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Celebrating leaders in AAPI communities

Posted by Google Developer Studio

In recognition of Asian Americans and Pacific Islanders Heritage Month, we are speaking with mentors and leaders in tech and who identify as part of the AAPI community. Many of the influential figures we feature are involved with and help champion inclusivity programs like Google Developer Experts and Google Developer Student Clubs, while others work on leading in product areas like TensorFlow and drive impact through their line of work and communities.

On that note, we are honoring this year’s theme of “Advancing Leaders Through Collaboration” by learning more about the power of mentorship, advice they’ve received from other leaders, and their biggest accomplishments.

Read more about leads in the AAPI community below.

Ben Hong

Senior Staff Developer Experience Engineer at Netlify

What’s the best piece of advice you can offer new/junior developers looking to grow into leadership roles?

There is a lot of advice out there on how to get the most out of your career by climbing the ladder and getting leadership roles. Before you embark on that journey, first ask yourself the question “Why do I want this?”

Becoming a leader comes with a lot of glitz and glamor, but the reality is that it carries a huge weight of responsibility because the decisions and actions you take as a leader will impact the lives of those around you in significant ways you can’t foresee.

As a result, the key to becoming the best leader you can be is to:

  1. Establish what your values and principles are
  2. Align them to the actions you take each and every day

Because at the end of the day, leaders are often faced with difficult decisions that lead to an uncertain future. And without core values and principles to guide you as an individual, you run the risk of being easily swayed by short term trade offs that could result in a long term loss.

This world needs leaders who can stand their ground against the temptations of short-term wins and make the best decisions they can while fighting for those that follow them. If you stand firm in your values and listen to those around you, you’ll be able to create profound impact in your community.

Taha Bouhsine

Data Scientist and GDSCUIZ Lead

What’s the best piece of advice you can offer new/junior developers looking to grow into leadership roles?

Create a journey worth taking. You will face many challenges and a new set of problems. You will start asking a lot of questions as everything seems to be unfamiliar.

Things get much lighter if you are guided by a mentor, as you will get guidance on how to act in this new chapter of life. In your early days, invest as much as you can in building and nurturing a team, as it will save you a lot of time along the road. Surround yourself with real people who take the initiative, get to the action, and are willing to grow and learn, nurture their skills and guide them towards your common goal. Don’t try to be a people pleaser as it’s an impossible mission.

Your actions will offend some people one way or the other. That’s ok as you should believe in your mission, create a clear plan with well-defined tasks and milestones, and be firm with your decision. In the end, responsibility is yours to bear, so at least take it on something you decided, not something that was forced upon you by others.

Finally, when there is fire, look for ways to put it out. Take care of your soul, and enjoy the journey!

Huyen Tue Dao

Android Developer, Trello

What do you love most about being a part of the developer community?

It has been the most rewarding and critical part of my career to meet other developers, learning and sharing knowledge and getting to know them as human beings.

Development is a job of constant learning, whether it is the latest technology, trends, issues, and challenges or the day-to-day intricacies and nuances of writing specialized code and solving problems in efficient and elegant ways. I don’t think I’d have the tools to solve issues large and small without the sharing of knowledge and experience of the developer community. If you’re having a problem of any kind, chances are that someone has had the same challenges. You can take comfort that you can probably find the answer or at least find people that can help you. You can also feel confident that if you discovered something new or learned important lessons, someone will want to hear what you have to say.

I love seeing and being part of this cycle and interchange; as we pool our experience, our knowledge, and insights, we become stronger and more skilled as a community. I would not be the engineer or person that I am without the opportunities of this exchange.

Just as important, though, is the camaraderie and support of those who do what I do and love it. I have been so fortunate to have been in communities that have been open and welcoming, ready to make connections and form networks, eager to celebrate victories and commiserate with challenges. Regardless of the technical and personal challenges of the everyday that may get to me, there are people that understand and can support me and provide brilliantly diverse perspectives of different industries, countries, cultures, and ages.

Malak Magdy Ali

Google Developer Student Club Lead at Canadian International College, Egypt

What’s the best piece of advice you can offer new/junior developers looking to grow into leadership roles?

The best piece of advice I can give to new leaders is to have empathy. Having empathy will make you understand people’s actions and respect their feelings. This will make for stronger teams.

Also, give others a space to lead. Involve your team in making decisions; they come up with great ideas that can help you and teammates learn from each other. In this process, trust is also built, resulting in a better quality product.

Finally, don’t underestimate yourself. Do your best and involve your team to discuss the overall quality of your work and let them make recommendations.

Celebrating global women in tech and trailblazers

Posted by Google Developer Studio

In honor of Women’s History Month, we are featuring tech trailblazers who have made significant contributions to developer communities close to Google and beyond. Many of the women we spoke to work directly with some of our educational outreach and inclusivity programs like Google Developer Experts and Women Techmakers, while others are Google Developer Student Clubs participants or Googlers who do incredible work around the globe.

They all share a passion for making the developer community more accessible and inclusive for generations of women to come. Read about them below to learn more about these individuals whose drive contributes to a better workplace and world.

We’re proud to celebrate #WHM22 with them.

Google Developer Experts

Laura Morillo-Velarde Rodríguez

Guest’s location: Zaragoza, Spain

Tell us more about your role.

I work as a Tech Lead at Seedtag, a contextual advertising company, where I help build an amazing tech team to go through all the technical challenges that we have to face. Besides that, I’m also a Women Techmakers Ambassador and Cloud Google Developer Expert.

Is there a project you’ve worked on recently that you’re excited to share?

During the pandemic I started recording podcasts (in Spanish) with some friends (GDG Spain Podcast, Cloud Español) and one of those is Tech & Ladies Podcast. Every two weeks Cristina Pampín, Silvia García and I talk with other women in tech about their careers, different technologies or other topics related to the tech space.

What makes you passionate about being in the tech space?

I’m passionate about the tech space because you always have something new to learn. I think that this can be a bit overwhelming sometimes, as you need to find the time and it usually involves a lot of self-study, but it also prevents our work from becoming boring.

What is the biggest piece of advice you would offer professionals starting in the tech space?

I would recommend them to make the most of the technical communities that we have. There, you can learn a lot, meet amazing people and contribute to the growth of others with your knowledge and experience.

Luz Maria Maida Claude

Guest’s Location: Ingelheim, Germany

Tell us more about your role.

I’ve been a Software Engineer for the last 7 years. Right now, I’m working at BIX that is the Digital Lab of Boehringer Ingelheim. Although my job description is “Frontend Engineer,” the reality is that every day I have different challenges that involve a great diversity of technologies and tools.

Is there a project you’ve worked on recently that you’re excited to share?

With my team I created some prototypes using hardware oriented to the healthcare systems. In my free time I’m creating a project to collect funds for stray animals.

What makes you passionate about being in the tech space?

Technology gives us the power to turn our ideas into reality, but many of the things that are in our lives today are there because we share our knowledge with others. Thanks to many communities and groups we have more opportunities to improve our environments and grow step by step, something that is important in this time where we need to create changes.

What is the biggest piece of advice you would offer professionals starting in the tech space?

Be curious, trust in yourself and enjoy the journey. It is important to understand that every day counts to reach the objectives that we have. We’ll never have all the knowledge, but your current version knows something more than yesterday and the last week. Don’t stop and continue growing.

Google’s Coding Competitions

Chu-Ling Ko

Guest’s Location: Palo Alto, California

Tell us more about your role

I am a software engineer at Google for Clinicians of Google Health. Also, I am a volunteer for Google’s Coding Competitions. We develop the coding competition problems for Kick Start, Code Jam, and Code Jam to I/O for Women!

Is there a project you’ve worked on recently that you’re excited to share?

Recently, a group of women volunteers including me are working together to develop the problem sets for Code Jam to I/O for Women 2022. We prepare input verifiers, test case generators, various solutions (and some fake ones), and solution articles. It is so exciting that we are all a part of this amazing event!

What makes you passionate about being in the tech space?

I am so passionate about this work because it is something that helps people. Google’s Coding Competition team produces plenty of high-quality problem sets every year, along with comprehensible, educational solution articles. We hope the participants can enjoy and learn new things from each of our coding competitions!

What is the biggest piece of advice you would offer professionals starting in the tech space?

Enjoy and take everything you are doing seriously, and appreciate the people you meet in the adventure!

Tatiyana Mishtal

Guest’s Location: Zurich, Switzerland

Tell us more about your role.

I’m a Senior Software Engineer at YouTube Content ID, also TL of our team. We are working on detection of copyright violations on YouTube. Due to the specifics of our product, we have a very intensive Quality focus – I spend a lot of time on data analysis and cross-team collaboration to improve automated decisions made. At the same time reliability requirements, new signals development and continuous improvements to YouTube infrastructure bring endless interesting engineering challenges as well.

Is there a project you’ve worked on recently that you’re excited to share?

In addition to my main project, I’m also part of the Hash Code team. For several years already we have organized this coding competition for developers of all levels from all around the world. And just a few weeks ago we held the 2022 Qualification Round, which was especially challenging for us. Not only did we need to prepare a hard and exciting problem for the competition as we do every year, but also we had migrated to the new Google Coding Competitions platform and it was our debut there. Thanks to ours and the Coding Competitions team’s joint effort everything went smoothly!

What makes you passionate about being in the tech space?

I really like making things work. I enjoy solving problems, overcoming challenges and in the end seeing how results impact people’s lives. I especially value personal time and it delights me that technology can both improve the quality of people’s lives and cut the “time cost” of many mundane things.

What is the biggest piece of advice you would offer professionals starting in the tech space?

Ask “why” instead of “how”. Why something works the way it does, why people came to particular ideas and why would one use the technology in a way they do. There are a lot of options of “how” for everything in tech, but you need to know “why” to take the most out of it.

Google Developer Groups

Michelle Mannering

Guest’s Location: Melbourne, Victoria

Tell us more about your role.

The GitHub DevRel team gets to do some of the most amazing things in the Developer Relations space. We showcase the products and services that GitHub has, but more importantly we highlight the awesome things our community is doing. Whether someone is a maintainer, an open source contributor, student, or developer working within a company, everyone has a unique and interesting experience. By showcasing these cool developers and projects we can show how people are building better things for the world.

Is there a project you’ve worked on recently that you’re excited to share?

We’re always doing such fun and awesome things at GitHub. One of the things I’ve been working on a lot is the Release Radar. It’s a monthly blog post that goes out showcasing awesome open source projects. We also have a video that goes out featuring some of the projects, talking about what they do, and how others can use them. It’s a really awesome way to get the word out about what developers are building. You can find out more on

What makes you passionate about being in the tech space?

I really love talking to others and hearing about their journey and experience. The best thing about the tech space is listening to someone get really excited about the thing they are building and then showing it to as many people as possible. I’m always so blown away by what people can create. I’ve been in this boat a few times and when you’re learning or building something and you get it right, and it deploys and doesn’t break, it’s not just you that gets excited, but everyone around you!

What is the biggest piece of advice you would offer professionals starting in the tech space?

Don’t think that this is a space where you have to be a genius and know everything. Everyone, all developers, from the most junior to the most senior, still use Stack Overflow to find answers. Never think you are not enough, and on the flip side, never think that you know it all. You can always learn more. So my best advice is “no matter what your role or your experience, always be learning!”

Cassidy Williams

Guest’s Location: Chicago, Illinois

Tell us more about your role.

In short: I build open source and educational content to help people get jobs!

Is there a project you’ve worked on recently that you’re excited to share?

I’ve been working on my newsletter full of web news, practice interview questions, and jokes! It’s at and I’m about to hit my 5-year-anniversary writing it!

What makes you passionate about being in the tech space?

Tech is such a creative, logical, exciting field that can change peoples’ lives. I love helping people get jobs in tech to afford and build the lives and ideas they want to.

What is the biggest piece of advice you would offer professionals starting in the tech space?

Look for people who are where you want to be. Look at their paths, and see how you can try to mimic it. Make yourself available for people to mimic you. One of my favorite quotes is to “lift as you climb”! If you help others as you move forward in their careers as you move forward in yours, you’ll build a wonderful community of people around you, and make the tech community a better place!

Remember the $86 million license plate scanner I replicated?

I caught someone with it

Canceled driver caught in action.

A few weeks ago, I published what I thought at the time was a fairly innocuous article: How I replicated an $86 million project in 57 lines of code.

I’ll admit — it was a rather click-bait claim. I was essentially saying that I’d reproduced the same license plate scanning and validating technology that the police in Victoria, Australia had just paid $86 million for.

Since then, the reactions have been overwhelming. My article received over 100,000 hits in the first day, and at last glance sits somewhere around 450,000. I’ve been invited to speak on local radio talk shows and at a conference in California. I think someone may have misread Victoria, AU as Victoria, BC.

Although I politely declined these offers, I have met for coffee with various local developers and big name firms alike. It’s been incredibly exciting.

Most readers saw it for what it was: a proof of concept to spark discussion about the use of open source technology, government spending, and one man’s desire to build cool stuff from his couch.

Pedants have pointed out the lack of training, support, and usual enterprise IT cost padders, but it’s not worth anyone’s time exploring these. I’d rather spend this post looking at my results and how others can go about shoring up their own accuracy.

Before we get too deep into the results, I’d like to go over one thing that I feel was lost in the original post. The concept for this project started completely separate from the $86 million BlueNet project. It was by no means an attempt to knock it off.

It started with the nagging thought that since OpenCV exists and the VicRoads website has license plate checks, there must be a way to combine the two or use something better.

It was only when I began my write-up that I stumbled upon BlueNet. While discovering BlueNet and its price tag gave me a great editorial angle, with the code already written. There were bound to be some inconsistencies between the projects.

I also believe part of the reason this blew up was the convenient timing of a report on wasteful government IT spending in Australia. The Federal Government’s IT bill has shot up from $5.9 billion to $10 billion, and it delivered dubious value for that blow out. Media researchers who contacted me were quick to link the two, but this is not something I am quick to encourage.

A Disclaimer

In the spirit of transparency, I must declare something that was also missing from the original post. My previous employer delivered smaller (less than $1 million) IT projects for Victoria Police and other state bodies. As a result, I’ve undergone police checks and completed the forms required to become a VicPol contractor.

This may imply I have an axe to grind or have some specific insider knowledge, but instead I am proud of the projects we delivered. They were both on time and on budget.

Visualizing the Results

The following is a video representation of my results, composited in After Effects for a bit of fun. I recorded various test footage, and this was the most successful clip.

I will go into detail about ideal camera setups, detection regions, and more after the video. It will help you better understand what made this iPhone video I took from through the windscreen a better video than a Contour HD angled out the side window.

An Ethical Dilemma

If you saw the hero graphic of this article or watched the video above, you may have noticed a very interesting development: I caught someone.

Specifically, I caught someone driving a vehicle with a canceled registration from 2016. This could have happened for many reasons, the most innocent of which is a dodgy resale practice.

Occasionally, when the private sale of a vehicle is not done by the book, the buyer and seller may not complete an official transfer of registration. This saves the buyer hundreds of dollars, but the vehicle is still registered to the seller. It’s not unheard of for a seller to then cancel the registration and receive an ad hoc refund of remaining months, also worth hundreds of dollars.

Alternatively, the driver of the vehicle could well be the criminal we suspect that they are.

So, although I jokingly named the project plate-snitch when I set it up on my computer, I’m now faced with the conundrum of whether to report what I saw.

Ultimately, the driver was detected using a prototype of a police-only device. But driving on a 2016 registration (canceled, not expired) is a very deliberate move. Hmm.

Back to the Results

Of the many reactions to my article, a significant amount were quite literal and dubious. Since I said I replicated the software, they asserted that I must have a support center, warranties, and training manuals. One even attempted to replicate my results and hit the inevitable roadblocks of image quality and source material.

Because of this, some implied that I cherry-picked my source images. To that I can only say, “Well, duh.”

When I built my initial proof of concept (again, focusing on validating an idea, not replicating BlueNet), I used a small sample set of less than ten images. Since camera setup is one of, if not the most, important factors in ALPR, I selected them for ideal characteristics that enhance recognition.

At the end of the day, it is very simple to take a fragile proof of concept and break it. The true innovation and challenge comes from taking a proof of concept, and making it work. Throughout my professional career, many senior developers have told me that things can’t be done or at least can’t be done in a timely manner. Sometimes they were right. Often, they were just risk averse.

“Nothing is impossible until it is proven to be.”

Many people bastardize this quote, and you may have seen or heard one of it’s incarnations before. To me, it neatly summarizes a healthy development mindset, in which spiking and validating ideas is almost mandatory to understanding them.

Optimal ALPR Camera Setups

This project is so exciting and different for me because it has a clear success metric — whether the software recognizes the plate. This can only happen with a combination of hardware, software, and networking solutions. After posting my original article, people who sell ALPR cameras quickly offered advice.

Optical Zoom

The most obvious solution in hindsight is the use of an optical zoom. Though I explore other important factors below, none lead to such a sheer increase in recognition as this. In general, professional ALPR solutions are offset at an angle, trained on where the license plate will be, and zoomed into the area to maximize clarity.

This means the more zoom, more pixels to play with.

All the cameras I had at my disposal were of a fixed lens. They included:

  • A Contour HD action camera. These came out in 2009, and I use mine to record my cycling commute and to replay each week’s near death experience.
  • A Fujifilm X100S (famously a fixed prime lens)
  • My iPhone 6+

The featured test run was recorded on my phone. My only method of replicating an optical zoom was using an app to record at 3K instead of 1080p, and then digitally zooming and cropping. Again, more pixels to play with.

Angle & Positioning

The viewing angle of 30° is often referenced as the standard for ideal plate recognition. This is incredibly important when you learn that BlueNet uses an array of cameras. It also makes sense when you consider what a front facing camera would generally see — not very much.

What a front facing ALPR camera sees — not much.

If I had to guess I’d say a mostly forward-facing array would be the ideal setup. It would consist of a single camera pointed dead center as above, two off-center at 30° each side, and a single rear-facing camera. The value in having most of the cameras pointed forward would come from the increased reaction time if the vehicle is traveling in the opposite direction. This would allow a quicker scan, process, and U-turn than if the rear facing cameras picked up a suspect vehicle already ten meters past the police vehicle.

A four camera array would need to be angled similar to this. Icons from Freepik.

A Gymbal

When compositing the video, I thought about stabilizing the footage. Instead I opted to show the bumpy ride for what it was. What you saw was me holding my phone near the windscreen while my wife drove. Check out that rigorous scientific method.

Any production-ready version of a vehicle-mounted ALPR needs some form of stabilisation. Not a hand.

Other Important Factors

Frame Rate

Both the attempt to replicate my project and my recordings since then explored the same misconception that ALPR sampling frame rate may be linked to success. In my experience, this did nothing but waste cycles. Instead, what is incredibly important is the shutter speed creating clean, crisp footage that feeds well into the algorithm.

But I was also testing fairly low-speed footage. At most, two vehicles passing each other in a 60km/h zone created a 120km/h differential. BlueNet, on the other hand, can work up to an alleged 200km/h.

As a way of solving this, a colleague suggested object detection and out-of-band processing. Identify a vehicle and draw a bounding box. Wait for it to come into the ideal recognition angle and zoom. Then shoot a burst of photos for asynchronous processing.

I looked into using OpenCV (node-opencv) for object recognition, but I found something simpler like face detection, taking anywhere from 600–800ms. Not only less than ideal for my use, but pretty poor in general.

Hype-train TensorFlow comes to the rescue. Able to run on-device, there are examples of projects identifying multiple vehicles per frame at an astounding 27.7fps. This version could even expose speed estimations. Legally worthless, but perhaps useful in every day policing (no fps benchmark in readme).

To better explain how high-performance vehicle recognition could couple with slower ALPR techniques, I created another video in After Effects. I imagine that the two working hand-in-hand would look something like this:

Frame Rate vs Shutter Speed

A different manifestation of frame rate is largely influenced upon shutter speed, and more specifically, the rolling shutter issues that plague early or low end digital movie recorders. The following is a snapshot from some Contour HD footage. You can see at only 60km/h the rolling shutter issue makes the footage more or less unusable from an ALPR point of view.

Rolling shutter issues on a Contour HD @ 60km/h.

Adjusting frame rate on both the Contour HD and my iPhone did not result in noticeably less distortion. In theory, a higher shutter speed should produce clearer and crisper images. They’d become increasingly important if you were to chase the 200km/h BlueNet benchmark. Less blur and less rolling shutter distortion would ideally lead to a better read.

Open ALPR Version

One of the more interesting discoveries was that the node-openalpr version I was using is both out-of-date and not nearly as powerful as their proprietary solution. While an open source requirement was certainly a factor, it was amazing how accurately the cloud version could successfully read frames that I couldn’t even identify a plate on.

ALPR Country Training Data

I also found that the main node-openalpr package defaults to US country processing with no way of overriding it. You have to pull down someone else’s fork which allows you to then provide an extra country parameter.

Slimline Australian plates need their own separate country detection to regular Australian plates?

But this doesn’t always help. Using the default US algorithm I was able to produce the most results. Specifying the Australian data set actually halved the number of successful plate reads, and it only managed to find one or two that the US algorithm couldn’t. Providing the separate “Australian Wide Plate” set again halved the count and introduced a single extra plate.

There is clearly a lot to be desired when it comes to Australian-based data sets for ALPR, and I think that the sheer number of plate styles available in Victoria is a contributing factor.

Good luck with that.

Planar Warps

Open ALPR comes with one particular tool to reduce the impact of distortion from both the camera angle and rolling shutter issues. Planar warp refers to a method in which coordinates are passed to the library to skew, translate, and rotate an image until it closely resembles a straight-on plate.

In my limited testing experience, I wasn’t able to find a planar warp that worked at all speeds. When you consider rolling shutter, it makes sense that the distortion grows relative to vehicle speed. I would imagine feeding accelerometer or GPS speed data as a coefficient might work. Or, you know, get a camera that isn’t completely rubbish.

The planar warp tool provided with Open ALPR

What others are doing in the industry

Numerous readers reached out after the last post to share their own experiences and ideas. Perhaps one of the more interesting solutions shared with me was by Auror in New Zealand.

They employ fixed ALPR cameras in petrol stations to report on people stealing petrol. This in itself is not particularly new and revolutionary. But when coupled with their network, they can automatically raise an alert when known offenders have returned, or are targeting petrol stations in the area.

Independent developers in Israel, South Africa, and Argentina have shown interest in building their own hacked-together versions of BlueNet. Some will probably fare better than others, as places like Israel use a seven digit license plates with no alphabet characters.

Key Takeaways

There is simply too much that I’ve learned in the last few weeks of dabbling to fit into one post. While there have been plenty of detractors, I really do appreciate the support and knowledge that has been sent my way.

There are a lot of challenges you will face in trying to build your own ALPR solution, but thankfully a lot of them are solved problems.

To put things in perspective, I’m a designer and front end developer. I’ve spent about ten hours now on footage and code, another eight on video production, and at least another ten on write-ups alone. I’ve achieved what I have by standing on the shoulders of giants. I’m installing libraries built by intelligent people and have leveraged advice from people who sell these cameras for a living.

The $86 million question still remains — if you can build a half-arsed solution that does an okay job by standing on the shoulders of giants, how much more money should you pour in to do a really really good job?

My solution is not even in the same solar system as the 99.999% accurate scanner that some internet commenters seem to expect. But then again, BlueNet only has to meet a 95% accuracy target.

So if $1 million gets you to 80% accuracy, and maybe $10 million gets you to 90% accuracy — when do you stop spending? Furthermore, considering that the technology has proven commercial applications here in Oceania, how much more taxpayer money should be poured into a proprietary, close-sourced solution when local startups could benefit? Australia is supposed to be an “innovation nation” after all.

Remember the $86 million license plate scanner I replicated? was originally published in A Cloud Guru on Medium, where people are continuing the conversation by highlighting and responding to this story.

How I replicated an $86 million project in 57 lines of code

When an experiment with existing open source technology does a “good enough” job

The Victoria Police are the primary law enforcement agency of Victoria, Australia. With over 16,000 vehicles stolen in Victoria this past year — at a cost of about $170 million — the police department is experimenting with a variety of technology-driven solutions to crackdown on car theft. They call this system BlueNet.

To help prevent fraudulent sales of stolen vehicles, there is already a VicRoads web-based service for checking the status of vehicle registrations. The department has also invested in a stationary license plate scanner — a fixed tripod camera which scans passing traffic to automatically identify stolen vehicles.

Don’t ask me why, but one afternoon I had the desire to prototype a vehicle-mounted license plate scanner that would automatically notify you if a vehicle had been stolen or was unregistered. Understanding that these individual components existed, I wondered how difficult it would be to wire them together.

But it was after a bit of googling that I discovered the Victoria Police had recently undergone a trial of a similar device, and the estimated cost of roll out was somewhere in the vicinity of $86,000,000. One astute commenter pointed out that the $86M cost to fit out 220 vehicles comes in at a rather thirsty $390,909 per vehicle.

Surely we can do a bit better than that.

Existing stationary license plate recognition systems

The Success Criteria

Before getting started, I outlined a few key requirements for product design.

Requirement #1: The image processing must be performed locally

Streaming live video to a central processing warehouse seemed the least efficient approach to solving this problem. Besides the whopping bill for data traffic, you’re also introducing network latency into a process which may already be quite slow.

Although a centralized machine learning algorithm is only going to get more accurate over time, I wanted to learn if an local on-device implementation would be “good enough”.

Requirement #2: It must work with low quality images

Since I don’t have a Raspberry Pi camera or USB webcam, so I’ll be using dashcam footage — it’s readily available and an ideal source of sample data. As an added bonus, dashcam video represents the overall quality of footage you’d expect from vehicle mounted cameras.

Requirement #3: It needs to be built using open source technology

Relying upon a proprietary software means you’ll get stung every time you request a change or enhancement — and the stinging will continue for every request made thereafter. Using open source technology is a no-brainer.

My solution

At a high level, my solution takes an image from a dashcam video, pumps it through an open source license plate recognition system installed locally on the device, queries the registration check service, and then returns the results for display.

The data returned to the device installed in the law enforcement vehicle includes the vehicle’s make and model (which it only uses to verify whether the plates have been stolen), the registration status, and any notifications of the vehicle being reported stolen.

If that sounds rather simple, it’s because it really is. For example, the image processing can all be handled by the openalpr library.

This is really all that’s involved to recognize the characters on a license plate:

A Minor Caveat
Public access to the VicRoads APIs is not available, so license plate checks occur via web scraping for this prototype. While generally frowned upon — this is a proof of concept and I’m not slamming anyone’s servers.

Here’s what the dirtiness of my proof-of-concept scraping looks like:


I must say I was pleasantly surprised.

I expected the open source license plate recognition to be pretty rubbish. Additionally, the image recognition algorithms are probably not optimised for Australian license plates.

The solution was able to recognise license plates in a wide field of view.

Annotations added for effect. Number plate identified despite reflections and lens distortion.

Although, the solution would occasionally have issues with particular letters.

Incorrect reading of plate, mistook the M for an H

But … the solution would eventually get them correct.

A few frames later, the M is correctly identified and at a higher confidence rating

As you can see in the above two images, processing the image a couple of frames later jumped from a confidence rating of 87% to a hair over 91%.

I’m confident, pardon the pun, that the accuracy could be improved by increasing the sample rate, and then sorting by the highest confidence rating. Alternatively a threshold could be set that only accepts a confidence of greater than 90% before going on to validate the registration number.

Those are very straight forward code-first fixes, and don’t preclude the training of the license plate recognition software with a local data set.

The $86,000,000 Question

To be fair, I have absolutely no clue what the $86M figure includes — nor can I speak to the accuracy of my open source tool with no localized training vs. the pilot BlueNet system.

I would expect part of that budget includes the replacement of several legacy databases and software applications to support the high frequency, low latency querying of license plates several times per second, per vehicle.

On the other hand, the cost of ~$391k per vehicle seems pretty rich — especially if the BlueNet isn’t particularly accurate and there are no large scale IT projects to decommission or upgrade dependent systems.

Future Applications

While it’s easy to get caught up in the Orwellian nature of an “always on” network of license plate snitchers, there are many positive applications of this technology. Imagine a passive system scanning fellow motorists for an abductors car that automatically alerts authorities and family members to their current location and direction.

Teslas vehicles are already brimming with cameras and sensors with the ability to receive OTA updates — imagine turning these into a fleet of virtual good samaritans. Ubers and Lyft drivers could also be outfitted with these devices to dramatically increase the coverage area.

Using open source technology and existing components, it seems possible to offer a solution that provides a much higher rate of return — for an investment much less than $86M.

Part 2 — I’ve published an update, in which I test with my own footage and catch an unregistered vehicle, over here:

Remember the $86 million license plate scanner I replicated? I caught someone with it.

How I replicated an $86 million project in 57 lines of code was originally published in A Cloud Guru on Medium, where people are continuing the conversation by highlighting and responding to this story.

Design your cloud center of excellence for constant evolution

Accelerate transformation at supersonic speed with a well designed cloud program enabled by committed change agents

Werner Vogels launched the 2017 AWS Summits in Sydney with his typical non-conformist approach that transcends both keynotes and strategy. The Amazon CTO took center stage wearing a custom T-shirt emblazoned with “Werner Against the Machine” — the chest insignia of a modern superhero.

As the ultimate committed change agent disguised as a CTO, Werner is using his AWS superpowers to save businesses worldwide — using speed, analytics, flexibility, the skill to adapt, and the power to take flight from ‘hostile’ database vendors.

The Supersonic Speed of an Enterprise

TL;DR — speed matters

For most other enterprises, supersonic speed is the most elusive superpower for a heroic cloud journey — and the most necessary to harness.

During my 20-year career at Capital One, speed at scale was the modus operandi of the lean enterprise. Accelerated by an agile mindset and DevOps culture, the rapid rate of adoption enabled a cloud journey that transformed Capital One into what is now essentially a large FinTech startup.

Capital One’s accelerated technology transition is powered by the API-driven cloud computing services from AWS, enabled by real-time access to big data, and fueled by a commitment to the open source community. While technology plays a leading role, the hardest part of that transition and ultimate superpower is really a talent transformation.

The Gravitational Pull of Legacy

Gaining the supersonic speed to achieve escape velocity from on-premise data centers requires bold leadership with a long-term dedication to innovation. There is no magic pill for enterprises — although going all-in with AWS is a leap in the right direction.

The Cloud Center of Excellence is the tip of the spear for enterprises adopting the cloud

Cloud computing has posed a disruptive threat for years, but short-term incentive structures provide little reason for large corporations to invest in the disruptive technology. Many enterprises continue to be anchored by the weight of their own internal processes and platforms — and paralyzed by the FUD of vendors in survival mode contributing noise to their echo chambers.

“It’s not the big that eat the small … it’s the fast that eat the slow.“
 — Laurence Haughton

The gravitational pull is preventing many enterprises from gaining momentum and advantage in the cloud — at least not at the speed required to avoid death by the fast and hungry. The only way out of the dilemma is to adopt a new approach — where continuous innovation is the new bottom line and speed is the name of the game.

Achieve Escape Velocity with a CCoE

For enterprises that are serious about cloud adoption and aspire to achieve supersonic speed, the executive leadership team must invest their time, resources and budget into the sponsorship of a Cloud Center of Excellence (CCoE).

The CCoE is an essential mechanism for large organizations planning to achieve the velocity required to escape the gravitational pull of their own death star — a private cloud or an on-premise data center.

Design the CCoE for Constant Evolution

TL;DR — transition matters

The Cloud Center of Excellence will continually evolve in order too keep pace with the rate of innovation associated with cloud adoption. Modern enterprises should be very purposeful with the organization design principles of their cloud program structure.

The leaders of cloud programs should observe the flow of their value within the organization, and design an adaptive structure that evolves alongside the internal needs of the enterprise. Simon Wardley infuses these concepts of flow and transition within his value chain mapping strategies — and his design principles apply as much to organizational design as they do towards product evolution.

Applying Simon’s design principles of Pioneers → Settlers → Town Planners (PST) toward an organization’s cloud adoption program offers an innovative approach for effectively navigating the evolving journey. Guided by astute situational awareness, organizations learn to continuously pivot through the cycle — from the exploration stage, through expansion, toward the enhancement of industrialized components and patterns.

Accelerate through the ‘trough of despair’ with an education program that achieves critical mass of cloud fluency

Stage 1: The Cloud Center of Exploration

The Pioneers Explore

Embarking on a cloud journey requires a tremendous amount of iterative experimentation with the underlying AWS utility compute services. During this stage of early adoption, the core team is focused on pioneering engineers with plenty of aptitude and attitude.

The two-pizza team leverages agile techniques to break things daily — and collect the data which will determine the patterns of future success.

The core team is stacked with battle-tested engineers with deep experience in understanding how critical functions currently operate, and know how to translate existing data center platforms into cloud services. These engineers have plenty of scars and war stories from previous tours of duty with security, network, and access control.

ProTip 1: An executive sponsor is absolutely essential during the early phases of the cloud adoption. The sponsor must be a strong advocate, provide plenty of air-cover, and actively engage with the core team for the purpose of removing impediments from the board. 

Stage 2: Cloud Center of Expansion

The Settlers Expand

Once the pioneers solidify early successes into identified patterns, the focus turns toward scaling the prototypes into products and services that are consumable by the enterprise. By listening to a broad range of internal customers, the settlers refine the patterns and help the understanding grow.

When the cloud services start to scale across an enterprise, it’s natural to place a heavier emphasis on governance and controls. A key advantage of AWS is the ability to engineer your governance by leveraging the API-driven services to access real-time controls and compliance.

How we know what is knowable, or epistemology, is much different in the cloud versus on-premise. — adrian cockcroft

At this stage, it’s imperative to focus on scaling the early understanding of AWS to the enterprise — achieving critical mass of cloud fluency is the only way an organization can sustain a transition to the new operating model.

Social Consensus Through the Influence of Committed Minorities shows that when just 10% of randomly distributed committed agents holds an unshakable belief, the prevailing majority opinion in a population can be rapidly reversed.

Invest the time and money into a multi-dimensional cloud education program. An engaged workforce armed with compelling context and content will dramatically accelerate your organization through the ‘trough of despair’ and ensure more attraction versus attrition.

During the enablement phase, the core team should no longer be considered the most cloud savvy department in the organization. By unleashing cloud superpowers upon thousands of developers throughout the enterprise, the core team should pivot and begin harvesting new and improved patterns from other divisions.

Elevating other departments beyond your core team’s existing capabilities is a key early indicator of achieving escape velocity.

ProTip: Instead of outsourcing your cloud training to Human Resources, tightly integrate cloud education as a core function on your program team. Leverage the AWS certifications as a benchmark for cloud fluency and set a minimum goal of 10% enterprise-wide.

Stage 3: Cloud Center of Enhancement

The Town Planners Enhance

As the cloud journey matures, it should lead toward the commoditization of services that result in more cost efficient, faster, and industrialized platforms. In highly regulated industries like financial services, the organization depends on these town planners to ensure customers and regulators can trust what’s built.

Innovation it’s not just limited to the early stages and pioneers — it’s also found in the operational stages of cloud adoption. For example, Capital One’s Terren Peterson is evolving the mindsets of their engineering teams by embracing the concepts of Site Reliability Engineering using innovative approaches to manage operations.

Their SRE teams leverages industrialized utility functions to manage compliance and controls with Cloud Custodian. The SRE’s also contribute new functions to the ever expanding platform — completing the cycle as higher order services continuously evolve.

Cloud Custodian is a function-based policy rules engine. The origin of the service demonstrates the PST design principles at work. Originally developed internally by their pioneers, it was scaled across the entire enterprise by settlers, and finally driven toward open source commoditization by the town planners.

ProTip: Involve your operational teams starting on day one of the cloud journey.  Since operations is 24x7, consider a shift-and-lift for a subset of workloads.  Leverage a cloud capable MSP for interim support to lighten the load during their talent transition.


Using these organizational design principles, a cloud program team can begin to continually cycle through the explore-expand-enhance stages. Over time, this approach will begin to harness Werner’s superpowers and accelerate cloud adoption at supersonic speeds.

Drew is an AWS Community Hero, Alexa Champion, and maker of dad jokes.
Follow on Twitter
@drewfirment. #WePowerTech

Design your cloud center of excellence for constant evolution was originally published in A Cloud Guru on Medium, where people are continuing the conversation by highlighting and responding to this story.

Alexa Data Analytics are a Gold Mine

Amazon is collecting a massive treasure trove of data from consumers invoking custom skills that’ll drive further disruption

The consumer data derived from the Alexa voice skills is a gold mine for startups like VoiceLabs

“There’s gold in them thar hills and there’s millions in it.” — M. F. Stephenson

The recently published 2017 Voice Report from Adam Marchick is a solid “state of the union on voice platforms” — and you’ll really appreciate the effort and intellectual rigor that VoiceLabs put into their analysis.

Here are my two takeaways from this report:

  1. Alexa-like voice platforms are about to cross-the-chasm
  2. Amazon is sitting on a gold mine of data

In the new space of voice analytics, VoiceLabs is a standout startup offering services that “provide accurate, real-time data to developers about how consumers use their Amazon Alexa skills and Google Assistant Actions.”

The emerging strategy to collect real-time data was referenced in a recent article which describes how analytics startups like VoiceLabs are Scrambling for Alexa Data from Amazon — and rightfully so.

The private questions that consumers “ask” voice platforms generates an extremely valuable data set. At the very least, the consumer data derived from the voice skills provides can be used to create highly accurate profiles of consumers for marketing purposes.

The Value of Voice Data Analytics

For now, Amazon is keeping the majority of the voice data to themselves. And since Amazon has quickly attracted a massive ecosystem to feed the accumulation of that data set, they have a strategic advantage over competing voice platforms — one they know exactly how to exploit.

The most obvious way for Amazon to leverage the voice analytics is feeding the data into their existing consumer models for targeted pricing and and promotions. For example, if someone asks “Alexa, what are the signs of pregnancy” — the customer should also expect to see diapers as an item on their suggested wish-list the next time they go shopping on Amazon.

But the consumer line of smart-speaker devices and the developers building custom skills are just a pawn in Amazon’s longer-term strategy. Amazon is playing chess and positioning Alexa as the Queen — while everyone else is getting played, or at best, playing checkers.

Amazon can lead a horse to water better than anyone in the industry

Woo the Developers, Then Woo the Enterprise

With over 5M units of Amazon’s smart-speaker devices sold in the past two years, there is an army of users interacting daily with custom-developed Alexa skills. As Amazon builds toward critical mass, it’s hard to bet against their domination of the voice platform — just as Amazon’s AWS has dominated the cloud industry since 2007.

Similar to how Amazon’s cloud strategy evolved from the developer community to the enterprise, AWS will follow a similar pattern from consumer to enterprise dominance with their Alexa voice platform.

When AWS introduced their first set of cloud services, they were geared almost exclusively toward the 180,000 members of their development community. Since 2007, AWS has grown exponentially as it’s strategically cycled iteratively through a series of innovate → leverage → commoditize (ILC) cycles. Each cycle create new services — enabling AWS to move up their value chain and step closer to the needs of enterprise customers.

And with the introduction of Lambda, it’s now possible for AWS to move services even faster through the ILC cycle since underlying functions supporting new services are essentially commoditized as they’re innovated.

After years of moving up the value chain with higher level abstraction of services, AWS is now clearly focused on features that woo the enterprises instead of the developers — such as Snowmobile and Managed Services Program. Nobody should be surprised when the exact same strategy plays out with Alexa.

Amazon ‘s experience with AWS will be a key differentiator in their dominance of voice platforms

This Isn’t Amazon’s First Rodeo

While Amazon harvests usage patterns and strategic insight from their voice platform, the flow through the Innovate-Leverage-Commoditize (ILC) cycle should seem very familiar to the early days of AWS.

  • Amazon’s Alexa innovative voice platform is first to market (i.e. AWS).
  • The initial Alexa platform has enough minimal viable features on the development portal to attract an ecosystem of developer (i.e. EC2, S3).
  • The development community and Alexa Dev Champions are enticed by compelling features, and free T-shirts, to leverage the platform and build custom skills (i.e. web sites).
  • AWS monitors their usage patterns and leverages the learnings to drive further innovation and commoditization (i.e. Jeff Barr daily blogs).
  • Rinse and repeat.

Every time an Alexa skill is invoked, Amazon learns — each utterance sharpening the machine learning and artificial intelligence capabilities.

The FUD is Real

With feature-rich IaaS and PasS offerings now available from AWS, migrating a corporations on-premise data centers into their cloud is not a matter of ‘if “ — but “when”. Everyone knows that customer’s don’t give a shit about your data centers.

In similar fashion, AWS will leverage the voice data analytics from consumer Alexa skills to fuel their longer-term strategy — enterprise call centers.

With voice services, their target just moves down the street from the data center — to corporate call centers. The recent AWS releases of the Polly, Lex, and Rekognition services services offer compelling features that are early indicators of a viable strategy.

In the coming years, expect to hear lots of familiar Fear, Uncertainty, and Doubt (FUD) derived from cloud adoption and reapplied to the call center industry as thought leaders declare all the reason why this won’t work — probably starting with security issues. This phase will be followed by nervous sales teams casting doubt on the viability of machine learning and artificial intelligence for call centers.

swardley offers a simple yet effective illustration of how FUD impacts enterprise adoption

Ultimately, we’ll know when the AWS strategy has succeed when incumbent vendors start proposing hybrid call-center solutions. The final sign of success is when all the engineers are arguing about the term callcenterless — and every article starts with “first and foremost, there are still call centers in a callcenterless architecture”.

Let’s just hope the companies we are patronizing have already migrated their call centers to AWS voice platforms at that point. Then we can all finally stop yelling “representative” to get actual service through voice channels — instead we can just ask Alexa.

Alexa Data Analytics are a Gold Mine was originally published in A Cloud Guru on Medium, where people are continuing the conversation by highlighting and responding to this story.

A company’s success will be measured by their investment in talent transformation

Talent Transformation

“There’s no way through this problem other than education and we have a long way to go” — Marc Andreessen

If software is eating the world, then the forward progress of companies is directly related to their ability to develop software effectively. — Joe Emison

Software is eating the world— and many businesses are being served for dinner by the fast and furious who leverage Amazon Web Services’ ever-expanding infrastructure and platform services.

The adoption of cloud is now mainstream, and AWS makes it possible for progressive companies to focus on their marketplace differentiators — while AWS does the heavy-lifting of utility compute.

With every release of new features and services, AWS is creating even more opportunities for companies to enjoy the benefits of frictionless innovation. And for what it’s worth, the opportunities to feast at the table of AWS applies as much to enterprises as it does to start-ups.

Emerging serverless architectural patterns are accelerating the insane pace of innovation, along with the ability for a business to efficiently disrupt at scale.

The endless opportunities are now only limited by your imagination — especially as serverless seamlessly integrates with maturing AWS machine learning and artificial intelligence services such as Polly, Lex, and Rekognition.

As Joe Emison highlights in his series on modern programming practices, a company’s forward progress is directly related to their ability to effectively develop software — and take advantage of infrastructure and platform services from cloud providers like Amazon Web Services.

In the blog, Joe references the famous Wall Street Journal essay published in 2011 by Marc Andreessen — Why Software Is Eating the World — that outlines the shift toward an economy dominated by software-based companies.

Most notable in Marc’s essay is the now cliché statement that “every company needs to become a software company” — regardless of your industry. He correctly predicted that companies who fail to embrace the transition should prepare to be disrupted by the next Uber.

While most organizations are focusing on the bright and shiny aspects of the technology disruption outlined in the essay, very few heard Andreessen’s most important call to action for overcoming the challenges of the technology transition—education.

Overcoming the lack of skills is the most critical — and often overlooked — prerequisite for a company to participate in the software revolution.

Many people in the U.S. and around the world lack the education and skills required to participate in the great new companies coming out of the software revolution. […] This problem is even worse than it looks because many workers in existing industries will be stranded on the wrong side of software-based disruption and may never be able to work in their fields again. There’s no way through this problem other than education, and we have a long way to go.

There’s a simple path to determining the success of a company trying to compete in today’s software-driven marketplace: assess how much they are investing in a meaningful talent transformation program that focuses on modern software development skills and cloud fluency.

With over 25% of corporations identifying the lack of cloud expertise as the #1 challenge with cloud adoption, there’s no other way through this problem other than education.

And we have a long way to go.

A company’s success will be measured by their investment in talent transformation was originally published in A Cloud Guru on Medium, where people are continuing the conversation by highlighting and responding to this story.