Welcome to the first installment in our series on ModelOps. This blog focuses on the importance of Data Preparation in support of your AI projects and avoiding the common pitfalls. Data is the heart of every AI investment, but data is not a monolithic concept. It comes in many different forms and sizes. For example, your data may be well structured and categorized, like the data you might find in a spreadsheet or database. Alternatively, your data could be an unstructured collection of text like newspaper articles or customer reviews. While many other types of data exist, these two categories – structured and unstructured – power the most common AI applications being developed today. It’s important to understand what it takes to gather and prepare these types of data to budget accordingly. No one should be doing AI for the sake of doing AI—it must be tied to a clear business objective. Whether the AI application is aimed at improving an internal process, developing a new consumer product, or gaining a competitive advantage, all AI applications stem from mining data. For this reason, well-conditioned data is vital to the AI development process. As a budget owner, the first question you need to ask is “How much is data acquisition and preparation going to cost?” A complete answer to this question will cover eight areas. Does the team know where to find relevant data? Is data already available to your organization? …POS data for instance? Is there a cost to acquire the data? Is the data labeled? Will the labeling be done in-house or by a third party? Where will the data be stored? How will the data be conditioned and formatted for the AI application? How will the data be delivered to the team building the AI application? The first consideration for good data preparation is the data provenance. There are a lot of good ideas for AI applications, with the caveat that the data can be acquired. Before green lighting any project, it is vital to have the team building the AI solution articulate the location of the data they are going to use so that you don’t waste money on other unnecessary expenses. The second thing you need to clarify is whether or not the planned data source is available for use by your organization. Some questions that can help determine the answer to this include: Does your organization own the data? Does your group have the internal authority to hold the data (e.g., HR data is often restricted)? Is the data open source and free? Is it open source, but available only to members of a consortium? Is the data purchasable? And most importantly is the data the team plans to use licensed for commercial use? Asking these kinds of questions puts you in a position to determine whether or not the data is a good fit for the kinds of AI applications you are considering. Assuming that you are comfortable with the source and licensing constraints of the data, the next step is to understand costs. Some widely available datasets are freely available and licensed correctly for commercial use (e.g., Spacenet ), but other data is available to a restricted members only or commercial audience (e.g., Maxar imagery, Linguistic Data Consortium data). If your team is planning on using data sources from commercial or consortium sources, the cost of the data and membership fees can be a significant percentage of what you end up spending on building the AI application. Additionally, you need to ask about the completeness of the data. Will the data be ready to use when purchased, or will it need to be augmented, annotated, or labeled? If it needs to be labeled, will the team be doing that or will it require outside help? Asking these questions early can prevent sticker shock after a project is already underway. Finally, to get a holistic view of the costs associated with data acquisition, you need to ask about the data storage and delivery mechanisms. Some data sets are simple and can easily be stored in databases on your network infrastructure, but other types of data like satellite imagery can require the kind of storage that you aren’t likely to have on hand (e.g. Petabyte level file systems) and may require cloud storage. Regardless of where and how the data is stored, someone will need to provide or build connections that makes it easy for the data science team to use for model development. Data engineers do this in large organizations, but in smaller organizations this role may very well be filled by a member of the IT department or the team. When you approve a project, you’re also approving the labor budget and the cost of data storage. data science Investing in AI can dramatically shift how your organization operates. However realizing the advantages and measuring their impact requires a proper accounting of the total project cost to estimate ROI. Data acquisition costs are often an overlooked contributor to those costs. By asking the questions upfront, and establishing a good process for data preparation, you’ll get a more complete view of data acquisition costs to make more informed decisions when authorizing AI investments.

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ModelOps Introduction Series: Data Preparation In AI

Agile software development takes a new angle on software projects. Instead of living with late projects and hoping for better estimations next time, the agile approach recognizes the specific and unique nature of software and takes a different path. In this article, I would like to explore the essence of the release management process, its anatomy, and ways of its improvement. In a nutshell: what is release management? Release management refers to planning, management, scheduling, and controlling an entire software build through every stage and environment involved, including testing and deploying software releases. The planning stage may be the most time-intensive, as this is where your entire release is structured from start to finish. One of the most popular release management methods is the systems development life cycle (SDLC). The SDLC helps software developers plan, develop, maintain, and replace software systems with a high degree of efficiency and quality. Every release plan should include: Timelines Delivery dates Requirements The overall scope of the project. With the release plan finalized, we can start designing and building the product for release. This is the actual “development” of the product based on the requirements outlined in the release plan. Once all the issues that may have arisen are addressed, it’s time to subject the build to real-world scenario testing. This could take several iterations. As the team builds out the product, it is sent to a testing environment for user acceptance. This allows the team to identify any bugs or issues that may arise in a real-world environment. The next step is user acceptance testing (UTA), which is when the end user’s product is built to actually be used and give feedback. As bugs are identified, the team goes back to the drawing board to fix the issues and redesign the build for greater integrity. The build must pass the UAT stage to be considered for final implementation and release. Release preparation also includes a final quality review by the QA team. During the review, the QA team will conduct final checks to ensure the build meets the acceptable standards and business requirements outlined in the release plan. After the review, the functional team will validate the findings and finalize the release for deployment. Before the build can deploy into a live environment, it must be approved by the product owner. Besides simply sending the build-out into production, the deployment stage also includes messaging and education on the product to both the end-user and your company at large. For instance, users should be notified of changes with the release and new features. Finally, during the deployment stage, the development team should meet to assess the release’s performance and discuss how the deployment went. If there are any lingering issues, they should be identified and documented for the team to address in the next iteration. Why do you need to adopt a release management strategy? There are a number of reasons a company implements release management strategies: The complexity is in direct correlation with functionality risks: there is a high probability a new release will disrupt something. Infrastructure is complex when many technologies are being used, which implies many versions and configurations. An enterprise's operational structure is complex. Operational infrastructure is typically a huge environment that consists of smaller shared environments. As the company grows, so too will the number of delivery teams. Therefore, it is essential to make sure there are no conflicts in delivery efforts across teams. Several delivery teams work concurrently. With the growth of the company, new people join delivery teams, and they have no experience as to operational environment solution deployment. To help keep everyone on the same page, the release management team can become coaches, provide guidance, assist in coordination and planning. Help needed for solutions release. DevOps approach is successful and demands the seamless flow between operations and delivery, hence, the efforts of the teams require support and orchestration. The necessity in streamlining operations and delivery. The perks of release management In a world where the delivery pipeline is automated, and several independent teams work on a project, there is a great need in seeing the big picture. Release management presents a holistic approach to efficient development and delivery. In contrast, the absence of this orchestration results in profligate pipelines, irrational resource management, and incorrect prioritization with the focus on features with low impact. A release manager bridges the gap between the inside and outside perspectives and makes sure every sprint has proper prioritization. The other advantages of release management are: Many companies tend to put software development speed at the center of their attention. However, the major focus must be on delivering more value to customers, as it is the ultimate goal of digital transformation. In view of this, the release management teams gather and analyze customer feedback, which gives a deep understanding of business needs. They form the foundation for an effective development plan. Value delivery to customers. Product development will always be exposed to business risks, even though DevOps significantly shortens time to market. Process audit and governance standardization are exactly what the release management teams are wired for. Scalable risk management is possible when risk management is a standardized process. Risk management at scale. In this context, we are talking about the three key metrics that grade the deployment effectiveness—more positive value, less negative value, and fast net value. In other words, deployment efficiency is defined primarily by fixes and new features, as well as the absence of bugs and delays. Release management teams are responsible for sustaining these three areas. Enhanced deployment effectiveness. Challenges of a release manager When you are working on big projects, you have to balance a lot of things. Multinational companies demand working with different cultures, mentalities, and ensuring seamless communication. Depending on a project, issues might vary. But there are a number of common challenges release managers tend to address: Heterogeneous environments might lack integration Lack of alignment between IT and business line Increased risks imposed by large updates and modifications to the application Insufficient visibility across stakeholders Regulatory compliance and governance Making sure teams followed every step to deliver exactly what the customer asked for. An effective release manager must have robust risk management techniques at hand to address these issues. Otherwise, there is a high probability of sabotaging the release management process. Structuring release management process: the methods Since we are striving for increased agility, your release management process will be better structured with the following steps: Among the pillars of agility, constantly monitoring and delivering reports is crucial. In this way, you keep your finger on the pulse at every stage and ensure nothing is missed along the way. After each and every sprint is completed, you need to juxtapose the results with the pre-defined objectives. Ensure real-time monitoring. You need to make sure there is a visible link between the business requirements and their fulfillment — being able to trace the requirement back and forth. The manual approach to this process is time and labor-intensive, and can be quite costly. In this case, automation works miracles, and the release managers can make timely data-based decisions. Trace the requirements. The absence of standardized governance leads to disruptions. For instance, imagine an engineering team that creates something manually. Obviously, the human factor is always around when something is done manually, and packages will look and work differently. Downtime is the smallest problem you might face at the end of the day. Standardize. Compelling and superb customer experience relies on the way a company manages the development and delivery processes. On the path to complete agility, you as a release manager need to keep in mind the three things: avoid sticking to the status quo, convey a clear mission, and find the “right” technology partners to provide you with custom-tailored tools. When those are settled within your company, you can start thinking of your future tech excellence in the market.

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2022 - HackerNoon Contributor of the Year - Agile Software Development

Nominated for 2022 - HackerNoon Contributor of the Year - Agile Software Development

What Is Release Management And How Can Your Team Adopt It

HackerNoon Reporter: Please tell us briefly about your  background. Growing up in the time of the Soviet Union I went to a strong math school, I was always passionate about programming. I was fascinated by innovation, research, and industrial programming; сreating smart algorithms to solve real-world problems. During my third year at the university, I went to work at an IT programming lab where I made my first steps studying ontologies and creating learning management systems. We did a project for a client that was at the forefront of innovation in Russia. As it turns out, this was the place where I met my future business partners. I realize now that we were brought together by this drive for innovation and desire to put our brains into something big and meaningful. What's your startup called? And in a sentence or two, what does it do? ENBISYS is a growing IT company and we have several owned startups and spin-offs of our own. We focus on сustom software development, machine learning research, adaptive learning systems, and medical imaging diagnostic platforms. What is the origin story? In the beginning, we were just five guys working on one project for a single client in healthcare. Then, they spread the word and another customer came in. Our clients from Europe appreciated working with talented programmers. We started building a network and gradually learned everything about leading a business in IT. What do you love about your team, and why are you the ones to solve this problem? ENBISYS team consists of the best professionals with a great attitude for their work. All of them are committed to their projects and have a good cultural fit for clients from different countries, as well as communication and soft skills. The orchestrated effort of each team member allows ENBISYS to solve the most ambitious challenges for our clients. If you weren’t building your startup, what would you be doing? I am into gaming, so if I ever consider doing something different than leading an innovative business, I will choose to join a cybersport team and compete with the most experienced gamers! At the moment, how do you measure success? What are your core metrics? First of all, it is my team that inspires me to reach the most ambitious goals. Second is my own resourcefulness and well-being that helps me to perform at my best. The third is the exponential growth of the company’s profit. What’s most exciting about your traction to date? The IT industry is changing rapidly, always offering numerous challenges and opportunities to solve. I am excited by new technologies and approaches and the leap they may make in enhancing people’s quality of life through digitalization that will inevitably take place. What technologies are you currently most excited about, and most worried about? And why? The technologies that fascinate me are Artificial Intelligence, Machine Learning, and Quantum Computing: they will help humanity reduce routine processes and enhance their quality. I am not worried about robots or AI taking over people’s jobs because creativity and innovative thinking are very difficult to automate. What drew you to get published on HackerNoon? What do you like most about our platform? Within our content strategy at ENBISYS, we always look for opportunities to share our expertise with the audience who might need it. It was a surprise for us to get listed as a nominee on Startup of the Year, so after exploring the platform we understood that we would like to contribute to your community knowledge base with our vision on digitalization, eHealth, EdTech, and other subjects of expertise. What advice would you give to the 21-year-old version of yourself? Try to check hypotheses faster, since not every hypothesis is both suitable for business and can bring value to people. What is something surprising you've learned this year that your contemporaries would benefit from knowing? Every unexpected event like lockdown or crisis is challenging on the one hand and creates opportunities on the other hand. For example, Telemedicine and EdTech apps have grown dramatically and gained popularity during lockdown times, and this was a great chance to show people the convenience of using such services. Always look for opportunities the situation may bring! ENBISYS is nominated as a . Go vote! startup of the year in Tomsk

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Startup Interview with Dmitry Bubnov, CEO Of ENBISYS

HackerNoon Reporter: Please tell us briefly about your  background. I am originally from Venezuela.  Moved to the US for college and have jumped around a bunch of industries until I was able to finally start a company. What's your startup called? And in a sentence or two, what does it do? Remoov - Remoov is enabling the circular economy by making it simple to upcycle unwanted goods, ensuring we end up with value in hand rather than at the dumpster. What is the origin story? We originally launched at Stanford to help graduate students. What do you love about your team, and why are you the ones to solve this problem? Hustlers, everyone willing to wear many hats and figure things out daily. If you weren’t building your startup, what would you be doing? Probably some corporate job. At the moment, how do you measure success? What are your core metrics? Revenue, pickup, NPS. What’s most exciting about your traction to date? Great product-market fit. What technologies are you currently most excited about, and most worried about? And why? AI, CRISPR - what it can mean for the future of medicine. What drew you to get published on HackerNoon? What do you like most about our platform? Great stories about inspiring entrepreneurs. What advice would you give to the 21-year-old version of yourself? It is going to be a rollercoaster, learn how to ride the highs and lows. What is something surprising you've learned this year that your contemporaries would benefit from knowing? It is never as good or as bad as you think. Remoov is nominated as a . Go vote! startup of the year in South San Francisco

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"Learn How To Ride The Highs And Lows" Says Luis Perez, Remoov CEO

So it’s not the Light Runner but the new Audi e-tron is still pretty sweet. With a towing capacity of 4000 pounds and a dashboard chock full of tech, the new generation electric SUVs look to be getting increasingly beastly. However, a key characteristic of the Audi EV range is that—despite their sci-fi-inspired names—the cars look exceedingly ordinary. Without the sleek body design and fancy lighting accents, some of the recently released EVs can only be distinguished from conventional cars by their inner workings. While those of us looking for a futuristic ride may find that boring, the integration of EV technology with ‘normal’ cars is a definite sign that EVs are going mainstream. Tron: Legacy The EV market has been on fire in the past year, with stocks wildly outperforming expectations. The ubiquitous Tesla Inc. hit a low of $70 in 2020 and a phenomenal high of $800 this year as trades were swayed by the random social media posts of CEO Elon Musk. are now poised for an upward ride as lesser-known players in the EV market are being swept up by the clean energy storm. With the global EV market growing 43 percent from 2019 to 2020 and populous China making a huge push for EV adoption, EV stocks are looking like the next big thing. Electric vehicle penny stocks In the Before Time Although the name Tesla is now synonymous with EVs, electric vehicles have been around a long time before Elon Musk was a twinkle in his daddy’s eye. In 1828, Hungarian inventor Anyos Jedlik built the first model electrical car. Over the decades, designers continued to experiment and, in 1884, British industrialist Thomas Parker built the first production electric car using custom-made rechargeable batteries. Ten years later, the cool-sounding Electrobat was unveiled in Pennsylvania by electrochemical engineer Pedro Salom and inventor Henry G. Morris. (Spoiler: It looked nothing like a bat.) Interest in the electric car continued to rise in industrialized countries, with electric battery-powered London cabs being introduced in 1897. Sadly, a combination of poor electrical infrastructure and the discovery of large deposits of petroleum and crude oil in the early 1900s led to the premature demise of the industry. EVs were shelved for a good long time until General Motors tried (somewhat fruitlessly) to revive the market with their EV1 in 1996. In 2008, Tesla Motors produced the Roadster, the first relatively successful EV and the first electric car to use a lithium-ion battery. Since then, the EV market has been expanding exponentially. Captain Planet, He’s a Hero The popularization of EVs goes hand-in-hand with the impetus to protect the environment. The 1970 Clean Air Act set the scene for the eventual phasing out of fossil fuel combustion engines and international treaties such as the Paris Agreement are pushing countries to take measures to severely reduce their emissions. The Clean Energy Ministerial (CEM), a high-level global forum, launched the Electric Vehicles Initiative (EVI), a program designed to accelerate the conversion of the global vehicle fleet from gas to electric. For those of us who do not yet want to perish in a great ball of fire, owning an EV has the side perk of taking care of the climate. Studies have shown that EVs produce 34 percent than hybrid vehicles and an impressive 68 percent less than conventional cars. While the production of lithium-ion batteries remains the most criticized component of EVs, the entire life cycle of the EV (including disposal and recycling) still fares better for the environment than a gas car. Leading manufacturers like Tesla have also committed to producing a scalable recycling system for used batteries. less emissions Shut Up and Take My Money As a party to both the Paris Agreement and the CEM, the US is offering unprecedented financial benefits for EV buyers. Federal tax credit is available for plug-in EVs and over half of the states in the country provide rebates, exemption from fuel emission testing, free parking, and more for those who purchase EVs. For example, California is leading the way in incentivizing EV purchases by dishing out $1,500 discounts as part of the California Clean Fuel Reward. Of course, savings from EVs are not only at the point of sale. Electricity costs a lot less than gas and, according to eGallon, an EV costs only half as much to operate as a conventional vehicle. For car owners who produce their own electricity through solar power, the running costs for an EV are practically negligible. Add to this the fact that EVs have a more simplified internal setup and require , and EV owners are looking at an average of $330 in annual maintenance savings. less maintenance From the Audi e-tron to the Rimac Concept One and the Ford Mustang Mach-E, the EVs entering the market are getting sexier and more varied. Whether people are into EVs for energy efficiency, reduced emissions, cold hard dollar savings, or the thrill of owning the latest in automobile technology, the attraction is undeniable. It is not hard to imagine a distant future where EVs make up a solid majority of the global vehicle fleet. If vehicle designers could just add more neon lights, everything would be absolutely perfect.

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Electric Vehicles Are Getting Mainstream, Whether You Like It Or Not

opened , and we some preliminary data to report on. For reference, nominations close Sep 6, 2021 and voting ends on January 26, 2022. Startup of the Year Voting on July 26 Trending Startup Locations Based on Total City Vote Counts: 2,067 Votes on Singapore Startups 1,534 Votes on Los Angeles Startups 1,523 Votes on Redwood City Startups 1,170 Votes on Chicago Startups 882 Votes on San Francisco Startups 709 Votes on Lagos Startups 552 Votes on San Luis Obispo 391 Votes on Kyiv Startups 365 Votes on South San Francisco Startups 322 Votes on London Startups 317 Votes on Portland Startups 312 Votes on New York City Startups 311 Votes on Zug Startups 284 Votes on Mexico City Startups 268 Votes on Mississauga Startups 271 Votes on Baltimore Startups 257 Votes on Boston Startups 227 Votes on Sydney Startups 225 Votes on Pune Startups 213 Votes of Almaty Startups 208 Votes on Ontario Startups 208 Votes on Toronto Startups 194 Votes on MelBourne Startups 151 Votes on Indianapolis Startups 155 Votes on Calgary Startups 144 Votes on Tel Aviv Startups 141 Votes on Columbus Startups 120 Votes on Somerset Startups 118 Votes on Berkeley Startups 112 Votes on Colorado Springs Startups 107 Votes on Palo Alto Startups 104 Votes on Berlin Startups 103 Votes on Ankara Startups 101 Votes on Tomsk Startups Note, there is some bias based on , and please check out about our . HackerNoon’s readership locations the full startup voting faq for any details Startup of the Year Voting

HackerNoon Tech Stories Archive August 16th, 2021

ModelOps Introduction Series: Data Preparation In AI

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Modzy

What Is Release Management And How Can Your Team Adopt It

@anastasiiayamkova
Anastasiia Yamkova

Startup Interview with Dmitry Bubnov, CEO Of ENBISYS

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krasseve

"Learn How To Ride The Highs And Lows" Says Luis Perez, Remoov CEO

@remoov
remoov

Electric Vehicles Are Getting Mainstream, Whether You Like It Or Not

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Michael

34 Trending Startup Locations Based on 2021 Tech Community Polling

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David Smooke

HackerNoon Tech Stories Archive August 16th, 2021

ModelOps Introduction Series: Data Preparation In AI

@modzyModzy

What Is Release Management And How Can Your Team Adopt It

@anastasiiayamkovaAnastasiia Yamkova

Startup Interview with Dmitry Bubnov, CEO Of ENBISYS

@krassevekrasseve

"Learn How To Ride The Highs And Lows" Says Luis Perez, Remoov CEO

@remoovremoov

Electric Vehicles Are Getting Mainstream, Whether You Like It Or Not

@truepublicityMichael

34 Trending Startup Locations Based on 2021 Tech Community Polling

@modzy
Modzy

@anastasiiayamkova
Anastasiia Yamkova

@krasseve
krasseve

@remoov
remoov

@truepublicity
Michael