-edge computing with Juniper’s Session Smart Router and the vSRX Virtual Firewall secure networking capabilities on top of ZEDEDA’s zero-trust edge orchestration foundation. Together, ZEDEDA and Juniper provide customers with the simplicity of cloud orchestration and the flexibility of either backhauling data to the cloud or keeping it on-prem.
- Dianomic: an edge application platform for Industrial IoT use cases. ZEDEDA’s edge orchestration solution simplifies secure deployment of Dianomic’s FogLAMP platform and management of the underlying hardware.
- IOTA Foundation: a key collaborator for Project Alvarium, focused on facilitating trust in interconnected ecosystems through its feeless Distributed Ledger Technology (DLT). IOTA is leading a number of decentralized, innovative projects in the energy space.
“We are pleased to be working with ZEDEDA to provide advanced solutions for the energy sector,” said Karen Falcone, Sr. Director of Enterprise Marketing at Juniper Networks. “Combining our broad networking experience, including software-defined capabilities with the Juniper Session Smart Router and the vSRX Virtual Firewall with ZEDEDA’s Zero Trust architecture, provides customers with a robust security foundation for any mission-critical use cases within the energy vertical and beyond.”
“Together, Dianomic, ZEDEDA and Google deliver a complete Industry 4.0 edge stack built on an open-source foundation,” said Tom Arthur, CEO at Dianomic. “The energy industry faces new challenges as its generation and storage systems become massively distributed. Combining Dianomic’s FogLAMP for edge application development and data acquisition with ZEDEDA’s secure orchestration solution and Google’s state-of-the-art ML and cloud services delivers a robust and flexible foundation for edge computing challenges in industrial use cases.”
ZEDEDA is also increasing focus with Advantech as a strategic hardware partner for the energy space due to its broad portfolio of edge computing offerings, including models with C1/D2 certification for critical environments and new NVIDIA Jetson-enabled boxes to power edge AI.
“We are always looking for new and innovative ways to make edge computing solutions easier, more efficient, and more secure for customers in an industry that is seeing tremendous change,” said Jeff Brown, Sr. Sales Director for Advantech’s Industrial IoT Group. “Working with strategic, domain-focused partners such as ZEDEDA and Dianomic allows us to do just that. Advantech has one of the broadest hardware portfolios in the market, and our expansive Class 1, Division 2 product line allows for reliable, rugged solutions in remote and hazardous locations. We are thrilled to be a part of this ground-breaking group that’s putting digital transformation into the hands of the energy industry.”
Collaboration with PVHardware
ZEDEDA continues to make great progress with energy customers, recently closing a win with PVHardware. The company is using ZEDEDA’s orchestration solution to deploy and manage edge hardware and applications that aid in tracking the sun to maximize power generation.
“As we looked to leverage edge computing to help maximize power generation, we needed a solution to securely scale deployments in solar plants, including the ability to remotely manage the overall deployment lifecycle,” said Ivan Arkitpoff, CTO at PVHardware, “ZEDEDA provided us with a solution that makes it easy to deploy hardware and applications in the field and perform fail-proof updates from the cloud without having to send a technician out to the plant.”
ZEDEDA Joins Industry Consortium Groups LF Energy, OSDU and Project Alvarium
The emerging trends that face the energy industry are so wide-ranging that they require industry collaboration to address. ZEDEDA has joined several industry consortiums to drive standards via open source:
- LF Energy, a Linux Foundation project, is seeking to accelerate the energy transition of the world’s power and transportation systems through open-source technology. As a member, ZEDEDA will work with the LF Energy community to integrate EVE-OS into its reference architecture.
- The OSDU Forum, part of the Open Group and focused on developing an open, standards-based foundation to accelerate innovation in the energy space. ZEDEDA and Dianomic are assisting in building a proof-of-concept for OSDU’s edge computing reference architecture leveraging EVE-OS and Fledge from LF Edge, with more open-source efforts to be integrated over time.
- Project Alvarium, an emerging project within the Linux Foundation, is focused on enabling data confidence through the concept of trust fabrics. ZEDEDA is collaborating with Dell, the IOTA Foundation, Intel and other industry leaders to formally launch the project, with energy being an initial focus vertical.
“ZEDEDA’s capabilities enable zero-touch deployments of IOTA and Project Alvarium, creating scalable connective fabrics at the edge,” said Mat Yarger, Head of Smart Mobility at the IOTA Foundation. “This can enable a peer-to-peer utility of data in the energy sector, which has massive implications to address critical problems with grid management and oversight. It will also allow new business models around electric vehicles and smart grids to thrive, as well as the realization of new asset structures. All with trust being ingrained in how these systems operate.”
To learn more about how ZEDEDA is partnering with Google Cloud and Dianomic on edge solutions, register for ZEDEDA Transform 2021 on August 18-19. This free online event brings together experts from across the edge computing and IoT landscape to discuss today’s trends, challenges and opportunities. (Shameless self-promotion plug, you might find me among the participants there.)
What these cloud companies are doing with their platforms is becoming amazing. This news is from Google—a little later than first Amazon Web Services and then Microsoft Azure. It is quickly adding some interesting capabilities. Once again, we’re seeing artificial intelligence (AI) built into so many applications that we should cease to have surprise and awe. It’s a tool—and a powerful one if used appropriately. Check out this vision inspection solution.
Google Cloud today launched Visual Inspection AI, a new purpose-built solution to help manufacturers, consumer packaged goods companies, and other businesses worldwide reduce defects and deliver significant operational savings from the manufacturing and inspection process.
Today, defects in products such as computer chips, cars, machinery, and other products cost manufacturers billions of dollars annually. In fact, quality-related costs can consume 15% to 20% of sales revenue. In addition, high production volumes outpace the ability of humans to manually inspect each part.
Google Cloud has traditionally supported manufacturing quality control through its general purpose AI product, AutoML. Today, it is taking the next step by offering a purpose-built solution for manufacturers. Using Google Cloud’s leading computer vision technology, Visual Inspection AI automates the quality control process, enabling manufacturers to quickly and accurately detect defects before products are shipped. By identifying defects early in the process, customers can improve production throughput, increase yields, reduce rework, and reduce return and repair costs. Visual Inspection AI operates across a wide range of industries and use cases, potentially saving manufacturers millions of dollars at each facility.
Based on pilots run by Google Cloud customers, Visual Inspection AI can build accurate models with up to 300 times fewer human-labelled images than general-purpose ML platforms. This allows the solution to be deployed quickly and easily in any manufacturing setting. In addition, Visual Inspection AI customers improved accuracy in production trials by up to 10X compared with general-purpose ML approaches. And, unlike competing solutions that use simple anomaly detection, Visual Inspection AI’s deep learning allows customers to train models that detect, classify, and precisely locate multiple defect types in a single image.
“AI has proven to be particularly beneficial in helping to automate the visual quality control process for manufacturers—a particular pain point felt by the industry. We’ve been delighted by the strong interest in Visual Inspection AI, and we look forward to supporting more organizations as they continue to find innovative new ways to deploy AI at scale,” said Dominik Wee, Managing Director Manufacturing and Industrial at Google Cloud.
“We’ve been listening to the specific needs of the industry and have brought the best of Google AI technologies to help address those needs. The outcome is an AI solution that, built upon years of computer vision expertise, is purpose-built to solve quality control problems for nearly any type of discrete manufacturing process,” said Mandeep Waraich, Head of Product for Industrial AI at Google Cloud.
Building and training machine learning models typically requires deep AI expertise, as well as extensive databases containing thousands of labelled images. Such systems usually run in an on-premise data center or in the cloud, making them difficult to deploy at scale across the factory floor. With Google Cloud Visual Inspection AI:
- No special expertise is required. Quality, test, and manufacturing engineers can use the solution without any computer vision or AI subject-matter expertise. An intuitive user interface guides employees through all of the necessary steps.
- Engineers can get started quickly and build more accurate models. Machine learning models can be trained using as few as 10 labelled images (vs. thousands) and will automatically increase in accuracy over time as they are exposed to more products.
- Full edge-to-cloud capability: Inspection models can be downloaded to machines on the factory floor and run autonomously at the edge, whether it be for data governance reasons or to improve latency. At the same time, Visual Inspection AI is fully integrated in Google Cloud’s portfolio of analytics and ML/AI solutions. This enables manufacturers to combine insights from Visual Inspection AI with other data sources on the shop floor and beyond, for instance to identify root causes of quality problems or to cross-reference with supplier and customer data.
- Problems are resolved faster. Not only does the solution flag a defective component, but also Visual Inspection AI can locate and identify the specific defect within each part, which reduces the time spent by engineers to diagnose problems, rework parts, and implement process improvements.
“Google Cloud’s approach to visual inspection is the roadmap most manufacturing companies are looking for. Manufacturers want flexibility, scale, inherent edge-to-cloud capabilities, access to both real-time and historical data, and ease of use and maintainability”, said Kevin Prouty, Group Vice President at IDC. “Google is one of those companies that has the potential to bring together IT, OT and an ecosystem of partners that manufacturers need to deploy AI on the shop floor at scale.”
Wide Range of Use Cases for Visual Inspection AI
Automotive manufacturers: A typical vehicle factory produces around 300,000 vehicles each year, and up to 10% of them may have parts that underwent rework or replacement during the manufacturing process to address some type of production defect . By automatically identifying defects in paint finish, seat fabrication, body welds, and end-of-line testing of mechanical parts, Visual Inspection AI could save automakers more than $50 million annually per plant.
“Google Cloud’s strength in machine learning and artificial intelligence is accelerating Renault’s Industry 4.0 transformation. We are adopting innovative computer vision solutions like Visual Inspection AI, AutoML and Vertex AI to implement more accurate quality controls with a significantly reduced time to market at a lower cost. We are working now on deploying these new tools in every Renault factory. Renault is ready for future-oriented manufacturing and welcomes the partnership with Google Cloud,” said Dominique Tachet, Digital Project Leader, Renault.
Electronics manufacturing services (EMS): Of the 15 million circuit boards produced each year in a typical EMS factory, as many as 6% may be reworked or scrapped during the assembly process due to internal or external quality failures, such as soldering errors or missing screws . Reducing rework and material waste can save such a facility nearly $23 million each year.
“It’s been amazing to work with Google Cloud to bring innovative machine learning and computer vision technologies to our quality processes. Engineers from FIH Mobile, a subsidiary of Foxconn, trust Google Cloud and we are achieving considerable product improvements through our collaboration. We cannot wait to roll out the Visual Inspection AI solution further across our extensive PCB manufacturing operations.” said Sabcat Shih, Senior Associate Manager, FIH Mobile.
Semiconductor production: A chip fabrication plant that produces 600,000 wafers per year could see yield losses of up to 3% from cracks and other defects . Implementing Visual Inspection AI can reduce production delays and scrap, saving up to $56 million per fab.
“With the shortage of AI engineers, Visual Inspection AI is an innovative service that can be used by non-AI engineers. We have found that we are able to create highly accurate models with as few as 10-20 defective images with Visual Inspection AI. We will continue to strengthen our partnership with Google to develop solutions that will lead our customers’ digital transformation projects to success.” said Masaharu Akieda, Division Manager, Digital Solution Division, KYOCERA Communication Systems Co., Ltd.
 “Cost of Quality,” American Society for Quality (ASQ).
 “Internal documents reveal the grueling way Tesla hit its 5,000 Model 3 target,” Business Insider
 “Capturing the value of good quality in medical devices,” McKinsey & Company
 “Taking the next leap forward in semiconductor yield improvement,” McKinsey & Company
- Visual Inspection AI solution webpage
- Visual Inspection AI launch blog
- Visual Inspection AI overview video
- FIH Mobile case study
- Keep up with the latest Google Cloud news on our newsroom and blog
Google Cloud accelerates organizations’ ability to digitally transform their business with the best infrastructure, platform, industry solutions and expertise. We deliver enterprise-grade cloud solutions that leverage Google’s cutting-edge technology to help companies operate more efficiently and adapt to changing needs, giving customers a foundation for the future. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to solve their most critical business problems.
In this year of all things data, I’ve only just discovered a company called Datadobi (news items in March and May). It bills itself as the “global leader in unstructured data management software.” I’ve had an interest in the growth of unstructured data management for several years knowing that a rapid growth in data from manufacturing was coming (and now is here).
I have two news items from the company. Just released today was news about a new training portal. Some news about an application of its technology came my way a week or so ago. That is below.
Training Portal for DatadobiDriven program
Customers Can Now Eliminate Pain and Risk Associated with Complex Data Migrations
Datadobi announced the launch of a new DatadobiDriven Training Portal, intended to provide strategic partners and end customers with tailored technical certification, support, and on-going communication. The new portal is an enhancement to the DatadobiDriven Program, which is focused on adding value for Sales Engineers, Professionals, and Administrators that helps to drive business success.
Over a decade ago, Datadobi raised the bar for data migration solutions with the launch of DobiMigrate, enterprise-class migration software for NAS (network-attached storage) and object data. With DobiMigrate, channel solutions providers and end customers now have a solution that is proven and can be trusted in the most complex and demanding environments to deliver fast, efficient, secure, accurate, and verifiable migration to new storage and/or the cloud.
“With the launch of the DatadobiDriven Training Portal, we continue to set new industry standards. It is now faster and easier than ever for channel partners to be trained and prepared to sell, deploy, and support Datadobi solutions. As a result, our partners are able to increase customer satisfaction, enjoy optimum revenue, and accelerate time to profitability,” said Michael Jack, Chief Revenue Officer and Co-Founder, Datadobi. “Likewise, for end users the portal facilitates direct access to information, training, and solutions for eliminating the pain and risk associated with seemingly straightforward, but more often than not, complex data migrations.”
In related news today, Datadobi announced it has partnered with CLIMB Channel Solutions to provide DatadobiDriven Program benefits to its Climbing Club members. “The Climbing Club is an exclusive group of valued reseller partners that we reward for their efforts in working with Climb and its partners,” said Charles Bass, Vice President of Alliances and Marketing, Climb.
Sports Gear and Equipment Company Teams with Datadobi
DobiMigrate Meets Complex and Demanding Requirements — Compliantly Migrating Heterogeneous Archive Data
Datadobi announced Decathlon, a global leader in sports gear and equipment, has deployed its DobiMigrate software to help enable the move of Decathlon’s entire IT operations into the cloud.
To support the company’s tremendous growth and success, Decathlon made the decision to completely leave its onsite datacenters and migrate to a number of the main cloud providers (Azure, AWS, GCP, Alibaba Cloud, Yandex, and others). One of the final steps would be one of its most critical on its trek towards a successful digital transformation — the migration of all its on-premises unstructured archive data, ranging from product and inventory to customer data.
“We knew a migration of this magnitude could be very complicated, particularly in relation to moving the archive data,” said Tony Devert, IS Engineer, Decathlon. “We considered using CSP’s data movement capabilities but knew this wasn’t their core competency and that their tools were not really qualified to conserve our legal timestamp.” He explained, “We had over six years of archive data to move to the cloud. Every application has its own particularity and would have needed to do its own migration. In other words, the project would have been chopped up into little pieces individually by application. In addition, we had the added complication that the archives included legal data that had associated required retention periods. So, we needed to have extra checks and safety measures in place that would provide proof of the correct migration of the content.”
After careful research and a successful POC, Decathlon chose to deploy DobiMigrate as it found it to be the ideal solution for meeting its complex and demanding requirements. This included maintaining data integrity in addition to providing chain of custody via its hashing of every single file as it is migrated. With DobiMigrate, a file would only be declared successfully migrated if the source and target were an identical match. A report could then be created to show every single hash of every single file, which could be kept for future auditing.
“With DobiMigrate, we were able to dramatically accelerate our migration and complete it well under our timeline objective,” said Devert. “And, with its chain of custody capabilities, we don’t have to check and double check that our data was moved to the destination. With DobiMigrate, if we are audited in two years, five years, or 10 years we can be confident our data is there, and it is correct.”
He continued. “Now that we are in our new cloud environment we can benefit from the speed, agility, and elasticity of on-demand solutions that we can intelligently adapt to our business requirements, thereby positively impacting our bottom-line.”
With its IT infrastructure now deployed 100% across public clouds, Decathlon’s training, recruitment, and partnership with technology visionaries and experts has become a key strategic priority. Its goal is to continue to leverage the newest and most innovative technologies in areas such as serverless applications, automation, and continuous integration and delivery (CI/CD) such as Terraform, Git, Kubernetes, and the like.
Containers have become a must have technology for those pursuing some form of Digital Transformation, or whatever you wish to label it. I’ve written little about the subject. Following is a news release concerning a way for cloud-native Microsoft SQL Server.
DH2i, a provider of multi-platform Software Defined Perimeter (SDP) and Smart Availability software, announced June 22 the general availability (GA) of DxEnterprise (DxE) for Containers, enabling cloud-native Microsoft SQL Server container Availability Groups (AG) outside and inside Kubernetes (K8).
Container use is skyrocketing for digital transformation projects—particularly the use of stateful containers for databases such as Microsoft SQL Server. This growing stateful database container use is also generating a hard production deployment requirement for database-level high availability (HA) in Kubernetes.
For medium and large organizations running SQL Server, database-level HA has traditionally been provided by SQL Server Availability Groups (AGs). However, SQL Server AGs have not been supported in Kubernetes until now—hindering organizations’ ability to undergo digital transformations. DxEnterprise (DxE) for Containers is the answer to the problem.
DxEnterprise for Containers accelerates an enterprise’s digital transformation (DX) by speeding the adoption of highly available stateful containers. DxEnterprise (DxE) for Containers provides SQL Server Availability Group (AG) support for SQL Server containers, including for Kubernetes clusters. It enables customers to deploy stateful containers to create new and innovative applications while also improving operations with near-zero RTO to more efficiently deliver better products and services at a lower cost. Additionally, it helps organizations generate new revenue streams by enabling them to build distributed Kubernetes AG clusters across availability zones/regions, resulting in hybrid cloud and multi-cloud environments which can rapidly adapt to changes in market conditions and consumer preferences.
“Kubernetes lacks SQL Server AG support, which is essential for using stateful containers in production,” said Shamus McGillicuddy, Vice President of Research, EMA Network Management Practice. “DxEnterprise for Containers solves this problem. It enables AG support in Kubernetes.”
“DxE for Containers is the perfect complement to Kubernetes’ pod/node-level cluster HA,” said Don Boxley, DH2i CEO and Co-Founder. “DxE for Containers enables Microsoft users to confidently deploy highly available SQL Server containers in production, speeding their organizations’ digital transformation.”
DxEnterprise for Containers Features & Benefits:
– Kubernetes SQL Server Container Availability Groups with automatic failover, an industry first – Enables customers to deploy stateful containers to create new and innovative applications
– Near-zero recovery time objective (RTO) container database-level failover – Improves operations to more efficiently and resiliently deliver better products and services at a lower cost to the business
– Distributed Kubernetes AG clusters across availability zones/regions, hybrid cloud and multi-cloud environment with built-in secure multi-subnet express micro-tunnel technology – Enables customers to rapidly adapt to changes in market conditions and consumer preferences
– Intelligent Health & performance QoS monitoring, alerting management – Simplifies system management
– Mix and match support for Windows and Linux; bare metal, virtual, cloud servers – Maximizes IT budget ROI
Organizations can now purchase DxEnterprise (DxE) for Containers directly from the DH2i website to get immediate full access to the software and support. Customers have the flexibility to select the support level and subscription duration to best meet the needs of their organization. Users can also subscribe to the Developer Edition of DxEnterprise (DxE) for Containers to dive into the technology for free for non-production use.
DH2i Company is the leading provider of multi-platform Software Defined Perimeter (SDP) and Smart Availability software for Windows and Linux. DH2i software products DxOdyssey and DxEnterprise enable customers to create an entire IT infrastructure that is “always-secure and always-on.”
Employers finding ways to encourage employees to boost skills and grow is not a new phenomenon. However, this will only grow further for a while as the employee supply chain restocks and reorients following the pandemic. This is an example.
In order to meet new skills demands and develop the workforce of the future for the automotive industry, InStridetoday announced the launch of a pilot program with leading automotive technology company Magna International to develop strategic education programs for qualified employees to access undergraduate degrees and other learning and development opportunities. The company’s initial education offerings will be available for US-based employees beginning this month.
“We are incredibly excited to work with a global mobility powerhouse such as Magna and are looking forward to serving their employees through our academic partnerships,” said Vivek Sharma, CEO of InStride. “The executive team at Magna have been thoughtful in their strategic design of this pilot education program to ensure that Magna employee-learners have the opportunity to access life-changing credentials and skills.”
With this education initiative, internally known as EPIC (Educational Pathways for Innovative Careers), Magna looks to build on its culture of creating continuous, scalable, lifelong learning opportunities for its employees. One way they expect to achieve this objective is by establishing pathways to learning and educational opportunities using InStride’s strategic enterprise education programs. Magna will draw on relevant education providers from InStride’s leading academic network to address skills needs within their organization and support the career objectives of qualified employees.
“The mobility industry is transforming rapidly and in need of ever-changing skill sets to meet new demands. As vehicles change, the way we design and build them will be drastically different, requiring employees to expand their knowledge to maintain our company’s competitive advantage,” said Aaron McCarthy, Magna Chief Human Resources Officer.“With the help of this pilot program, we hope to continue moving the company forward for and with our employees as part of our learning culture.”
Machine Learning (ML) is a flavor of Artificial Intelligence (AI). This news release from Seeq illuminates a bit of the mystery surrounding much discussion of the topic.
Seeq Corporation released R52 with new features to support the use of machine learning innovation in process manufacturing organizations. These features enable organizations to deploy their own or third-party machine learning algorithms into the advanced analytics applications used by front line process engineers and subject matter experts, thus scaling the efforts of a single data scientist to many front-line OT employees.
New Seeq capabilities include Add-on Tools, Display Panes, and User-defined Functions, each of which extend Seeq’s predictive, diagnostic, and descriptive analytics. The result is faster development and deployment of easy-to-use algorithms and visualizations for process engineers. With R52, end users will also be able to schedule Seeq Data Lab notebooks to run in the background, fulfilling a top customer request.
Seeq customers include companies in the oil and gas, pharmaceutical, chemical, energy, mining, food and beverage, and other process industries. Investors in Seeq—which has raised over $100M to date—include Insight Ventures, Saudi Aramco Energy Ventures, Altira Group, Chevron Technology Ventures, Cisco Investments, and Next47, the venture group for Siemens.
As a compliment to the new extensibility features, Seeq data scientists are working with customers to develop and deploy machine learning algorithms tailored to the industrial process domain. Current areas of focus include automatically detecting performance changes in monitored assets, identifying causal relationships among process variables, and improved diagnostics by identifying and labeling patterns within a data set. For example, a super-major oil & gas company is using Seeq extensibility features to enable easy access by process engineers to a neural-network algorithm created by their data science team, helping reduce greenhouse gas emissions.
“Analytics software for manufacturing organizations is an area overdue for innovation,” says Steve Sliwa, CEO and Co-Founder of Seeq. “Spreadsheets replaced pen and paper 30 years ago for analytics and haven’t changed much since. By leveraging big data, machine learning and computer science innovations, Seeq is enabling a new generation of software-led insights.”
Seeq first shipped easy to use machine learning-enabled features in 2017 in Seeq Workbench, and then in 2020 introduced Seeq Data Lab for Python scripting and access to any machine learning algorithm. This support for multiple audiences—with no code/low code features for process engineers and a scripting environment for data scientists engaged in feature engineering and data reduction efforts—democratized access to machine learning innovation.
Seeq’s approach to integrating machine learning features in its applications addresses many of the reasons data science initiative fail in manufacturing organizations.
- Seeq connects to all underlying data sources—historian, contextual, manufacturing applications, or other data sources—for data cleansing and modeling.
- Seeq supports the connected, two-way, interaction of plant data and process engineering expertise in OT departments with the data science and algorithm expertise in IT departments.
- Seeq provides a complete solution for algorithm development, updating and improving algorithms over time, employee collaboration and knowledge capture, and publishing insights for faster decision making.
In addition to Seeq Data Lab support for machine learning code and libraries, Seeq also enables access to the Seeq/Python library by third-party machine learning solutions including Microsoft Azure Machine Learning, Amazon SageMaker, and open source offerings such as Apache Anaconda. For example, a manufacturer using Amazon SageMaker is evaluating their machine learning insights with Seeq to create work orders in their SAP system.