We digitalize and automatize industrial companies to prevent and mitigate workplace accidents and achieve efficient processes. We developed the first Digital PPE, an IoT solution that helps industries guarantee their team's safety and boost operational excellence.

Drixit Technologies

Welcome to Hacker Noon Interview Series! See all other Interviews . Good-Company here Please tell us briefly about your background. I’m Tanay Dixit, Co-Founder & CPO of . Previously I was involved in product development across deep learning, computer vision, web, and mobile for over eight years. Wobot.ai At the age of 19, I co-founded Senfinance while preparing for CFA examinations, which got me involved in the world of technology and led me towards the journey in entrepreneurship and product development. I then co-founded a development agency called WEMO with the intent to help brand managers with deep learning technology-based marketing solutions. Later, for several years, I was involved with deep learning and computer vision in fashion, building one of the most innovative products in the retail space, Talespin. Currently, I'm building , which we established in 2017, and I oversee product development, where I augment deep learning and computer vision for various sectors with the intent of commercializing deep tech at scale. Wobot.ai What's your company called? And in a sentence or two, what does it do? The company is called , and we are a Video Intelligence Platform that enables businesses across food services, manufacturing, retail, hospitality, and others to do more with their existing camera systems. We help go beyond simple security and improve operations and gather insights for operators. Wobot.ai What is the origin story? Our story dates back to 2015 when the co-founders were working on different pieces to a puzzle. Back then, we were running our own startups. Few of us were applying computer vision to fashion, and the other had built a smart audit and inspection platform to help operators digitize their checklist on an app. In 2016 while working with customers across different sectors through the inspection app, we realized that there was a need for an autonomous validation for operational tasks as well as the possibilities of computer vision. At that time, we also learned that 100’s of millions of cameras were mostly being used for post mortem analysis and that too for security purposes only. Later that year, we had a ‘what if’ moment and imagined a feedback process for operations to happen through these existing cameras. We believed that such a platform would make not only such feedback pre-emptive and non-biased but also continuous as well as actionable. So, in 2017, we started converting food-related operational checklists to computer vision models that could run on existing CCTV cameras. Soon enough, we realized that this could work across industries and checklists. That was it for us to accelerate our journey to today. What do you love about your team? Passion, commitment, out-of-box thinking, and our nimbleness. If you weren’t working at your company, what would you be doing? Somehow would have stumbled later on to this opportunity. In the meantime, I would be building another startup. At the moment, how do you measure success? What are your core metrics? A lot of our success metrics are driven by customer-driven index as well as team growth. What’s most exciting about your company traction to date? We continue to add intelligence in many cameras across different sectors and use cases. What technologies are you currently most excited about, and most worried about? And why? The future of computer vision is bright, and we are still scratching the surface with deep learning and AI. What drew you to get published on HackerNoon? What do you like most about our platform? Great place to be in as the audience is technologists. What advice would you give to the 21-year-old version of yourself? Focus and concentrate on the small tasks that lead to the big ones. What is something surprising you've learned this year that your contemporaries would benefit from knowing? While the bigger picture is important, it is essential to plan and execute the smaller steps.

Enabling Business Operations With Computer Vision: Interview With Tanay Dixit, CPO of Wobot.ai

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A Quality Assurance (QA) Engineer is a professional who tests software during the entire development process. Before distributing the product to the general public, they validate that it satisfies the desired quality criteria. If you are looking to become a quality assurance engineer, then this blog is for you. In this blog, we will cover everything from the quality assurance process to the responsibilities of a quality assurance engineer and the other nitty-gritties that you should be aware of. Who exactly is a quality assurance engineer? Every stage of the development process, comprising design, development, testing, debugging, and delivery, is monitored by a quality assurance engineer. They assure that the software meets the necessary quality standards. A QA engineer also constructs the tests to make sure that the product is free of technical errors. They analyse data, find flaws, and document results in an office or a lab setting in front of a system. Why is quality assurance engineering a great career choice? As a QA engineer, you mostly focus on the development of business software. You have the ability to use coding and other tools to increase the quality of a product. It's a meticulous profession that necessitates both technical and soft skills. A QA engineer's job requires you to study user behaviour and build products that are simple to use. It also necessitates keeping up with technological advancements, new tools, and laws. A QA engineer job may be right for you if you want to work in a challenging, technologically oriented work environment where quality is prioritised while meeting tight deadlines. The key responsibilities of a QA engineer In most cases, a QA engineer works mostly in an office. They spend most of their time looking for faults in the programme, offering fixes, and making sure it meets the required standards. They create test cases to verify the software and keep track of the outcomes. The QA engineer's job description varies based on the sector and the project they're working on. A QA engineer is likely to execute the following tasks on a regular basis: Creating guidelines for developers to follow, including manual and automated test scripts. Creating and running automated tests. Consulting with stakeholders to better understand and describe software needs. Analyzing test findings to predict user behaviour, find flaws, and provide remedies to minimise issues. Keeping track of performance indicators by working closely with the product testing and development teams to guarantee that the project is delivered on time. Checking to see if the finished product fulfils the specifications. Analyzing product features and giving recommendations to improve them so that they are more productive and user friendly. Keeping up with the newest innovations, standards, and regulations in the business. Skill sets expected in a Quality Assurance Engineer A QA engineer's job requires an array of skill sets. A QA engineer often has excellent managerial skills in addition to a thorough understanding of quality assurance procedures and technologies. The following are some of the skills that are required of them: Robust Interpersonal Skills A quality assurance engineer serves as a point of contact for management, clients, and developers. They engage with team members and clients to comprehend their needs and to keep them up to date on the project's development. Before moving on to the design and creation stages, they have a good understanding of the software's objective. Hence, a QA engineer must have great listening skills and the ability to quickly understand information. A QA engineer also collaborates closely with development teams to organize development, monitor testing, make modifications, fix errors, and keep the team on schedule. They also analyse the data and report it to their boss. As a result, a QA engineer must be able to effectively clarify their thoughts and communicate them to the team, either in writing or verbally. Punctuality A QA engineer is responsible for meeting rigorous timelines. They plan the roadmap for their group. They are a necessity for the team to provide new software on the predefined timeline and in congruence with quality standards. They frequently oversee other activities in addition to development and testing, such as preparing reports or writing automated testing scripts. A QA engineer with good punctuality can thrive on the grounds of workload. Detail-Orientation To produce a high-quality product, the QA engineer designs multiple use cases, writes test scripts and detects defects early on when designing software. This requires technical skills as well as the ability to note minute issues that could put the software in peril. They are detail-oriented as a result of the meticulous recording of the procedure and test findings. Strategic Thinking and Analytical Abilities The QA engineer uncovers flaws and vulnerabilities that may impair software performance during development. They then make recommendations for refinements and keep tabs on the debugging process. To test the product and uncover flaws early on, a QA engineer creates several use cases. QA handles numerous concerns that any stakeholder may raise during the development or testing phase. As a result, the capacity to address and tackle issues is crucial to the QA engineer's expertise. Healthy Collaboration A QA engineer is usually in charge of a group of testers and developers. They also collaborate with senior authorities and clients to appropriately define the solution. A quality assurance engineer defines deadlines and ensures that their team meets them. They work on projects with a large number of individuals, so the capacity to keep the team together and expand is critical. Technical Abilities A QA engineer generally has a thorough understanding of multiple technologies in order to effectively find faults, recommend modifications, and address difficulties. They also update themselves with the latest changes and quality assurance processes. QA engineers gain a variety of hard skills, including: Understand functional and smoke testing, as well as performance, load, security, API testing, and end-to-end testing. Learn about websites, software development, testing, and programming languages. Working knowledge of several quality assurance testing tools. Using specialised software platforms to create automated tests. Using project management tools, create agile workflows. Know how to use quality assurance management software The Step-by-Step Guide to Becoming a Quality Assurance Engineer Becoming a quality assurance engineer requires you to follow a step-by-step guide. These steps encompass: Get a Relevant Degree A bachelor's degree in software engineering or relevant computer science is needed to become a quality assurance engineer. These courses will help you develop the essential skills you'll need as a QA engineer. It gives you extensive experience with software development, testing, and several software platforms and technical tools. Apply for Internships Internships can assist you in gaining real-time experience in the disciplines of development, testing, and quality assurance. This experience in the corporate world can aid in the development of your resume. Internships also allow you to learn about different testing approaches, new technologies, and industry standards. It also aids in the formation of a network of colleagues and mentors. Get Accreditation Certifications give your CV more weight. The International Software Testing Qualifications Board (ISTQB) has certification for three levels. These certifications can be earned at any time during your employment, even after you have gained some experience. Work on a Portfolio You can demonstrate your professional projects with an eloquent portfolio. Describe how you contributed to each project and what role you played in it. You can include your accomplishments to showcase your soft skills. Having a portfolio with your resume gives the company a better picture of your work. Prepare for Interviews A QA engineer's job is complicated. Employers may therefore put your technological talents to the test during the interview process. The employer may give you a task or ask you questions to assess your abilities. In either scenario, it's critical to brush up on your fundamentals and get ready for the interview. Apply for QA Jobs You can apply for positions as a quality assurance engineer in multiple ways. One option is to take advantage of your existing network. Inform your friends and colleagues that you're looking for employment. They may be able to give you leads or make recommendations for organizations where you can apply. You can also use a job search website. These portals can give you access to a huge number of companies across the globe. The Pay Scale of a QA Engineer The is usually decided by the company's size and location. salary of a quality assurance engineer Also, based on the organization and the amount of experience, this number can be significantly higher or lower. Summary We hope that this blog has provided the necessary insights you need to know about becoming a quality assurance engineer. Software quality assurance engineers are software testers who evaluate a software product's efficiency and accuracy. As a software quality assurance engineer, he or she is in charge of a wide range of technical and non-technical activities. Coding, test case preparation, testing, and requirement collection are all technical duties. Non-technical tasks, on the other hand, include collaborating with team members, monitoring the workforce, and confirming the final deployment of software products.

How to Become a Quality Assurance Engineer

Data science is an emerging field in the IT industry. On the other hand, Software engineering is already well-established. Computer science graduates are usually aware of what software engineers are but have a vague knowledge of what data scientists do. Both these roles have their own importance in their respective teams. In this blog, we will discuss the differences between the two concerning skills, salary, career path, and methodologies. The goal of the blog is to better understand these positions and how they collaborate. The terms data science and big data are slowly occupying the computer science world, the data scientist is emerging as a new career option. On the other hand, software engineering is an older discipline that has already made its perfect place in the IT world. These both are popular professions. The graduates can opt for any of the roles based on their interests. But for choosing the one, you must have the proper knowledge to differentiate between the two to make the right choice. The future in both roles is bright. What is Data Science? Hugo Browne-Anderson, a renowned data science evangelist, once said that "If you wanna do data science, learn how it is a technical, cultural, economic, and social discipline that has the ability to consolidate and rearrange societal power structure." Data science is the process of extracting knowledge and insights from data by using scientific methods. It uses various processes and methods to study different kinds of data - structured, unstructured, and semi-structured data. Data Science is now affecting our day-to-day activities. Some of the technologies to analyze data are data purging, data transformation, and data mining. The main goal of a data scientist is to optimize the algorithms and maintain a balance between speed and accuracy. They integrate with experts and work together to come up with an optimized solution. What is Software Engineering? The term software engineering is made from two words, software, which is the set of integrated programs, and engineering, which is applying theoretical and practical knowledge to design, build, test, and deploy the application. Software engineering is a branch of engineering that involves developing software products using well-defined principles, procedures, and methods. The outcome of this process is an efficient and reliable software product. The processes involved in delivering a software product are understanding customer needs, analyzing requirements, creating a design, coding, testing, software deployment, and maintenance. Data Scientist vs Software Engineer: Skills Data scientist skills Data scientists are highly qualified individuals; they are familiar with the dos and don'ts in an entire machine learning lifecycle. The more advanced your position, the more advanced set of skills are required. But as a beginner, you need to have a specific set of skills to get eligible for this role in companies. Let's have a look at the skills: A good data scientist should have a solid basic knowledge of both subjects. There are steps in data science that require statistical knowledge to make decisions, analyze and solve the problem. Additionally, linear algebra and calculus are also essential skills required. Programming: The two most common languages used in data science are Python and R, and a data scientist needs to have a firm grip on one of the languages. This is a crucial skill for any data scientist. It is a way to communicate with your data and get valuable insights that help develop some business solutions. It becomes easy to analyze complex data by breaking it down into small segments. For a data scientist, ML is an essential skill to have. It is used to develop predictive models. There are the following types of learning: supervised, unsupervised, semi-supervised, and reinforcement learning. Applying the appropriate learning type gives you quality predictions and estimations. Data manipulation skills help clean and transform the data for better analysis in the following stages. Data analytics skills help data scientists understand their data in more depth and gain valuable insights that can help come up with the solution. Problem-solving skills: Data science problems are usually hard at the technical level. It needs a lot of research and knowledge to get good accuracy. So, it is important for a data scientist to have good problem-solving skills. Math and statistics: Data Visualization: Machine Learning: Data Manipulation and analysis: These were some of the important skills to begin with the data science projects. Other skills are big data, software engineering, model deployment and good communication. Software engineer skills The software engineer skill set does not include the level of education needed for a data scientist. Some of the skills included don't require much effort to learn. Let's have a look: This is a core skill for any software engineer. They should have a firm grip on at least one programming language, but learning more is also better. Some of the popular languages are Java, C++, Python, and PHP. It is recommended to create projects in the respective languages to build a strong base. Software engineers cannot avoid working on Linux because you can find it at many stages. When you deploy any application to a server, it is most likely running a Linux OS. This is another most essential skill. Databases like MySQL, Oracle, MongoDB are widely used. Software engineers create various applications, so some information may need to be stored in the database. So, to interact with it, this skill is a mandate. Concepts like joins and normalization are essential for learning. This skill is the topmost priority by most companies to check problem-solving and coding skills. You can become a good software developer if you know about organizing data and using it to solve real-world problems. Software engineers spend more time debugging the errors than development, so it is important to have good problem-solving skills. This skill distinguishes great software engineers from good software engineers. The development of any software will require an SDLC model. It helps in analyzing and understanding the customer's requirements. Further steps include designing the solution, developing the application, performing testing, and deploying. Programming language: Linux: Database: Data structures and algorithms: Problem-solving skills: Software Development Lifecycle (SDLC): Data Scientist vs Software Engineer: Methodologies Every role requires a methodology that is followed by the individuals/team to give the results. Data scientists and Software engineers both have different methods to work. Let'sLet's have a walkthrough for both: Data Scientist Methodologies This is a crucial step because it helps understand the customer's end goal. It would be best if you asked many questions to understand every aspect of the problem statement. The result of this stage gives us a list of business requirements. Once the requirement is cleared, data scientists come up with an analytical approach. They express the problem in terms of statistical and machine learning techniques, which helps identify the problem statement pattern. This is the step to identify the data format, content, and sources for initial data collection. Data scientists collect data from different sources using techniques like web scraping or premade data present on the repositories. Data Understanding: Data scientists try to understand the collected data; they learn about the type and attribute of the data and check whether it is appropriate for the given requirement. This is one of the essential steps as data scientists start preparing data for their model. They need to make sure that the data collected and the ML algorithm selected are compatible with each other or not. In this step, data scientists understand whether their work is good to go or needs a kind of review. Modeling focuses on developing either descriptive or predictive models, and these models are based on the analytic approach taken statistically or through machine learning. The model is ready to evaluate; data scientists can assess two ways: Holdout (dividing the dataset into three parts: training, validation, and testing) and Cross-validation. Once the data scientists are confident with their work, the model gets deployed. This step is usually made for most of the customers. They can check whether the deployed model fulfills their requirement or not. This stage can have several iterations. Business Understanding: Analytic Approach: Data Requirements: Data Collection: Data Preparation: Modeling: Evaluation: Deployment: Feedback: Software engineer methodologies Software engineers use various methodologies, let’s discuss the three popular methods: Requirements are refined iteratively by dividing the work into smaller chunks. The working style of teams is highly collaborative. There are regular scrum meetings. The agile process is transparent; there is an open interaction with investors and stakeholders. In agile, the planning cycles are shorter, so it becomes easy to accept and accommodate the change at any point throughout the project. It is a linear model that consists of sequential phases (requirements, design, implementation, verification, maintenance). It is mandatory to complete the current stage to proceed to the next step. There is no option to backtrack the flow for any modification in the project. It is a slow and costly methodology due to its rigid structure and also most of people confused to choose the right model between agile and waterfall. Check this for head to head comparison between them. This kind of development produces high-quality software at a low cost. It contains four phases: requirements planning, user design, construction, and cutover. The second and third phases repeat until users get to confirm that the product fulfills their requirements. RAD is helpful for small to medium size projects that are time-sensitive. Agile development: Waterfall model: source Rapid Application Development: Career Paths: Data Scientist vs Software Engineer Each company has its specific career roadmap. Below is the list of career paths for a data scientist: They have limited experience and start as entry-level data scientists. They are usually assigned the task to explore and test new ideas along with refactoring the existing models. This career period brings the opportunity to learn new skills and gain experience while working on real-world projects. They are responsible for building well-architectured projects. They act as a mentor for associate-level data scientists and also deal with business-level people. They collaborate with finance, researchers, software developers, and business leaders to define product requirements and provide analytical support. Associate/Junior Data Scientist: Senior Data Scientist: They use exceptional mathematical skills to perform computations and work with the algorithms involved in this type of programming They collaborate with data engineers to build data and model pipelines and manage the infrastructure and data pipelines needed to bring code to production. They provide support to engineers and product managers in implementing machine learning in the product. These data scientists usually have 5+ years of experience and are well-versed with machine learning models. They understand challenges in multiple business domains, discover new business opportunities, and leadership excellence in data science methodologies. They have industrial maturity while delivering the designs and algorithms that is a plus point for cross-organization tradeoffs. AI/ML Engineer: Principal Data Scientist: Let's discuss a typical career path for a software engineer. They have limited experience and start as an entry-level engineer. They are usually assigned a task to develop software that meets client requirements within a specified amount of time. This career period brings the opportunity to learn new skills and gain experience while working on real-world projects. They master the software development lifecycle process. They get the opportunity to train junior software engineers and manage small teams. At this stage, engineers start to get introduced to other business elements such as high-level company objectives and project budgets. They are responsible for the entire software development lifecycle. They usually manage a large team of professionals that are part of software design and development. They are responsible for reporting development progress to company stakeholders and provide input into the decision-making process. This role requires strong leadership skills. They are responsible for the well-being of the entire team. They help in the career progression of their team. This role usually overlooks the entire architecture technical design. They are responsible for providing technical leadership and building processes for the team members. They ensure that the technological resources can satisfy the short and long term needs of the company. They are responsible for outlining the company goals for R&D. They help various departments to use technology profitably. Associate Software Engineer: Senior Software Engineer: Tech Lead: Team Manager: Technical Architect: Chief Technology Officer: Salary: Data Scientist vs Software Engineer The salary of an employee is usually based on their skill, experience and performance. This section will give you a brief idea for the salaries of data scientists and software engineers. Data Scientist Salaries In India, the average salary for a data scientist in India is Rs.698,412 per year, and in the United States, the average salary for a data scientist is $117,142 per year. Software Engineer Salaries In India, the average salary for a software engineer in India is Rs.566,971 per year, and in the United States, the average salary for a software engineer is $99,315 per year. Different Tools: Data Scientist vs Software Engineering The data scientist role is highly competitive. They work on various tools that help them in carrying out their machine learning-related tasks: Some of them are listed below: Apache Spark: It is a popular data science tool specifically designed to handle batch processing and stream processing. It is designed to support data analytics-related tasks and is capable of handling streaming data. Tableau: It is a data visualization tool that helps in data analysis and decision-making. You can represent data visually in less time by Tableau so that everyone can understand it, and it becomes easy for you to solve advanced data analytics. Tensorflow: It is a widely-used tool helping hand with various newly emerging technologies like Data Science and Artificial Intelligence. TensorFlow is a Python-based library that you can use for building and training Data Science models. BigML: It is used for building datasets and then sharing them easily with other systems. One can efficiently perform data classification and find the outliers in the dataset. Data scientists can make decisions due to their interactive data visualization process. PowerBI: It is also one of the essential tools of Data Science integrated with business intelligence. It is possible to generate rich and insightful reports from a given dataset using PowerBI. Users can also create their data analytics dashboard using PowerBI. Software engineering tools are essential for any organization to be productive and tackle business challenges. GitHub: It is also known as a Google Drive for software industry projects. This tool is a hosting service with open-source codes and allows you to upload any project. The IT community consists of many developers who can share, discover, and collaborate with this tool. Jenkins: This is an open-source that offers orchestration capabilities to deploy various kinds of applications. It is used for development, continuous integration, testing, and deployment. GitLab: It is a web-based tool popular for the developers’ lifecycle management. GitLab is a platform that manages git repositories and provides integrated features like continuous integration, issue tracking, team support, and wiki documentation. Jira: It is used to plan and manage the projects. It becomes easy to customize the workflow, generate performance reports, track the team backlogs, and visualize progress. Docker: It helps in the packaging of the software into a file system. Docker makes creating containers (lightweight, standalone, executable package of the software) easier, simpler, and safer to build, deploy and manage containers. IDEs: Integrated Development Environment (IDE) enable programmers to consolidate the different aspects of writing a computer program.It increases programmer productivity by combining everyday software development activities into a single application like editing source code, building executables, and debugging. Summary Software engineers and data scientists are among the famous roles in the IT industry. The skills required for both positions have similarities and differences. The work and salary might differ with company and location, but the specific part is already discussed. Thi blog also mentions the advancement in career for both the roles, which is the only matter of how much experience you have. These roles will never end because the requirement for both is high.

Data Scientist vs. Software Engineer - Key Differences Explained

In part 1 of this article, I talked about the first two out of five steps for converting your idea to MVP. This article elaborates remaining steps: defining architecture, setting up development process and initial marketing strategies. Before we go into these steps, I would like to reiterate: “Don’t forget your users during MVP phase.”. In the early stages of MVP development, most of the founders with tech background hesitate to go in front of their potential users. It seems easier to deep dive in the comfort zone of product development rather than exploring unknowns in the grey area of product-market fit. If you keep reminding yourself that MVP is not just about developing tech but also to ascertain viability of key business differentiator, you will be well-prepared to start full product development after completing the MVP. During MVP phase, there should be an equal focus on uncovering unknowns in operations, content development and go-to-market aspects of your business as developing the product. Having said that, let’s look in detail at the next three steps of MVP development. 3. Architecture Defining an elaborate architecture for MVP may seem like the overkill. Typical thinking is that the MVP is just for quickly validating the idea, and the software can be rewritten once we decide to develop a complete product. Too many first-time founders fall in this trap. When there is traction to the MVP, rewriting seems to be disruptive, time-consuming and expensive. At the same time, building on top of such a software soon results in spaghetti and bloated code which quickly becomes hard to maintain and expand. A few days spent on a high-level architecture in MVP phase build a solid foundation for the product. Logical Component Design Logical component design allows visualizing a well-defined boundary around all the internal services of product. It also helps clarifying well-structured contracts between these services. With proper micro-services architecture, you can easily replace one implementation of the service with another one in the future. Services can be defined based on encapsulation of functionality, scalability characteristics, real-time vs non-real-time requirements, CPU or I/O bound operations etc. Each service can be independently deployed and scaled. A typical logical component diagram shows logical internal and external building blocks of the product such as User and Admin interfaces, Apps, SDKs, Authentication and Authorization Services, Adapters, Gateways, Queues, Aggregators, Processing Engines, Schedulers, etc. Use logical component design to clearly define your internal and external services. Logical Component Diagram (Source: 47Billion ) Event flows Event flows show request/response and asynchronous events between services. It is useful to draw event flows for a few complex features that involve multiple services. They help clarify responsibilities between various services and also can help identify and reduce complexity by moving around functionality between services. Flows that typically involve third-parties, authentication and authorization and a few common request/response flows are good candidates for event flow diagrams. Use event flows to identify complex interactions and redraw boundaries to simplify service interactions. Event Flow (Source: 47Billion ) Entity Relationship Model Defining entity relationship model is one of the most important tasks during architecture definition phase. You can refer to use cases written during product definition phase and list down all the entities and relationships between them. This phase can be used to uncover new entities or new relationships. You can revise use cases based on these new insights. ER model is a logical representation. The entity can map directly to a table, or you can denormalize and combine multiple entities based on read, write attributes of these entities. Use ER model to show core entities and define the relationship between them. Data Model (Source: 47Billion ) Technology stack It is easy to fall in the trap of thinking technology as a product and over-engineer during MVP phase. So instead of mapping use cases to product features to technologies, there is a tendency to reverse map. It is tempting to think of machine learning and blockchain as features without mapping them to use cases for your end users. It is important to remember that technology is merely a facilitator of your overall business idea and the tech stack you choose is completely transparent to your end users. Having said that, a well-chosen stack during MVP phase can have a significant impact on development speed, stability, reliability, performance, and costs. Another common pitfall is to assume that MVP stack can easily be replaced later with a more robust stack for the product. But in all probability, your MVP stack will become your product stack, at least during the first few of years. So choosing the right stack at this stage reduces a lot of rework in the later stages of product development. The choice of software stack for front-end, middleware and database can depend on variety of factors. Rather than debating pros and cons of different software stacks, below are some of the broad guidelines to choose the stack. Have a clear separation between front-end and backend through well-defined APIs. Weigh advantages and disadvantages of using native, or Progressive Web Apps for development of the app. React native Expose well-defined APIs for each backend service based on the logical architecture and event flows. Stick to one or two programming languages to reduce learning curve and overall complexity. Your choice of stateless App server framework like node.js+Express/Apollo (JavaScript), Flask (Python), Slim (PHP), SparkJava (Java) may significantly affect hosting costs later when you have large-scale deployment. With properly defined service oriented architecture, you have flexibility to use the right tool for the right service. This means your API server can be written in node.js and compute intensive analytics and machine learning code can be in Scala, Java or Python. Choice of the database should be based on number of factors such as ACID requirements, eventual consistency, read-write load, frequency, relationships, time-series, search, etc. There are varieties of databases to choose from such as Relational, Document, Columnar, Key-value, Reverse index, Graph. It is easy to underestimate deployment complexity and costs during MVP phase and choose multiple databases. But you might find that your requirements are easily satisfied with a single database given the overlapping features supported in modern databases. For example, simple graph relationships can be modeled in MongoDB or an SQL database. Choose popular open-source frameworks with active contributions from community. Using external PaaS services reduce time-to-market. This decision can significantly affect time-to-market for your MVP. Your core differentiator and services around it can eventually become your Intellectual Property (IP). It is something that, you hope, cannot be easily replicated by your competitors. You should identify these areas early in the process and develop them in-house. Design all services for horizontal scaling right from the start. Begin collecting as much useful raw data about your users as possible. Besides acting as a source of truth, it can be mined later for additional insights and machine learning model training. . Progressively add Machine Learning capabilities Most importantly, choose a stack based on your team’s expertise and skills. Choose software stack based on team’s skills while keeping in mind challenges of deployment and scaling in production. Software stack (Source: 47Billion ) Coding There are numerous articles and books written on “The art of programming”. Here are a few important guidelines - Write modular code. Be consistent. Choose simplicity over early optimization. Build tests as a part of code. Write modular, consistent and simple code that closely follows philosophy and structure of the chosen framework. Deployment Thinking about deployment architecture during MVP phase helps designing for long-term scalability and high-availability. It also helps in weighing costs vs. time-to-market tradeoffs of IaaS vs. PaaS services. PaaS services reduce time-to-market and devops headaches during MVP. But they also introduce vendor lock-in, reduce flexibility and increase costs. With properly designed architecture, you can always replace such services with internally developed services after MVP is done. Use containers (docker with Kubernetes) for development and deployment. The entire deployment automation becomes easier with the use of containers. The added advantage of cloud vendor agnostic deployment and increased portability. Add basic monitoring with services like AWS Cloudwatch, Crashlytics for Apps. Add appropriate logging to aid in debugging any issues. It is important to detect and fix any service outages or app crashes quickly when MVP is rolled out for early adopters. Database servers and any backend services should be secured behind VPCs. Plan your deployment for horizontal scaling and lower costs using deployment design. Multi Cloud Deployment Diagram with AWS and GCP (Source: 47Billion ) 4. Process Entrepreneurs typically fall into one of the two extreme traps during MVP development: thinking of agile as no planning or focussing too much on the process too early without understanding team dynamics. A proper balance between the two allows focusing on real deliverables without much overhead and provides constant feedback. A few days spent at the beginning on planning and setting up continuous integration and deployment process will go a long way towards smooth execution. Planning Work backwards from a date to go live with MVP. Reduce scope or split releases accordingly to fit the available time. This gives a sense of urgency and help focus on essential features. A typical first release should be planned within 3 to 4 months of the start. User visible high-impact features can be prioritized over backend operations. Use simple planning tools like Kanban boards to associate use cases with each sprint. It is easy to maintain backlog and track progress of the sprint using such tools. Plan sprints once in two weeks with daily stand-ups. the team should aim for end user visible features to demo at the end of each sprint. A sprint retrospective at the end of the sprint helps with improving subsequent sprints. Use a planning tool to focus on core features and track progress. Kanban board in Github (Source: 47Billion ) Continuous Integration (CI) A well-setup continuous integration process enforced with proper tools helps in smooth sprint deliveries. Create a source code repository, create a new branch for every new feature, use Pull Requests to merge code to Master at the end of the sprint. “Pull Request Review” allows developers to comment on the changes proposed in pull requests, approve the changes, or request further changes before the pull request is merged. Setup continuous integration process enforced using tools for smooth deliveries. Continuous Integration (Source: Jelle) Continuous Deployment (CD) A continuous automated deployment process provides immediate visibility to team’s work. Setup dev, stage, production deployment environments. Setup auto-build and auto-deployment on checkin. Use of containers and trusted registry. Number each release so it can be traced back to code repository. Develop simple migration scripts early on to take care of model changes after MVP deployment. Build and deployment notifications to your team’s messaging system or email help in early detection of issues. Setup CI-CD process but make sure you are not spending precious time in maintaining the process itself. Continuous Delivery using Docker and Jenkins 5. Marketing During MVP development phase, equal emphasis should be given to user acquisition and operational strategies. General thinking is that unless you have a working prototype, it is hard to approach potential users. Though true to some extent, you can start gauging interest from early adopters by talking about the product or giving them glimpses of interactive mockups. You should start putting together product distribution, go-to-market and pricing strategies during MVP development phase. If the product involves operations, putting together an operations team during MVP phase makes you ready to launch as soon as MVP is completed. Website A single page marketing website setup early helps generate early interest. A sign up form to notify users when product is launched can be added to the site. When the MVP is ready, this page can include links to Apps in App stores or entry point to registration and login of the web portal. Using CMS like WordPress for marketing site gives flexibility to non-tech founders to easily change content. Plugins for forms, Email, SEO, etc. can also be leveraged. Develop a single page marketing website for early signups. Content A well-written content for marketing website, App, web portal, user on-boarding, in-product user communication like emails, marketing communication, social media posts, blogs, investor pitches that fit visual design makes a significant difference. A good copywriter or a team member with a flair for writing can take on this job during MVP phase. If your product is content-focussed, this is the time to put together a solid content plan and team whose entire focus is on generating timely content for different user segments of your product. A combination of good writing with visuals portrays sleek image of your company and product. Analytics It is important to measure user acquisition channel effectiveness, user engagement and behavior and take corrective actions once MVP is deployed. A simple integration with Google Analytics gives you powerful insights into user behavior. You can also collect activity logs in low-cost storage like S3 which can be used for custom analytics. Integrate analytics to measure effectiveness of go-to-market strategies as well as product features. Google Analytics Retention (Source: Amazeemetrics) Campaigns Once MVP is deployed, the focus shifts to acquiring new users, preventing users you have got from leaving, and keeping them actively using your product. One of the ways to achieve this is to integrate campaign management software like Amplitude, MixPanel, CleverTap, Google Firebase, in your product. At a minimum, your early product should include ways to send targeted push notifications with deep linking for user retention and engagement. Integrate campaign management in your MVP for user retention and engagement. Campaign Manager (Source: 47Billion ) A few weeks of early preparation as described in these five steps can help you develop a successful MVP and builds a solid foundation for your product. Once MVP is launched, you are ready to start acquiring early adopters and further tune the product and go-to-market strategies. In this article I have described broad guidelines for developing MVP based on my experience. There are numerous different ways towards successful MVP. What worked and what didn’t work so well for you during MVP phase? If you need help bringing your ideas to life, contact us at info@47billion.com.

From Idea to MVP — Part 2

Before taking a deep dive into , I feel it is important to understand architecture and why it is a better alternative to traditional Monolithic applications. After that we will discuss how Docker can be leveraged to create loosely coupled microservices. Docker Microservice Here’s a link to previous article in this series: _This article is first in a series of articles on Docker. Today, we are going to introduce the core concepts of Docker…_medium.com Docker Tutorial Series Part 1: Images, Layers and Dockerfile Monolithic applications consist of an inner core that houses the business logic and an outer layer of adapters that interface with the real world. The business logic is segregated into modules that handle services, events and objects. While adapters provide access to databases, payment systems, messaging engines, web APIs and user interfaces. The application is packaged and deployed as a monolith, in spite of having logically modular design. During initial stages, such applications are very easy to develop, test, deploy and scale. But, as the complexity of application increases, each incremental change becomes very expensive. Developers, both old and new, face an uphill task trying to grasp the impact any code change would have on the application. Anyone who has worked with enterprise apps, with millions of lines of code that take forever to boot up and are nearly incomprehensible, as I did understands the sheer dread it invokes. Equally terrifying is trying to explain what you have learned to new hires. Adopting new technologies becomes almost impossible as it would involve rebuilding the application from scratch. To overcome the above problems, organisations have come up with the idea to split the application into several smaller interconnected services. At this point of time partitioning a system is more of an art than science, although several strategies exist to make the task easier. An application can be decomposed on the basis of service domain, business capability or operational responsibility. The goal is that simultaneous changes to more than one microservice should be minimized. A microservice is a mini-application with its own business logic and adapters. It implements a set of distinct features or functionality to serve its goal. It is developed and maintained by a small cross-functional team. It is only loosely coupled with other microservices. The above statements together imply that the team responsible for managing a microservice is free to develop new features at its own pace. Moreover the team is free to choose the technology stack it will use, thereby ensuring that it can adopt new technologies as it sees fit. This means that it can choose new hires from a deeper pool of applicants. Now, we come to the question how Docker can help us implement ‘Microservice Architecture’. Docker packages each service into a self-contained entity that is independent of underlying operating system environment. Each service can be deployed on its own at any physical or virtual location, independent of other services. Multiple instances of same services can be spawned. Functionally linked services can be placed next to each other on the same machine. This freedom is achieved is building a Docker container and putting everything that a service needs to run inside this container. First step in building a container is to create a configuration file called Dockerfile. A Dockerfile contains a list of instructions to install application dependencies such as compilers, interpreters, libraries and 3rd party modules, copy source code files and run system commands. This Dockerfile decouples a microservice from rest of application and allows the dev team to choose its technology stack, independent of rest of organisation. Moreover it specifies environment variables and open ports for transferring data and communicating with the outer world. The second step to create a Docker image, which is a blueprint of our microservice, by executing steps mentioned in Dockerfile. The Docker image is collection of layers built on top of each other in response to instructions issued in Dockerfile. What makes these layers special is that on each rebuild the Docker engine calculates checksum of each layer and uses previous version stored in cache if no changes are introduced in a layer. Armed with this information, we create our Dockerfile in such a manner that layers most likely to remain unchanged on successive rebuild are placed at the beginning and layers that change often such as one containing our source code are placed later. This makes rebuilding an image a far quicker process and improves speed of deploying new code changes. Lastly, we can create a Docker container, which is a runnable instance of our Docker image by issuing Docker run command. A Docker container encapsulates our microservice and makes it available on all platforms, irrespective of underlying environment, underlining Docker’s ‘build once, run anywhere’ motto. Now, we can run multiple parallel copies of this Docker image on a single system or on a cluster of machines. Containerization makes it easy to scale our application, i.e. we can dynamically increase or decrease the number of running instances of our microservice based on demand. But what makes scaling so efficient and powerful, when compared to monolithic applications, is that we can scale each microservice according to demand. Let’s take an example, suppose there is a sudden spike in user registration module inside our monolithic application. In this case to cater to new load, we must spawn new instances of our entire application, which will have a large footprint both in terms of memory and CPU usage. Whereas, in case of an application built using microservices we only have to scale up instances of user registration service, without affecting other services. This ability to fine tune our production environment is a huge blessing for all especially the Dev Ops team. This brings us to the end of this primer on microservice architecture. We discussed the limitations of traditional monolithic applications, which have been dominating enterprise development culture for a long time. Then we introduced microservice architecture as a valid alternative to manage complexities of software design and development. Finally, we elaborated on how Docker can help us build, deploy and scale microservices using container based approach. Originally published at blog.adityadixit.me .

Drixit Technologies

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Since 2018

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Drixit Technologies

drixit.com

11-50 employees

Since 2018

COMPANY RANKING

DRIXIT TECHNOLOGIES

EVERGREEN INDEX #3726

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