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MASON BEES

340 Stories To Learn About Careers

Community Violence Intervention Programs: Are They Effective?

End of the year and another boring annual review? Well, if you have superheroes in your team make them feel special – like true superheroes deserve. performance Photo by ErikaWittlieb I have to say that I believe in a performance review. A good performance review makes people feel valued and important, motivate them to grow and help them set their career goals. But to make it work well, a performance review needs to be done right. My was a part of the big reorganization on the group level some time ago. One of the consequences was that our own pretty good performance review program was replaced by a new one, which in my opinion hasn’t fully matched the nature of our work. Many people in the company haven’t been keen on doing it and therefore they haven’t been doing it in a proper fashion. To me, it has felt like a waste of time. company Additionally, my team has been struggling with low morale recently. Because of many reasons, on which we haven’t had much influence, the project we are working in hasn’t been going perfectly fine. In consequence, we started feeling stressed out and demotivated. I wanted to find something that would boost the morale and cheer people up. One of the outstanding things I find in my work are people who I work with. I find my team to be the best of a kind and I know that others feel similar. I thought that the end of the year can be a great opportunity to remind my teammates how great they are. I decided to run an extraordinary personal annual performance review. Getting feedback I started with getting the personal feedback about my team members. During biweekly one-on-ones, I asked everyone to say something about every colleague in the team. I said that I needed it to help me in the annual performance review. For our brains, and because I wanted to boost morale instead of plunging it, I asked for positive feedback only. I asked everyone by surprise and I didn’t give much time to think because I didn’t want to take the precious time of my developers and I thought that this way the feedback would be the humblest. bad emotions are stronger and more memorable than good ones When guys heard that, every one of them reacted similarly: “Well… that’s hard”. They thought that they wouldn’t make it. But eventually, everyone was able to say a couple of positive words about every other team member without a need to think much. To help them out, I asked them some auxiliary questions: What do you like about him? What impresses you about him? Has he surprised you positively recently? Has he helped you with something recently? I was surprised that their feedback was quick, humble, extensive and creative. I was expecting that they would copy the same obvious observations, but it turned out that most of their opinions were unique. It made me aware that asking others for a feedback about a person can be valuable and helpful when you do a personal review – different people have different experiences, contact on different matters, point out and value different behaviors. When guys were sharing their feedback I was solemnly writing their words down in my notebook. Sometimes I asked some additional questions to precise what they meant or get more detailed opinions. After the sessions, I rewrote some of the quotes, so that they sounded more neatly, but I left their original meaning. Here are some them: He is a man of compromise. We can easily work out good solutions together. He impresses me with his verve, energy and a fact, that he’s so eager to growth professionally. You can see that he cares about delivering a great product. He is a guy you can always count on. It’s cool that he always finds willingness and time to answer my questions. Certificates I put the most interesting quotes about each person on the sheet of paper and added their name in the heading. My plan was to print them and hand out to my team members at the end of the year. I realized that they looked pretty poor and I was thinking about the ways to make them look fancier. The quotes were showing that each of the guys is unique and has some superpowers. A thought crossed my mind that I would create certificates saying that each of the guys is a superhero! I drew their superheroes’ avatars using a . The choice of superheroes was pretty random, but in some cases, I based on some obvious clues – eg. a web developer was portrayed as a Spider-Man. free online service Superheros’ avatars I put avatars on the certificates and added some simple design (as complicated as my crummy design skills allowed me to). I contacted a local print shop and requested to print them on a high-quality shiny paper. I wanted to make them look professional like real certificates look like. Gifts I knew I would deliver the certificate at the end of the year, just before the Christmas break, so I decided to make them a part of some bigger Christmas gift. What does nearly every developer like that is a versatile and affordable gift? An answer was obvious: a beer. I’ve been working with my team for more than 1 ½ years and during this time we drunk many beers together, so I pretty much knew the beer style preferences of each of my team members. I went to a local craft beer shop and chose the best of each of preferred beer styles – a bottle for a guy. Printed certificates and the selection of beers Since these were about to be Christmas gifts I also wanted to include something that connects to Christmas. My fiancée and I are having a tradition of baking and decorating gingerbread cookies every year before Christmas. I decided to add some of these delicious homemade cookies to the bundles. Gingerbread cookies before and after wrapping up I also added Christmas cards with the best Christmas and New Year wishes to them and their loved ones. I put everything in nice Christmas gift bags. A content of a bundle and all gifts together Delivering I planned to give the gifts on the last day before the Christmas break. That day we had a sprint demo and after each sprint demo we run the retrospective meeting (you can read about how we run them, or actually what mistakes we try to avoid when running them, in ). I thought it was a perfect occasion to reveal the surprise. my blog post Because the occasion was special I thought about running an extraordinary retrospective session. We had done the once before and wished to try it again. This was a great opportunity to make it happen. During the speeding dating retro, we focus on providing a positive, constructive and challenging feedback. I believe it itself gave a good morale boost and was a good introduction to the main part of the program. speed dating retro After running the retro session I asked guys to stay a little longer and I brought the gift bags. I made a short speech in which I revealed the actual reason for getting the feedback during one-on-ones. Wishing guys all the best, I gave them their gifts. They were surprised and charmed. I believe it brought some smile and appreciation just before the long break of work and, as far as I can judge, it helped boosting morale. Conclusion Some standardized performance review programs with their complex forms and incomprehensible questions tend to lose the individual touch with the human being and don’t provide clear benefits for the employees. We can easily forget that people and their relationship between each other are the greatest assets. We as leaders and/or managers need to do everything to embrace their relationships and make sure that they feel appreciated and treated individually. With good employees (I hope you all have them) it will pay off for all – them, you, your clients and the company. I’m sure it will.

Superheroes’ performance review

The IT industry is booming, and there is a high demand for skilled engineers and developers - especially in fields like DevOps, where a wide range of knowledge and vision are essential. Vasilii Angapov, one of the most renowned DevOps engineers and a respected expert in containerization and cloud technologies, shared his insights on the current trends in the industry and how to become a truly exceptional professional. Vasilii Angapov is the Senior Architect at an international IT consulting firm based in the United States. He holds the rare and highly valuable Red Hat Certified Architect certification, along with top-tier professional certifications from Amazon, Google, Microsoft, IBM, and the Linux Foundation. Q: Vasilii, you have more than 10 years of experience and unique expertise as a DevOps engineer. You started your DevOps career in the CIS, working for the consulting company GDC. What kind of projects did you work on there? A: At GDC, one of my main roles was as a system administrator and lead UNIX engineer for Mazda Logistics, which has an office in Brussels. I had to maintain a large infrastructure based on the VMware vSphere virtualization platform. I was directly responsible for more than 200 servers in several data centers, while the company had over a thousand machines running RHEL, SLES, and HP-UX operating systems. I also had to make architectural decisions and implement new software solutions, often manually. It was challenging and demanding work, sometimes requiring night shifts. I managed to simplify these processes significantly for the company by automating infrastructure management using the Puppet configuration management system. Another major project I was involved in was building an internal private cloud for Mazda Logistics based on the OpenStack platform. The company wanted to speed up application development and delivery by adopting new private cloud technologies. While deploying the private cloud with my team, I not only learned how OpenStack works, but also developed some extra features, such as Neutron SSL VPN, Neutron port-forwarding, and Ceph dashboard. These credentials enhance my marketability and drive my ongoing growth, pushing me to stay updated in the ever-evolving tech landscape. Also, I take pride in being one of only fifteen Red Hat Certified Architects in CIS countries. Q: There are few specialists in the market who have extensive knowledge of OpenStack and experience with large cloud systems based on it. How did this rare expertise affect your future career in DevOps? A: My knowledge of OpenStack was the key factor that enabled me to join Wargaming, a legendary video game developer based in Cyprus. Wargaming is the company behind the MMO game World of Tanks, which has an audience of more than 160 million people worldwide. They also needed a private cloud on OpenStack - and there were hardly any specialists with relevant experience in the market. OpenStack has a high learning curve: it is not easy to understand and master the principles of this architecture, let alone study it in depth. That is why my OpenStack certificates from Red Hat impressed the company at that time, and they entrusted me with the project. It was an ambitious project: they wanted to set up a cloud from scratch for 5,000 servers across the world, from the United States and Europe to Asia and Australia. I completed it in two years and migrated more than two-thirds of Wargaming's web services to OpenStack.The company gained a significant performance boost: one of the key product metrics, Time to Market (TTM), improved by ten times. This is the time it takes for a company's product to go from the stage of code writing to its end user in a finished form. Before I joined the company, TTM was measured in weeks and months, but after that, it was measured in hours, sometimes minutes. That is why I can proudly say that I brought unique value to the company and trained a team of 15 people as well. Q: You are not only an expert in OpenStack - you also have strong skills in virtualization and software containerization systems.  Where and how did you use this knowledge? How did it benefit the business? A: A significant stage in my career was working with Bell Integrator, a large international consulting company. Through them, I worked with the American office of Juniper Networks, which is the second largest company in the world in terms of network equipment sales, i.e., a major part of the Internet runs on Juniper Networks equipment. There I was developing software for the NFV (Network Functions Virtualization) and SDN (Software Defined Network) project - writing code in Python. NFV makes network hardware more efficient: instead of physical devices, services are delivered using virtual network functions (VNFs) that run on standard servers. Virtual resources can be allocated to users on demand, utilizing the potential of devices 100% - as a result, the company saves millions of dollars on buying network equipment, and consequently, the end user gets a cheaper service. Besides that, during my time with Juniper Networks, I learned about containerization technology. As part of my work at Bell Integrator, I also participated in creating a unique project called Neuton - a machine learning model that could make predictions based on statistical data loaded into it. At that time, the Neuton model significantly surpassed existing analogs in terms of prediction accuracy and speed. I deployed the model in public cloud services - partly in AWS, and partly in Google Cloud. Eventually, Google Corporation became interested in our model. At their request, we took part in architectural planning with the Google team. They also gave us a big discount for computing resources in their cloud. As a result, the Neuton ML platform completely migrated to the Google Cloud platform. Moving from AWS to Google was an interesting experience that allowed me to enhance my knowledge of these environments. Another aspect of the experience I gained from this project was building and testing fault-tolerant systems. My task was to make sure that the infrastructure on which Neuton was running would automatically recover within an hour if an entire region of a cloud provider went down. It was a big challenge, the infrastructure was extensive - with many virtual machines, clusters, and databases - but I managed to set it all up so that it could resume quickly after any disaster without human intervention. In the process, I gained invaluable experience in stress testing, load testing, and identifying bottlenecks in distributed systems. In the end, through careful Disaster Recovery scenarios and stress testing, my team and I were able to create a system that was not just fault-tolerant, but disaster-resistant. I also automated as many processes as possible, and the system I built essentially worked on its own. Q: What international projects are you currently working on? A: Currently, I am working as a Senior Architect for Li9, a company that provides DevOps and cloud solutions. As part of my collaboration with them, I worked with Veeva Systems, a company that creates software for the US healthcare industry. There I implemented a fault-tolerant development platform called Gitlab. My goal was to create a system that could withstand, for example, the failure of an entire region. My work significantly improved the RTO (Recovery Time Objective), which is the time it takes for a system to recover from a failure. Before I joined the project, it was about 6-10 hours, because the system had to be repaired manually in case of an emergency. The introduction of the new architecture reduced the RTO to 10 minutes - the system was restored automatically. Another indicator that shows the importance of the quality work of the architect is RPO (Recovery Point Objective), which measures how much data is lost in case of system failure and recovery. For example, in Veeva Systems, all the data for the previous half an hour of operation was lost in a breakdown. With the new system, all the data was preserved, which means that the RPO was reduced to almost zero. I also worked on a project for Expeditors, an American logistics company that is on the Fortune 500 list. They used ManageIQ, an infrastructure management platform that allowed developers to request resources and managers to estimate costs and authorizations. I was part of the team that successfully added a multi-stage approval system to it and optimized the system. Today, I have been working at a large US company Thryv Holdings Inc. for more than two years as a Senior DevOps Engineer. At Thryv, for example, I have implemented several production Kubernetes clusters in different data centers and AWS/Azure cloud services - now the company's applications are hosted in several regions. Plus, I optimized the AWS and Azure accounts themselves, saving the company hundreds of thousands of dollars a year. Millions of dollars more were saved when I migrated the company's enterprise task scheduling system from IBM Tivoli Workload Scheduler (TWS), a proprietary service, to Argo Workflows, an open-source solution. Additionally, I automated routine tasks using Ansible AWX, saving Thryv engineers valuable time. Q: You've often had to build unique systems and set up complex processes for which standard methods are suitable. Can you give us some examples of when you had to improvise and invent something new? A: At Thryv, I had the opportunity to create a unique solution: to containerize legacy applications written in one of the first programming languages - COBOL. It started to be actively used back in 1968, and since then, it has been used in 90% of financial transactions worldwide. Of course, by now this language is super outdated, and almost all the specialists who have ever worked with it retired long ago. We faced a difficult assignment. It would seem that such an old programming language as COBOL was hardly compatible with modern approaches to systems design in general and containerization technology in particular. Nevertheless, my team and I managed to get rid of outdated and extremely resource-intensive mainframes and to carry out a complete optimization. As a result, the client company saved millions of dollars with our contribution. Another illustrative example of when I had to apply non-standard approaches was in the process of customizing the functionality of the OpenStack platform. I had to enhance it in order to align the system with the needs of Wargaming. I was involved in developing various plugins, for example, I created a VPN-as-a-service plugin for OpenStack from scratch. I also made a number of modifications to the OpenStack code written in Python. I have also automated OpenStack deployment as much as possible. Previously, deploying this system was a huge headache: there was no single universally accepted way to install it, and the process turned into an agonizing quest in which every mistake was costly. However, I automated the installation and adapted the process even for people who don't know the system thoroughly. Q: How is the DevOps engineer field changing lately? What trends should colleagues keep an eye on in the coming year? A: The first and most obvious trend is the growing demand for MLOps (Machine Learning Operations) engineers. Machine learning technologies have been around for a while, but they have recently become more advanced and widely used, especially with the advent of ChatGPT technology, ML received an impetus to the popularity effect. The challenge for MLOps engineers is to automate the deployment of machine learning models on various computing platforms. This requires a deep understanding of both machine learning and DevOps, as there are not many established best practices or guidelines in this domain so far. The second trend is containerization. Containers have become the standard enterprise way of delivering applications, replacing virtual machines in most cases. Therefore, Kubernetes and containerization skills are essential for DevOps engineers. And the third trend is cloud platforms. Cloud platforms have been popular for a long time, as they offer many benefits over owning and managing servers. They allow the transformation of long-term capital expenditures to day-to-day operational expenses which is essential for startup projects. Public cloud platforms such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure provide a huge number of managed services in different areas, including machine learning and containers. DevOps engineers need to be familiar with these services and how to use them effectively. Q: Becoming a successful DevOps engineer, Is it necessary to get a university education for this? What skills should a beginner learn right now? A: A university degree is not a requirement for becoming a DevOps engineer. However, the university can help you develop some soft skills that are useful in any career. For example, in the university, you can learn how to learn. So in general, I don't think higher education is worthless, but you can also pursue a career in IT without it. Instead, you should take specialized courses and keep learning new things. I do this myself every day. No one knows everything in this field: I still learn as much as I did when I started. When you stop learning, you stop being an IT person. You can't afford to be complacent, because the IT industry is evolving so fast, and you have to keep up with the latest trends. Otherwise, you might fall behind and miss the opportunities that are ahead of you. And one of the key points is troubleshooting skills. You have to find the root cause of the problem and solve it. The ability and willingness to dig deep and understand is crucial.

"DevOps Engineers Boost the Efficiency and Profitability of High-Tech Business" - Vasilii Angapov

Requests are undeniably unmissable for anyone willing to start in the Python ecosystem. Yet, the community is broadly unaware of how sad the situation has become. The Incident Due to dramatic circumstances, it was partially locked and put in "maintenance mode" only, meaning it would no longer evolve (features speaking). Some of you may have heard that more than six years ago, Requests was working toward a major version, namely the V3. There was even r with a substantial amount, around $30k. a completed fundraise At the time, there was a plan to bring Async and HTTP/2 support (most notably). You'll guess that none of the exposed promises ever landed. Impacts This incident led to several impacts. Maintainers felt too much pressure to make steps toward a major; some of them said: . "We just can't deliver the promises, and it's going hard to explain why" All the back pressure landed at urllib3. Abruptly started to refuse any changes, saying: "Requests is feature complete, and it's popularity makes it nearly impossible to fix bugs that may be considered feature to this point." Newer projects started ditching HTTP client Requests by learning to use a new one. Some large-scale projects bound to Requests did not migrate because of the potentially high cost of doing it. Let's be clear, this article is not about saying "Requests maintainers are bad" but rather saying we don't agree, and everyone is entitled to their choices. For many years now, Requests have been frozen. Being left in a vegetative state and not evolving, from using more advanced features. this blocked millions of developers The Journey Into Waking Up Initially, like many other reasonable people, I said to myself "Let's wait, Requests will eventually unblock the situation!" Yet, the years are still passing, and nothing. Let's realize something, Yet, Requests is unable to do so. Internet Explorer 11 just celebrated its tenth anniversary last year, and you’ll never guess that it has support for HTTP/2 integrated! I said to myself: "Why are we waiting for this indefinitely? They don't owe us anything! There's a reasonable chance we are waiting... trapped in a circular reference… Let's act." Then, I tried to get a firm grip on the urllib3 base code, contributing and until I was ready to kick things up with that would have put urllib3 far ahead. Without any breaking changes. I was delusional. This was a bit of a shock, but six months passed between my initial kick-off and , and here's why in a nutshell: this there a proof of concept my formal give-up Every bit of idea brought to the table was dismissed, and never seriously analyzed. This felt as good as taking the coldest wind on your face while exiting your warm home. It's the hard truth of the OSS; it's not a democracy, and it's not ruled by pure logic, but by humans. Not far later on, urllib3 called out to help with HTTP/2 support (only) and claimed around $40k to be able to deliver. They probably meant well, but[...] a fundraiser This was the last straw for me, knowing the past mistakes, and actually the best incentive to break the cycle. Everything points to another year before having the slightest sign of a into our production pipes. They clearly lack the time to move forward (or incentives? or both?). No worries, as developers, we should share this burden for the greater good. PRI * HTTP/2.0\r\n\r\nSM\r\n\r\n From my perspective (my opinion only), the OSS ecosystem should not fundraise money for a decade-old protocol, and most of the time should deliver prior to asking. Entities raising money with no solid proof of concept; that's rare! This felt like a bit of a hostage situation, but it could only be me seeing it this way. The Solution So many years and various expertise have been brought to Requests and urllib3… Do we throw all of it into the garbage disposal? Engineers should be reasonable scientists and not revisionists. We don't have to reinvent the wheel all over again; HTTP client Requests are established and really pleasant in their usage. We believe that Requests has the most inclusive and developer-friendly interfaces. So, how to propose an attractive continuity for Requests? How hard could this be to (i) bring a true upgrade experience and (ii) keep the compatibility as high as possible? Sometimes, the OSS ecosystem acts like it is trying to land a rover on the Moon, let's be more realistic and bring our vision down to firm ground. There's a chance offered to us to rehabilitate Requests (and urllib3) and give a fresh breath of air to us, the developers. Without having to learn something as basic as handling an HTTP client. So, the work started, without looking back. Just around my intent to give up on urllib3 evolution, we proposed a fork of both urllib3 and Requests, namely and . urllib3-future Niquests They are (truly) drop-in replacements, at the time of writing, those forks are six months old! And the whole work around this is roughly a year old. We listened to what the community needed by... triaging all the issues on major repositories (Requests, urllib3, and dependants of them) so that the upgrade would be truly useful. Are you seeing it coming? Great! against ! Let's look at the feature table comparison requests, httpx, and aiohttp Feature niquests requests httpx aiohttp HTTP/1.1 ✅ ✅ ✅ ✅ HTTP/2 ✅ ❌ ✅ ❌ HTTP/3 over QUIC ✅ ❌ ❌ ❌ Synchronous ✅ ✅ ✅ ❌ Asynchronous ✅ ❌ ✅ ✅ Thread Safe ✅ ✅ ❌ N/A Task Safe ✅ N/A ✅ ✅ OS Trust Store ✅ ❌ ❌ ❌ Multiplexing ✅ ❌ Limited ❌ DNSSEC ✅ ❌ ❌ ❌ Customizable DNS Resolution ✅ ❌ ❌ ✅ DNS over HTTPS ✅ ❌ ❌ ❌ DNS over QUIC ✅ ❌ ❌ ❌ DNS over TLS ✅ ❌ ❌ ❌ Multiple DNS Resolver ✅ ❌ ❌ ❌ Network Fine Tuning & Inspect ✅ ❌ Limited Limited Certificate Revocation Protection ✅ ❌ ❌ ❌ Session Persistence ✅ ✅ ✅ ✅ In-memory Certificate CA & mTLS ✅ ❌ Limited Limited SOCKS 4/5 Proxies ✅ ✅ ✅ ❌ HTTP/HTTPS Proxies ✅ ✅ ✅ ✅ TLS-in-TLS Support ✅ ✅ ✅ ✅ Direct HTTP/3 Negotiation ✅ N/A N/A N/A Happy Eyeballs ✅ ❌ ❌ ✅ Package / SLSA Signed ✅ ❌ ❌ ✅ And you would think... performance..? are bad? against ! Look at the performance comparison them Fetch a thousand requests using 10 tasks or threads, each with a hundred requests using a single pool of 10 connection. Scenario: High-Level APIs Client Average Delay to Complete Notes requests 987 ms ThreadPoolExecutor. HTTP/1.1 httpx 735 ms Asyncio. HTTP/2 niquests 470 ms Asyncio. HTTP/2 Simplified APIs Client Average Delay to Complete Notes requests core 643 ms ThreadPoolExecutor. HTTP/1.1 httpx core 550 ms Asyncio. HTTP/2 aiohttp 220 ms Asyncio. HTTP/1.1 niquests core 210 ms Asyncio. HTTP/2 Did you give up on HTTP/2 due to performance concerns? Think again! open up a wide range of possibilities! Multiplexing and response laziness And everything you are witnessing is in fact "Requests"-extended, and you have nothing to learn again to get started. We kept 98% of the test cases from both Requests and urllib3, then extended them to cover newer features. We are looking forward to seeing you at the Niquests repository! Let's say it's a promotional offer, get everything and more for $̶4̶0̶k̶  $0! #1 You depend on Requests? Discover instead. Niquests Drop-in replacement, simple patch needed, tiny effort required. #2 You depend on urllib3? Discover instead. urllib3.future Drop-in replacement, no patch required, atom-like sized effort required. By visiting the GitHub repository and documentation, you will be able to learn more about this, and hopefully make a decisive switch!

The Promise That Was Made Six Years Ago for Requests 3 Is Revived

Grandier languished in prison

As the title mentions, this is a quick recap of a community taught ML Engineer's journey. Milestones and a Full time Job 15th of October, 2019 marks a special milestone, actually quite a few milestones. So I considered sharing it in the form a blogpost, on a publication that has been home to all of my posts :) The online community has been too kind to me and these blogposts have been a method for me to celebrate any achievements big or small with the amazing people that I got know via slack groups, the fast.ai forums or even Twitter as well as , unfiltered although with a hand scratching the back of my head and the amazing community was nevertheless still too kind to me, even when I had failed one of my biggest dream interviews. share my failiures So, it’d be rude, If I didn’t share that I finally got an amazing full time opportunity at one of the companies that are the leaders in the "AI" space. I'm sure the company needs no introduction, so I'll skip the praise here. As I’m writing this post, I’ll be starting, in a full time role as a at . Even though a full-time role is something that I always envisioned to work towards, it’s still hard for me to wrap my head around the fact. Also given a few other milestones that my blogposts as well as the have achieved, I’m really excited and extremely grateful to the ML Community online, which has always been very warm and welcoming to me. Machine Learning Engineer and AI Content creator, H2O.ai Chai Time Podcast I think this post will also allow me to do a quick recap of my journey, as you might know, I’ve always been open to documenting my failures and successes openly via this blog, with an aim to leave an open journey documented so that anyone who is in a similar situation as me can leverage from these posts and not repeat my mistakes. The journey comes before the milestones :) The journey In my previous post marking a few milestones, I had shared my rejection with ,but even before that my journey really started when I went to the internet to find more about interesting ideas and “latest” topics to study in college. I figured either I could complain about the University syllabus being too dated and not the most in sync with industry or I could go out and figure out what the Industry actually is, what is “Machine Learning”, what are “models”. And you can imagine the list. the Google AI Residency I took to online courses, did quite a wild number of them while still being enrolled in college. Then on 29th September. I got this email from halfway around the globe and I remember reading it at 4 AM (Yes, I wake up stupid early and then the first thing I do is read email sometimes) This has been my most dearest “Certificate” fun fact, in the “Chai Time Data Science” Studio: This is the only framed printout that exists. Although I'm not sure if it'd be visible in the RGB Fun there. This led me to another discovery cycle, because Jeremy is the best professor that I had ever come across. I ended spending a huge number of hours just “watching” the course. In retrospect, which should have spent on coding and “training lots of models” (Jeremy’s top advice from the Amazing AI Podcast by Lex Fridman) At the same time, I got the courage to visit kaggle dot com once or twice and I was convinced that I should invest into a “DL Laptop” (Since I was in college so a box wasn’t possible). and . Also, I needed quite a sum to get the laptop so I took again to the internet since I still had another year of college to go and I couldn’t do a “job-job” while being in college. That’s how my “Freelance”/Contractual work started. This in itself led to creation of a long trail of thread of discussions on the forums I finally got started with Kaggle in late 2018 Then during early new year, I got an email from Google that absolutely blew my mind. I made it to the final rounds of the AI Residencey interviews and then got rejected. By the time the letter had come through, I had ticked off a few of my “2019 resolutions”. Becoming a Kaggle Expert Creating a good Kaggle Kernel Writing 1 blogpost per week :) Implementing a few papers. So I decided to spend some time in the community, with no end goals in mind. I really wanted to give back to the community and since I had graduated from college, I had the time to talk to many amazing people and offer help via slack, by hosting study groups and even workshops. First “Remote Job”, Community Activities By the end of April, I had an amazing job interview experience. One of the amazing people that I knew via their community work: had posted about a role in their company. So I pinged them and had the coolest interview experience. A 45 minute video call, rather a friendly discussion about the responsibilities and my long term goals. The next call was the on-boarding one. Aakash N S Wait, let that sink in for a second. That’s what I mean when I mention that fast.ai community is really amazing. A company, founded by two fast.ai fellows, meet another fast.ai fellow in another city and after a small discussion, they work on the same team. HOW AWESOME IS THAT! The company, is titled Jovian.ml and really builds on top of a few fast.ai philosophies, it was really a natural fit but the founding team’s goals of community efforts and kaggle really echoed with me and the complete board. So the team let me work remotely and part time so that I could also split time on Kaggle and Community work. One of my really proud moments have been having contributed to one of India’s largest communities: Data Science Network and having organised one of India’s largest KaggleDaysMeetup till date. Blogging One of the most amazing aspects of fast.ai is the emphasis on blogging as a portfolio or project building exercise. The complete community and even Jeremy Howard himself really emphasise of blogging and that’s how I started blogging. At one point of time, I requested a few friends of mine: Dominic Monn and Tuatini Godard for an interview about their journey since both of them had been really helpful to me in answering my stupid questions and their journey was very inspiring to me. These interviews were very well received by the community so next I landed at an intersection: Either I could continue experimenting with technical articles, explaining concepts such as a CNN, a LSTM, Transformer, etc. or really focus on sharing these interviews because I felt this was really missing in the community. So I chose to leave creating tutorials and posts such as to people smarter and wiser than me and reached out to a huge number of “Machine Learning Heroes” that I really admired. “PyTorch basics in 4 minutes” I still don’t believe how, but this led to 25 blog interviews. Even though it was scary, since I was moving slightly away from sharing engineering and code-walkthroughs something that’d be expected of me, for the role of Data Science and ML that I was aspiring for but I still stuck to the interview series. Now that I had graduated and found more bandwidth, I decided to re-start the series but this time in a podcast: Video, Audio and Blog format. I’d like to clarify one point, I’ve never monetised these posts, neither will I ever intend to, nor did I expect the Chai Time Data Science Podcast to be a “business”, it was really a new medium for me to act as a medium (Too meta, isn’t it?) sharing these stories across 3 different formats to really allow the community all options to consume these amazing stories of “My machine learning heroes” Chai Time Data Science Podcast Again with the , I was at an interesting cross road where I chose to release the video, audio of the interviews, handling the editing (The most effort demanding task), interviews and releases. podcast Since I was really enjoying these interviews, I chose to make time by terminating a few freelance contracts/agreements and decided for a few months to give up 90% of my income and focus on making these amazing journeys more reachable. I’m really grateful to the Jovian team that let me continue the podcast while also working on “Jovian”. In fact, Aakash was also kind enough to setup a Zoom account via which the interviews happened. Without that, there wouldn’t quite literally have been the first set of interview releases or calls. I really enjoyed the process and now being able to talk to my ML Heroes for an hour or sometimes two. The podcast calls were really one of the best activities that I got to do. Even though this also made me take a trade off between Kaggle competitions and practising more coding, which I still somehow managed to squeeze in a few hours of, everyday. I think I really stuck to There will be a stage in time when your tasks become repetitive so really take it slow and understand your passion”. I’m really happy that I did that :) Tim Dettmer’s advice for fresh graduates: “You have time. Take it easy, enjoy the process, Enjoy learning. The Milestones I really want to share three milestones, a few points that I never really expected to happen: Hitting 1 Million Views across my blogposts. Yes, 1 Million! Imagine, starting an activity that an online course suggested, an online community taught you and then being able to share it with that NUMBER of people! I’m really grateful to Hackernoon where all of these interviews and all of my posts were warmly accepted and to David (CEO of Hackernoon) who helped me a lot with feedback during the initial days of my experimental posts. Instead of sharing any advice for blogging, I'll share the best one that I used. This has been my guideline for every single post. I’m also really thankful to everyone that read my posts and specially to the “Machine Learning Heroes” who kindly shared their journey with a Noob just starting his journey. 10k Podcast downloads hit 10k downloads after 1.5 months of going live! I’m not a podcaster neither a good stats person but that is a huge number for me and I’m really happy that the podcast as a medium reached a good number of audience and shared these great stories, advices. And I hope to continue maintaining the crazy release schedule that I had come with. Chai Time Data Science Fast.ai I saved the best part of my journey for the last section. :) Of all the stats that I mentioned above, this is probably the one that's I'm the most proud of, maybe even more than my Kaggle Ranks (Maybe) Dear fast.ai Team: Jeremy, Rachel and Sylvain and also the fast.ai family, (It’s an online community that I like to call a family). One of the aspects of being self taught or community taught is, my education would have been only as good as the community’s resources and advices. In my previous post, I wasn’t unfortunately in a position to really thank the fast.ai team but finally getting really a dream job, that I couldn’t even dream of (Too meta, again?) I think I’m in a position finally to give all the credit of my achievements to the fast.ai course, the library and the community. Many of the things that I learned: competing on kaggle, blogging, software engineering and deep learning, even little things such as how to read a paper, how to sift through a math equation and not get lost in Greek but figure out the code. Any of these things that I’m able to do, to an extent or maybe more, is all thanks to the exposure I got via the fast.ai experience. I’d confess that I may not have been the best student since I’m yet to complete the swift for TF lessons, even though I’m already working on a few blogposts on Swift and I’m yet to watch the V2 walkthroughs as well, so maybe I’m not the sincerest student in the fast.ai global classroom. Yet, I’m really really grateful to Jeremy Howard, Rachel Thomas, Sylvain Gugger and all of the amazing people I met via the forums. I’d also point out that things such as Kaggle are softly highlighted but heavily emphasised in the course, so it’s not a go to for all of the above points I mentioned, you might have to do your homework as well. But as they say, Nuclear energy is produced by Uranium and not the by products that happen, I think fast.ai is the uranium of this chain reaction that if you follow correctly, you might get amazing results! :) However, it wouldn’t have really been possible for me it I didn’t join the KaggleNoobs community, DSNet, ODS.AI and TWiMLAI ones as well. All of these have been an amazing learning point for me even though I didn’t contribute to as many conversations or latest paper discussions even since I had committed to the podcast release schedule. Yet, I’m really grateful that these communities and everyone from them were welcoming to me. Finally, I’d mention that when I say “online or community or self taught” (I prefer community taught out of the three words), I did follow and pursue a traditional CS Undergrad degree where all of my online learning started. But the real credit for my (Very little) knowledge of the Machine Learning world is thanks to online resources. I only engaged in my university courses where I truly enjoyed the syllabus and really skimmed the others, I’m not sure why but I did get a recognition with “IET Prize”, which as they mentioned “is provided by the universities to their most outstanding students”. My only advice to anyone in University would be really, follow your passion and don’t be afraid to ask for help, online or offline. My next role and next part in my Machine Learning Journey From the page https://www.h2o.ai/team I’m incredibly lucky and really excited to be starting a full time role as a “Machine Learning Engineer and AI Content Creator” at H2O.AI. This by no means is to classify me as an expert, the company itself is home to many amazing kagglers and world’s top data scientist and I’m really excited about the next part of my journey and ready to learn more. Thanks to the internet, fast.ai and online ML Communities for defining my path and Thanks to my parents specially who agreed to this completely crazy plan and even supported me when the podcast’s initial growth was slow or helping me get started with my first GPU. And even more my mother, who would always put up with a son who'd quite often spend a few days, in a room and consuming endless cups of chai in a high electric noise generating GPU situation. I’ll continue sharing my journey, via these blogposts at Hackernoon (And maybe Medium), unfiltered and regularly. And thanks to everyone who has been a part of this amazing journey with me. You can find me on Twitter @bhutanisanyam1 Subscribe to my Newsletter for a weekly curated list of Deep Learning and Computer Vision Reads

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