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Adding EfficientNet-Lite4 and SSD-MobileNetV1 to the ONNX Model Zoo

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Guest Author: Shirley Su, University of Southern California (USC) freshman majoring in Computer Science. Her passion is to utilize technology for social good: whether it is creating usable applications or advancing research, she wants to be a positive contributor for the community.

The Coronavirus pandemic was a major impediment for job opportunities as it prompted several internships to be cancelled this year. While disappointed, I still wanted to have a productive summer and further my experience in the computer science field. I decided to reach out to my former Microsoft AI Platform intern co-workers from last summer. We were eager to contact our former mentor and continue our projects on ONNX – this time, serving as open-source contributors.

As an exploration of the recent advancements in computer vision, I was eager to research new machine learning models and contribute them to the ONNX Model Zoo. The ONNX Model Zoo provides many state-of-the-art pre-trained models that come with instructions for integrating into applications. I investigated the tensorflow-onnx GitHub repo, which detailed the conversion of both the EfficientNet-Lite4, an image classification model, and the SSD-MobileNetV1, an object detection model. These are popular computer vision models and I wanted to add them to the ONNX Model Zoo so others could more readily use them.

I began the conversion process by initially copying and then running the Jupyter Notebook Script from the GitHub repo. This process included setting up environmental variables and downloading the pre-trained model. After saving the model, I ran the script to convert the model from TensorFlow into the ONNX format. I then ran inference on the saved model using ONNX Runtime to view and validate results. I also uploaded the models to Netronan open source viewer that allows users to visualize the neural networks inside ONNX models. It provides information on the model’s operator set, the ONNX version, and input and output tensor shapes and types. I included that information in the template for the new model’s README as part of the instructions on how to pass in input data and how to interpret the output.

Comments from the ONNX community on GitHub were especially helpful in pointing out mistakes and helping me resolve the issues in the model folders. In both my EfficientNet-Lite4 and SSD-MobileNetV1 model, my file with the sample inputs and outputs were incorrect. To revise and fix the code, I converted the NumPy array to a TensorProto and saved the TensorProto as a serialized protobuf file.  Moreover, there was an error in the postprocessing code for the SSD-MobileNetV1, which incorrectly outputted the array of object detection predictions. I realized that while the model produced detection classes from the inference, the most accurate class label was not being outputted. To fix this issue, I changed how the results were looped over to include the most accurate class label.

My pull requests were merged and now everyone can use the EfficientNet-Lite4 and SSD-MobileNetV1 models I contributed to the ONNX Model Zoo.

Trials and Tribulations

Another model that interested me was the Visual Question Answering (VQA) model, in which users input an image and a question about the image and the model outputs an answer. I used a VQA GitHub that had the necessary files and an open-source license.

However, I ran into several issues during the process. The most time-consuming and tedious task was downloading 64 GB worth of MS COCO data onto my computerall without a fast internet connection or a powerful machine. This process took several hours and my computer crashed. Realizing that this attempt was futile, I began to look into Microsoft’s Azure Virtual Machines, which had the necessary memory and space needed. Using the virtual machine expedited the task and shorted the download time from approximately 10 hours to just 1 hour.

After I had successfully downloaded and preprocessed the data, the next obstacle was exporting the model to ONNX. When I passed in the standard model arguments, I was getting issues with PyTorch’s ONNX export call. Since the model was written with a much older version of PyTorch, I suspected the model needed updates to make it compatible with TorchScript and ONNX export.

I hope others in the open-source community will continue working on making this model accessible and contribute it to the model zoo.


Throughout this project, I gained more expertise working with Git and became more comfortable making pull requests and becoming an open-source contributor. I was able to contribute 2 important computer vision models to the ONNX model zoo. And while I was unable to contribute the VQA model, that project allowed me to gain hands-on experience with working with Azure virtual machinesa tool that is crucial when handling large amounts of data. I also became more comfortable reading others’ Python code and learning how to efficiently debug errors.

This summer, our team was especially grateful for the help we received from our mentors Vinitra Swamy and Natalie Kershaw. They have given us opportunities that allow us to make meaningful contributions to the ONNX Model Zoo. From dedicating time for weekly meetings with us to helping us debug strenuous errors, their guidance has been immensely helpful in our technical and professional development.

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LF AI & Data Foundation Announces New Project Lifecycle Document

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Today, LF AI & Data Foundation is releasing an updated version of its project lifecycle document that defines the project stages, requirements for a project to be accepted in each stage, transitioning between stages, and the benefits associated with each stage. Since the document’s last revision over two years ago, we have gained experience in onboarding over a dozen new projects and have received feedback that has allowed us to move forward with these improvements. 

Revisions to the new document include:

  • Introducing Sandbox stage. This new stage is specific to projects that intend to join LF AI & Data in the Incubation stage in the future. The Sandbox stage provides time to lay the foundations for Incubation and is designated for very new projects.  For example, new projects that are designed to extend one or more LF AI & Data projects with functionality or interoperability libraries, or independent projects that fit the LF AI & Data mission and provide the potential for a novel approach to existing functional areas.
  • Improving the requirements to incubate projects. These improvements more specifically require that submitted projects for the Incubation stage have at least two organizations actively contributing to the project, have a defined Technical Steering Committee (TSC), have a sponsor who is an existing LF AI & Data member, have earned at least 300 stars on GitHub, and have achieved and maintained a Core Infrastructure Initiative Best Practices Silver Badge.
  • Improving the requirements to graduate projects. These improvements more specifically require projects to have a healthy number of code contributions coming from at least five organizations, have reached a minimum of 1,000 stars on GitHub, and have achieved and maintained a Core Infrastructure Initiative Best Practices Gold Badge.
  • Adding specific language to clarify the benefits for projects hosted in every stage
  • Elaborating on the Archive Stage projects to eliminate ambiguities 
  • Adding information on the Annual Review of projects. This annual review will include an assessment as to whether projects in Sandbox and Incubation are making adequate progress towards the Graduation stage; and that projects in the Graduation stage are maintaining positive growth and adoption.
  • General edits for the purpose of clarity

Dr. Ibrahim Haddad, Executive Director of LF AI & Data, said: “It’s been great to witness and experience the growth in LF AI & Data’s hosted projects as we’ve added 15 new projects in 2020. With this intensive experience came a lot of learned lessons. The LF AI & Data community took these learnings and used them to update our Project Lifecycle Document introducing a new project stage – Sandbox – and raising the bar for the admission into other stages. I am looking forward to welcoming new projects in our updated stages and continuing the growth of our community.”

Dr. Jim Spohrer, Chair of the Technical Advisory Council in LF AI & Data said: “I am very excited about the sandbox stage that will help us engage with and provide visibility for early-stage open source AI and Data community projects. Having such a stage supported in LF AI & Data is a true best practice for steadily growing foundations.” 

New projects joining the LF AI & Data Foundation will be required to follow the process and requirements outlined in the updated project lifecycle document.

If you are interested in hosting your open source AI or Data  project with the LF AI & Data Foundation, please review the project lifecycle document and email us via We’re eager to help and discuss with you such possibilities.

For further reading, please visit these pages:

Artificial Intelligence (AI) Enables a Personalized Customer Experience at Scale

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Guest Authors: Utpal Mangla: VP & Senior Partner; Global Leader: IBM’s Telco Media Entertainment Industry Center of Competency at IBM ( AND Luca Marchi: Artificial Intelligence Lead – Center of Competence – Telco, Media andEntertainment at IBM (

As a client, how many times did you say: “This company really gets me?”. And when that happens, how did it influence your loyalty and the likelihood to do more business with them? Probably a LOT!!!

That’s exactly why customer experience is a KEY competitive advantage for companies in every sector and specifically for those in the telecommunication, media and entertainment industry. Providing an excellent and frictionless customer experience is the best way to reduce churn, improve NPS and increase ARPU through up-sell activities

On the other end, telecommunication, media & entertainment industries have been challenged by digitally native ‘Over The Top’ (OTT) entrants that design their organizations around the concept of superior customer experience. Matching their level of client focus and exceeding expectations is the only way to resist their attacks.

Using AI to understand your customers

AI has proven to be a key enabler in better understanding customers. AI enables companies to better relate with their client in 3 impactful and powerful ways:

  • 1) Derive insights from Big Data: In order to understand your customers, you need data about them. Luckily, we live in the era of big data, where user-generated data, internal and external data, public and private data abound. AI has the ability to ingest, understand and provide actionable insights based on those data. Also, AI models can be optimized and tuned automatically, improving after each interaction.
  • 2) Leverage Context: When making decisions, AI takes into account the context. The user experience is not only based on who the customer is but also on what the customer is going through at this very moment.
  • 3) Scale a personalized interaction: Ideal way to provide a customized experience would be to assign an attendant to each customer, like in a high-end restaurant. That is clearly not feasible, but AI comes to help. Through natural language understanding technology, companies can scale high touch customer support to provide the same level of expertise and engagement to all their clients

Customer experience starts with the Network

When it comes to telco, there is no personalized customer experience that can make up for network failures. We expect services to be up ALL the time. If not, we are ready to switch provider. Expectation of network availability has become even more pertinent in the current COVID environment. Implicit expectation is that networks are up and running 24/7

Telecommunication companies are acutely aware of these expectations. That’s why they are infusing artificial intelligence in their network operations

In fact, machine learning models can leverage network data to proactively get ahead of anomalous network activity and degradations; thereby increasing speed and accuracy of the detection, prevention, and repair of network issues. Artificial intelligence supports network engineers in root cause analysis, repair recommendation and covering the entire problem to resolution process.

AI can also be used to link network and services. Predictive insights proactively monitor and manage end-to-end service quality thus, enabling operators to prioritize actions based on impact to services and customer experience. 

Shaping Digital Re-invention and Personalized Engagement

Making the network available 24/7 is a great first step to improving personal experience, but it is not sufficient. Clients engagement with telco and media companies comprises different touchpoints, most of them digital, and a great experience has to be delivered at every single touchpoint. Hence, digital processes need to be re-designed around the customer experience like many new entrants have done.

Artificial intelligence is a key contributor to digital customer engagement.

In fact, AI enables business platforms to gather and leverage all relevant data emerging from customer interactions, providing a unique and complete view of the customer. This information is then used to support a personalized experience, that means a personal touch in the interaction, the anticipation of the customer needs, the resolutions of problems that have yet to be communicated and the offer of personalized packages.

This goes beyond the simple natural language interaction we discussed before. Most of the time, using virtual agents to provide customer support is the first step in the direction of a comprehensive redesign of the customer experience that needs to be proactive and not reactive and leverage all available information about the customer with the support of data platforms.

Recommended by a friend

Do you know why our friends are very good at recommending us the best TV show to follow or the next restaurant we should pay a visit to? Because they know us very well. Media companies need to achieve that same level of customer knowledge, intimacy & trust to keeping them engaged. More engagement means more screen time, more screen time leads to greater subscription and/or advertising revenues.

Artificial intelligence can turn media companies into a “friend” that recommends the best content. Media platforms already have access to a lot of user data (user profile, content history, payment ability) and they can integrate it with other sources like social media. Artificial intelligence leverages this data to recommend the best content and keep the viewer engaged and satisfied.  

On the other side of the ecosystem, advertisers need to maximize the return on their advertising investment. Media companies can now offer them the ability to show targeted advertising to on digital platforms, for example streaming services, matching the ads with the most receptive viewers, based on the information they gathered about them.

Personalization at scale

A personalized customer experience has a strong value and requires a high touch relationship, making it difficult to scale. Artificial intelligence applied to digital customer engagement has the ability to understand customers leveraging a large amount of data, engage with every customer in a consistent and personalized way and improve the organizations decision-making process over time. As a consequence, customers are pleased, satisfied and more loyal. 

LF AI & Data Resources

LF AI & Data Announces Principles for Trusted AI

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Guest Author: Alka Roy, Founder, The Responsible Innovation Project

On behalf of the LF AI & Data’s Trusted AI Committee Principles Working Group, I am pleased to announce LF AI & Data’s Principles for Trusted AI. LF AI & Data is an umbrella foundation of the Linux Foundation that supports open source innovation in artificial intelligence, machine learning, deep learning, and data. 

With these principles, the LF AI & Data Foundation is not only joining other open source and AI communities in adopting a set of ethical, responsible, and trust-based principles, it is also inviting the larger Linux Foundation community—19K+ companies and 235K+ developers to lead by trust and responsibility.  According to its website, the “Linux Foundation enables global innovation by growing open technology ecosystems that transform industries: 100% of supercomputers use Linux, ~95% public cloud providers use Kubernetes, 70% global mobile subscribers run on networks built using ONAP, 50% of the Fortune Top enterprise blockchain deployments use Hyperledger.” 

With such immense impact and scale, the responsibility to approach innovation with trust is immense. LF AI & Data’s AI principles are guided by a vision to expand access and invite innovation at all levels of engagement. The language of the principles has been kept simple and easy to understand, yet flexible, to help ensure flexibility and wider adoption. Not an easy task.

The Process

These principles were derived after over a year of deliberation which included parsing through the various industry, non-profit, and partner company’s AI principles, guidelines, contributions, and principles, while always keeping the community and social impact front and center. In addition to member companies’ and non-profit groups’ input, guidelines from OECD, EU, SoA, ACM, IEEE, DoD were also referenced. The key criteria balanced competing interests across the industry and companies with the need for open and innovative technology built with trust and accountability.

LF & AI Data Foundation AI Principles: (R)REPEATS

The (R)REPEATS acronym captures the principles of Reproducibility, Robustness, Equitability, Privacy, Explainability, Accountability, Transparency, and Security. The image below illustrates that a cohesive approach to implementation is needed. The order in which the principles are listed is not meant to denote hierarchy. Neither is this a list to pick and choose what is convenient. Rather, as so many discussions and efforts to implement AI principles in the industry and committee members in their ecosystem have illustrated, all these principles are interconnected and interdependent, and important.


Artificial Intelligence (AI) in the following definitions refer to and imply any flavor and use of Artificial Intelligence or a derivative of Artificial Intelligence–including but not limited to software or hardware, simple or complex systems that include machine learning, deep learning, data integrated with other adjacent technologies like computer vision whether created by people or another AI. 

  1. Reproducibility is the ability of an independent team to replicate in an equivalent AI environment, domain or area, the same experiences or results using the same AI methods, data, software, codes, algorithms, models, and documentation, to reach the same conclusions as the original research or activity. Adhering to this principle will ensure the reliability of the results or experiences produced by any AI.
  2. Robustness refers to the stability, resilience, and performance of the systems and machines dealing with changing ecosystems. AI must function robustly throughout its life cycle and potential risks should be continually assessed and managed.
  3. Equitability for AI and the people behind AI should take deliberate steps – in the AI life-cycle –  to avoid intended or unintended bias and unfairness that would inadvertently cause harm.
  4. Privacy requires AI systems to guarantee privacy and data protection throughout a system’s entire lifecycle. The lifecycle activities include the information initially collected from users, as well as information generated about users throughout their interaction with the system e.g., outputs that are AI-generated for specific users or how users responded to recommendations. Any AI must ensure that data collected or inferred about individuals will not be used to unlawfully or unfairly discriminate against them. Privacy and transparency are especially needed when dealing with digital records that allow inferences such as identity, preferences, and future behavior.  
  5. Explainability is the ability to describe how AI works, i.e., makes decisions. Explanations should be produced regarding both the procedures followed by the AI (i.e., its inputs, methods, models, and outputs) and the specific decisions that are made. These explanations should be accessible to people with varying degrees of expertise and capabilities including the public.  For the explainability principle to take effect, the AI engineering discipline should be sufficiently advanced such that technical experts possess an appropriate understanding of the technology, development processes, and operational methods of its AI systems, including the ability to explain the sources and triggers for decisions through transparent, traceable processes and auditable methodologies, data sources, and design procedure and documentation.
  6.  Accountability requires AI and people behind the AI to explain, justify, and take responsibility for any decision and action made by the AI.  Mechanisms, such as governance and tools, are necessary to achieve accountability.
  7. Transparency entails the disclosure around AI systems to ensure that people understand AI-based outcomes, especially in high-risk AI domains. When relevant and not immediately obvious, users should be clearly informed when and how they are interacting with an AI and not a human being. For transparency, ensuring that clear information is provided about the AI’s capabilities and limitations, in particular the purpose for which the systems are intended, is necessary. Information about training and testing data sets where feasible, the conditions under which AI can be expected to function as intended and the expected level of accuracy in achieving the specified purpose, should also be supplied. And finally,
  8. Security and safety of AI should be tested and assured across the entire life cycle within an explicit and well-defined domain of use. In addition, any AI should be designed to also safeguard the people who are impacted. 

In addition to the definitions of the principles shared here, further descriptions and background can be accessed at LF AI & Data wiki

The Team

The list of principles was reviewed and proposed by a committee and approved by the Technical Advisory Council of LF AI & Data. The Trusted AI Principles Working Group was chaired by Souad Ouali (Orange) and included Jeff Cao (Tencent), Francois Jezequel (Orange), Sarah Luger (Orange Silicon Valley), Susan Malaika (IBM), Alka Roy (The Responsible Innovation Project/ Former AT&T), Alejandro Saucedo (The Institute for Ethical AI / Seldon), and Marta Ziosi (AI for People). 

What’s Next?

This blog announcement serves as an open call for AI Projects to examine and adopt the Principles – at various stages in their life-cycle. We invite the LF AI & Data community to engage with the principles and examine how to apply them to their projects and share the results and challenges within the wider community of the Linux Foundation as well as LF AI & Data. 

Open source communities have the tradition to take standards, code or ideas, put them into practice, share results, and evolve them. We also invite volunteers to help assess the relationship of the Principles with existing and emerging trusted AI toolkits and software to help identify any gaps and solutions. The LF AI & Data Trusted AI Committee is holding a webinar hosted by the Principles Working Group members to present the principles, solicit feedback and continue to explore other options to engage the larger community. 

Call to Action: 

  1. Join the Principles Committee and help develop strategies for applying the principles.
  2. Apply principles to your projects and share feedback with the Trusted AI community.
  3. Register for the upcoming Webinar or submit your questions to the committee.

It’s only through collective commitment and habit of putting these principles into practice that we can get closer to building AI that is trustworthy and serves a larger community.

LF AI & Data Resources


LF AI & Data Day ONNX Community Virtual Meetup – March 2021

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The LF AI & Data Foundation is pleased to sponsor the upcoming LF AI & Data Day* – ONNX Community Virtual Meetup – March 2021, to be held via Zoom on March 24, 2021.

ONNX, an LF AI & Data Foundation Graduated Project, is an open format to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools and choose the combination that is best for them. 

The virtual meetup will cover ONNX Community updates, partner/end-user stories, and SIG/WG updates. Thanks to Baidu and Ti Zhou who volunteered to be the host for the workshop. If you are using ONNX in your services and applications, building software or hardware that supports ONNX, or contributing to ONNX, you should attend! This is a great opportunity to connect with and hear from people working with ONNX across many companies. 

Registration is now open and the event is free to attend. Capacity will be 500 attendees. Please see the event schedule below and also visit the event website for up to date information on this virtual meetup.

8:00 AM China (Thur 3/25)  
5:00 PM PT/USA (Wed 3/24)  
Event Kickoff – Agenda Review
Host: Ti Zhou (Baidu)

ONNX Progress Update
Speakers: ONNX Steering Committee
Prasanth, Harry, Jim, Joohoon, Sheng
8:25 AM China (Thur 3/25)  
5:25 PM PT/USA (Wed 3/24)
Community Presentations – Agenda Review (10 minute short talks)
Host: Ti Zhou (Baidu)

popONNX: Support ONNX on IPU
Speaker: Han Zhao (GraphCore-UK)

Spring Project:Multi Backend Neural Network Auto Quantization and Deploy over ONNX
Speaker: Yu Feng Wei (SenseTime-HongKong)

ONNX Runtime for Mobile Scenarios: From model to on-device inferencing
Speaker: Tom Wildenhain (Microsoft-USA) and Scott McKay (Microsoft-Australia)

Introduction to DL Framework PaddlePaddle and Paddle2ONNX Module
Speaker: Wranky Wang (Baidu-China)

ONNX on microcontrollers
Speaker: Rohit Sharma (AITechSystems-USA_CA)

Monitoring and Explaining ONNX Models in Production
Speaker: Krishna Gade (FiddlerAI-USA_CA)

ONNX client for Acumos
Speaker: Philippe Dooze (Orange-France)

Deploy ONNX model seamlessly across the cloud, edge, and mobile devices using MindSpore
Speaker: Leon Wang (Huawei-China)

ONNX Runtime Training
Speaker: Peng Wang (Microsoft_China)

Quantization support for ONNX using LPOT (Low precision optimization tool)
Speakers: Haihao Shen (Intel – China) and Saurabh Tangri (Intel)
Contact: Rajeev Nalawadi (Intel-China)
10:15 AM China (Thur 3/25)
7:15 PM PT/USA (Wed 3/24)
SIGs and WGs Updates – Agenda Review (10 minute talks)
Speaker: Ti Zhou (Baidu)

Architecture/Infrastructure SIG Update
Chair: Ashwini Khade (Microsoft)

Operators SIG Update
Co-Chairs: Michał Karzyński (Intel) and Ganesan Ramalingen (Microsoft)

Converters SIG Update
Co-Chairs: Guenther Schmuelling (Microsoft), Kevin Chen (Nvidia), Chin Huang (IBM)

Model Zoo/Tutorials SIG Update
Co-Chair: Wenbing Li (Microsoft) and Vinitra Swamy (Microsoft)

Q&A and Discussion

Want to get involved with ONNX? Be sure to join the ONNX-Announce mailing list to join the community and stay connected on the latest updates. You can join technical discussions on GitHub and more conversations with the community on LF AI & Data Slack’s ONNX channels.

Note: In order to ensure the safety of our event participants and staff due to the Novel Coronavirus situation (COVID-19) the ONNX Steering Committee decided to make this a virtual-only event via Zoom.

*LF AI & Data Day is a regional, one-day event hosted and organized by local members with support from LF AI & Data and its Projects. Learn more about the LF AI & Data Foundation here.

ONNX Key Links






LF人工智能与数据基金会很高兴欢迎您参加LF人工智能与数据日*-2021年3月ONNX社区虚拟⻅面会. 本次活动将于中国时间3月25日通过Zoom视频会议在线举办. 本次活动主题为LF人工智能与数据基金会的已毕业项目ONNX.

本次活动将涵盖ONNX社区更新、合作伙伴/终端用户故事以及SIG/WG更新. 感谢百度和周倜主动担任本次研讨会的主持人.

如果您在您的服务和应用中使用了ONNX,正在构建支持ONNX的软件或硬件,或者正在为ONNX做贡献,请务必参加!这是一个与众多使用 ONNX 技术的公司人员会面和交流的好机会.

注:由于新型冠状病毒(COVID-19)的情况,为了确保我们的活动参与者和工作人员的安全, ONNX指导委员会决定在Zoom线上举办此次活动.

*LF 人工智能与数据日(LF AI & Data Day)是由当地成员主办和组织的为期一天的地区性活动,得到 LF 人工智能 与数据基金会及其项目的支持. 在这里可以了解更多关于LF AI与数据基金会的信息.

LF AI & Data Resources

LF AI & Data Foundation Announces Graduation of Pyro Project

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The LF AI & Data Foundation, the organization building an ecosystem to sustain open source innovation in artificial intelligence (AI), machine learning (ML), deep learning (DL), and data open source projects, is announcing today that hosted project Pyro is advancing from an Incubation level project to a Graduate level. This graduation is the result of Pyro demonstrating thriving adoption, an ongoing flow of contributions from multiple organizations, and both documented and structured open governance processes. Pyro has also achieved a Core Infrastructure Initiative Best Practices Badge, and demonstrated a strong commitment to community.

As an Incubation Project, Pyro utilized the LF AI & Data Foundation’s various enablement services to foster its growth and adoption; including program management support, event coordination, legal services, and marketing services ranging from website creation to project promotion. 

Pyro is a universal probabilistic programming language (PPL) written in Python and supported by either PyTorch or JAX on the backend. Pyro enables flexible and expressive deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling.

It was open sourced by Uber, the project founder, and joined LF AI & Data as an Incubation Project in January 2019. 

“The journey of Pyro from Incubation to Graduation has been very impressive,” said Dr. Ibrahim Haddad, Executive Director of the LF AI & Data Foundation. “The development activities, the growth of its community, and its adoption is particularly noteworthy.  Pyro has exceeded our graduation criteria and we’re proud to be its host Foundation and to support it across a number of services. As a Graduate project, our support to the Pyro project and its community will continue and we’re excited to have the project represented as a voting member on our Technical Advisory Council. Congratulations, Pyro!”

“We on the Pyro team have been happily surprised at the wide adoption of Pyro in both industry and the sciences. Since branching out to provide NumPyro (a JAX-based implementation of Pyro), we’ve seen a growth in the diversity of contributors, from applied scientists and statistics practitioners to machine learning researchers. A big part of Pyro’s growth is due to user trust in our being part of a neutral foundation rather than a single company.” said Pyro project lead, Fritz Obermeyer. 

2020 in Numbers

The stats below capture Pyro’s development efforts from January 1, 2020 to December 14th, 2020:

  • 6.6k Github Stars
  • 797 Github forks 
  • 324 Github dependents 
  • >100 contributors  
  • >1000 forum topics 

Curious about how to get involved with Pyro? 

Check out their Getting Started Guide. And be sure to join the Pyro Announce and Pyro Technical-Discuss mailing lists to join the community and stay connected on the latest updates. 

Congratulations to the Pyro team! We look forward to continued growth and success as part of the LF AI & Data Foundation. To learn about hosting an open source project with us, visit the LF AI & Data Foundation website.

Pyro Key Links

LF AI & Data Resources

People Fall Detection via Privacy Preserving AI

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For many adults, one of the most difficult decisions to make is how to care for an elderly parent or relative that needs assistance. Every 11 seconds, an older adult is treated in the emergency room for a fall; every 19 minutes, an older adult dies from a fall. Ambianic is an open-source software solution, which allows the DIY community to take part in solving the global challenge of healthy ageing by offering round the clock surveillance of elderly relatives or patients and instant alert to family caregivers in the event of an accident or fall.

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The impact of Artificial Intelligence on Telecommunication Enterprises: Network and Customer Experience

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Guest Authors: Utpal Mangla: VP & Senior Partner; Global Leader: IBM’s Telco Media Entertainment Industry Center of Competency at IBM ( AND Luca Marchi: Artificial Intelligence Lead – Center of Competence – Telco, Media andEntertainment at IBM (

All telco companies that outperform their peers in terms of revenue growth and profitability have one thing in common. They apply AI throughout their organization following a clear leadership: they have established a new path to value by integrating data into their strategy, operations, and culture.

Data is key but not enough

In the telecommunication world, data availability is exploding: Gartner forecasts that more than 20.4 billions devices will be connected in 2020 generating a constant flow of data that telco can leverage to better understand their customers and increase their business. AI-powered natural language processing enabled the analysis of unstructured data at large scale, supposedly up to 80% of all existing data. Nonetheless, data is not enough. In order to make data usable, Telcos need solve two problems: information architecture (IA) and data privacy. Thus carriers moved data at the core of their strategy and invested in creating new data sources to provide the right information at the right time for the right purpose. Data privacy is a lever for telcos to gain the customer trust and a key competitive advantage they need to achieve.

What can a telco with the right data strategy achieve? Increased customer experience and business expansion.

In fact, data-driven companies leverage AI to better identify unmet customer needs and delivery value at every customer touchpoint. Analytics systems powered by artificial intelligence use structured and unstructured data to identify behavior patterns and customer needs that would be otherwise missed. For examples, telcos can understand when a customer is likely to churn out and provide the best offer to make her stay or they can push a personalized data package based on her data usage. Artificial intelligent scale rich customer interaction across channels. Virtual agents interact via text or voice with customer on an IVR, a mobile app or Whatsapp, providing the same level of customer experience. Cognitive care is usually the starting point of the telcos journey in AI and many market leaders like Vodafone and CenturyLink have achieved enormous success in answering customer queries, personalizing the sales journey, improving customer completionrates and satisfaction and increasing brand score and NPS. In terms of business expansion, the application of artificial intelligence and big data supports the development of new business models and the entry into new businesses and markets. In recent years, a common path followed by telcos internationally has been the extension into the fintech business. Thanks to the large amount of customer data they possess, telcos have a deep knowledge of their clients. When this knowledge is paired with the trust customers have for telcos, carriers are in the right spot to provide personalized financial services.

A great example is Orange Bank, the digital-native bank launched by French telco Orange: it provides unique offerings plus innovative customer relationship model, and it implements a new “phygital” and omnichannel model, integrated with banking, CRM, concierge and advisory services.

Not just technology, but technology and humans together.

Enterprise success is fostered by decision making based on data. To get there, organizations need to collect all data required to make decision and executives and employees need to have a data-oriented mindset to enable quality decision making.

Data-driven telco or cognitive telcos follow a 4 step process that entails (1) transformation of workforce, (2) data collection, (3) data purging: making data clean, current, curated and contextualized to create something profound, (4) implementation of intelligent workflows and humanized experiences that require skills and architecture to use data streaming from IoT, social media, pictures and video. This approach allows cognitive telcos to infuse AI in any process across different divisions: network, human talent, marketing, sales etc.

Transform the way Telcos manage their network

Network is a great example of how telcos are using AI to support process automation and support executive and employees in key decision making.

Some recurring application of artificial intelligence and automation in network operations are:

  • Customer Service Operations: A CSOC provides tools and processes to proactively monitor and manage end-to-end service quality with predictive insights, augmented with AI; thus, enabling operators to prioritize actions based on impact to services and customer experience.
  • Cognitive Network Operations: Generate efficiencies and optimization in Network Operations Center for Level 1 and Level 2 operations engineers & managers. Applies analytics and cognitive to network allowing for simplified and focused operations.
  • Network 360: Get ahead of anomalous network activity and degradations with ML models to detect, prevent, and recommend repair for network performance.

NBN, an Australian wholesale network provider, is at the forefront of AI application for network management: they updated their network management operations with AI, analytics and robotics in order to improve efficiency and sustain an 8x growth.

A key enabler of such success cases is hybrid cloud, allowing telcos to run applications and access data from across multiple disparate platforms.

Become a Cognitive Telco: Data + Strategy + People

In order to become a Cognitive Enterprise and outperform their peers, telcos need to collect and leverage their data, to implement a strategy that bases decision making on data and to create a partnership between humans and technology.

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JanusGraph Joins LF AI & Data as New Incubation Project

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LF AI & Data Foundation—the organization building an ecosystem to sustain open source innovation in artificial intelligence (AI), machine learning (ML), deep learning (DL), and data open source projects, today is announcing JanusGraph as its latest Incubation Project. 

JanusGraph is a distributed, scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. The project was launched in 2017 through a partnership with organizations including  Expero, Google, GRAKN.AI, Hortonworks, IBM and others.

Dr. Ibrahim Haddad, Executive Director of LF AI & Data, said: “We’re really excited with JanusGraph joining LF AI & Data alongside several other AI and Data projects. JanusGraph provides the capability for storing and processing large-scale connected data, which is proving to be very useful for projects in many domains, including IoT, Social Networks, Malware & Fraud detection, Identity and Access Management, etc. These areas can also benefit from intelligent analytics and predictions from AI & machine learning, a key focus of the LF AI & Data Foundation. We look forward to working with the community to grow the project’s footprint and to create new collaboration opportunities with our members and other hosted projects.” 

JanusGraph is an open source, distributed graph database under The Linux Foundation. JanusGraph is available under the Apache License 2.0. The project recently reached the #5 position on the December 2020 global graph database ranking by DB-Engines. JanusGraph was originally forked from the TitanDB graph database, which has been developed since 2012. The first version of JanusGraph (v0.1.0) was released on April 20, 2017.

JanusGraph supports various storage backends, including Apache Cassandra, Apache HBase, Google Cloud Bigtable, Oracle BerkeleyDB, Scylla. Additionally, JanusGraph supports 3rd party storage adapters to be used with other storage backends, such as Aerospike, DynamoDB, and FoundationDB.

In addition to online transactional processing (OLTP), JanusGraph supports global graph analytics (OLAP) with its Apache Spark integration. JanusGraph supports geo, numeric range, and full-text search via external index storages (Elasticsearch, Apache Solr, Apache Lucene). JanusGraph has native integration with the Apache TinkerPop graph stack, including Gremlin graph query language and graph server.

JanusGraph is a valuable graph database because it is developed to be a layer on top of other databases and thus, developers of JanusGraph may focus more on solving challenges related to graph itself. Instead of spending time “reinventing the wheel”, developers may leverage existing stores which focus on low level storage optimizations / performance / consistency / compression while use JanusGraph which focus more on query optimizations, TinkerPop stack up-to date implementations, data storage / index storage integration, etc.

Oleksandr Porunov, member of the JanusGraph Technical Steering Committee, said: “On behalf of the JanusGraph Technical Steering Committee, we are excited to join LF AI & Data Foundation. JanusGraph has a number of applications in wide-ranging domains — including financial services, security, and Internet of Things — which benefit from managing and analyzing large amounts of connected data to derive insights and make intelligent predictions using relationships inherent in their data with the help of machine learning. We look forward to collaborating with other projects under the LF AI & Data umbrella to enable solving complex, large-scale problems with solutions built on scalable storage, analytics, and machine learning.”

LF AI & Data supports projects via a wide range of services, and the first step is joining as an Incubation Project.  LF AI & Data will support the neutral open governance for JanusGraph to help foster the growth of the project. Learn more about JanusGraph on their GitHub and be sure to join the JanusGraph-Announce and JanusGraph-Dev mail lists to join the community and stay connected on the latest updates.A warm welcome to JanusGraph! We look forward to the project’s continued growth and success as part of the LF AI & Data Foundation. To learn about how to host an open source project with us, visit the LF AI & Data website.

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Ludwig Joins LF AI & Data as New Incubation Project

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LF AI & Data Foundation—the organization building an ecosystem to sustain open source innovation in artificial intelligence (AI), machine learning (ML), deep learning (DL), and Data open source projects, today is announcing Ludwig as its latest Incubation Project.

Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. All you need to provide is your data, a list of fields to use as inputs, and a list of fields to use as outputs, Ludwig will do the rest. Simple command line interfaces and programmatic APIs can be used to train models both locally and in a distributed way, and to use them to predict on new data. Ludwig was released and open sourced by Uber

“We are very pleased to welcome Ludwig to LF AI. AI, ML, and DL can be perceived as a difficult technology to use. Ludwig provides the opportunity for less experienced engineers and data scientists to use DL models in their work, providing easy-to-use tools and API’s.” said Dr. Ibrahim Haddad, Executive Director of LF AI & Data. “We look forward to supporting this project and helping it to thrive under a neutral, vendor-free, and open governance.” LF AI & Data supports projects via a wide range of benefits; and the first step is joining as an Incubation Project. 

Dr. Piero Molino, Ludwig’s creator and maintainer, said: “I’m excited about Ludwig joining the Linux Foundation. The open governance will allow for both increased participation from the community and companies already using it as well as opening the door to new collaborations. This is definitely a step towards Ludwig’s goal of democratizing AI, ML and DL.” 

LF AI & Data will support the neutral open governance for Ludwig to help foster the growth of the project. Key features for Ludwig include:

  • General: A new data type-based approach to deep learning model design that makes the tool suited for many different applications.
  • Flexible: Experienced users have deep control over model building and training, while newcomers will find it easy to use.
  • Extensible: Easy to add new model architecture and new feature data-types.
  • Understandable: Deep learning models internals are often considered black boxes, but we provide standard visualizations to understand their performances and compare their predictions.
  • Easy: No coding skills are required to train a model and use it for obtaining predictions.
  • Open: Ludwig is released under the open source Apache License 2.0.

Ludwig’s type based abstraction allows to define combinations of inputs and output types to create deep learning models to solve many different tasks without writing code: a text classifier can be trained by specifying text as input and category as output, an image captioning system can be trained by specifying image as input and text as output, a speaker verification model can be obtained providing two audio inputs and a binary output, and a time series forecasting can be obtained by providing a time series as input and a numerical value as output. By combining different data types, the number of tasks are limitless. 

Despite not requiring any coding skills, Ludwig also provides an extremely simple programmatic interface, that allows for training deep learning models and uses them for prediction in just a couple lines of code. It also comes with already built in REST serving capabilities, visualizations of models and predictions, and extensible interfaces to add your own models and hyperparameter optimization.

Check out the Getting Started guide to to start working with Ludwig today. Learn more about Ludwig on their website and be sure to join the Ludwig-Announce and Ludwig-Technical-Discuss mail lists to join the community and stay connected on the latest updates. 

A warm welcome to Ludwig and we look forward to the project’s continued growth and success as part of the LF AI & Data Foundation. To learn about how to host an open source project with us, visit the LF AI & Data website.

Ludwig Key Links

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