Ship & Grow 500k+ User Side Projects [Part 10] - Dogfooding

It's hard to get meaningful feedback to improve on your side project when it's just starting out. However, there is a way that you should really pursue as the developer. This lesson teaches you:

- The critical way for you to self-power leveling up your side project's quality
- Why dogfooding was an instrumental part of Facebook's and Instagram's success (and why it made the engineers better)
- Tips for dogfooding effectively and finding concrete ways to make your side project better

Ship & Grow 500k+ User Side Projects [Part 9] - Leverage Existing Patterns

Just because you're an engineer doesn't mean that you can't be a solid product designer. This lesson teaches you:

- The easiest way to bootstrap a product design from scratch
- What the pinnacle of front-end UI/UX looks like and the company behind it
- A company you can use as inspiration for a back-end project
- A company you can use as inspiration for an ML project

Ship & Grow 500k+ User Side Projects [Part 8] - Easy Design Wins

Creating a smooth, functional user experience isn't too hard. This lesson teaches you:

- How you can make features easier to access for users, explained with an in-depth example
- How to take care of users in trickier situations, explained with an in-depth example
- What you can do to lower the cognitive burden of using your product, explained with an in-depth example

Ship & Grow 500k+ User Side Projects [Part 7] - KISS

Engineers naturally gravitate towards complexity, and it shows in how they communicate and design things. This is a problem when it comes to side projects. This lesson teaches you:

- The beautiful, often under-appreciated value of simplicity
- Assumptions you shouldn't make about the end-user
- What it means to KISS (Keep It Simple, Stupid), explained with an in-depth example

Ship & Grow 500k+ User Side Projects [Part 6] - Least Clicks Design

You don't need to be a product design to build great software with a smooth user interface. This lesson teaches you:

- Why *every* engineer needs to care about UX, not just front-end engineers (web/mobile)
- The core philosophy you need to remember when crafting user interfaces
- The difference between a good product and a bad product when it comes to least clicks design
- What it looks like to truly champion least clicks design, explained with an in-depth example

Ship & Grow 500k+ User Side Projects [Part 5] - Make It 5x Better With 0 Effort

It can be tempting to make some money on your side projects given how much time you're putting in. However, this is almost never worth it. This lesson teaches you:

- Why the ROI on adding paywalls and ads to your product is pretty bad, especially when considering the true purpose of your side project
- Why payments are way more complex than you think
- The many downsides of adding ads to your product
- A little bonus you can do on top of making your product free and ad-free

Ship & Grow 500k+ User Side Projects [Part 4] - Planning Your Initial Launch: API Example

You have now figured out what should go into your side project MVP (minimum viable product). How do you bring it to life? This lesson teaches you:

- The exact steps to follow to ship your MVP
- What good decomposition looks like, explained with an in-depth example

You can go deeper into the mechanics of software decomposition here: <https://www.jointaro.com/course/level-up-your-code-quality-as-a-software-engineer/break-it-down/>

Ship & Grow 500k+ User Side Projects [Part 3] - Crafting The MVP

You should launch the initial version of your side project as fast as possible. So what exactly should it have? This lesson teaches you:

- The only thing your side project MVP (minimum viable product) needs to have
- How a piece of software can still deliver value despite having incredibly few features (or even just 1 feature)
- What a good MVP looks like, explained with an in-depth example
- A common mistake engineers make when it comes to shipping their MVP, explained with an in-depth example

If you're curious to learn why building a good auth flow is 5x harder than engineers think: <https://www.jointaro.com/course/level-up-your-code-quality-as-a-software-engineer/case-study-signup-form-edge-cases/>

Ship & Grow 500k+ User Side Projects [Part 2] - Done Is Better Than Perfect

Code quality is important, but side projects sort of break the rules here. This lesson teaches you:

- How good engineers often mess up their side projects from the very beginning
- Why making mistakes is perfectly fine (good even) when starting a side project
- How fast you should launch the v1 of your side project

Ship & Grow 500k+ User Side Projects [Part 1] - How This Course Works

Like most engineers, do you have a slate of personal projects on your resume but recruiters and hiring managers simply don't seem to care? Well, that's almost certainly because [your projects don't have any users](https://www.jointaro.com/course/build-side-projects-with-500k-users-coming-up-with-an-idea/what-makes-a-project-valuable/).

This course is here to fix that issue for you. Here are the core parts of the lesson:

- Your instructor [Alex Chiou](https://www.linkedin.com/in/alexander-chiou/) walks you through his extensive 10+ year Silicon Valley background with rapid career advancement, ending up as a high-performing tech lead making $750k at Robinhood. Most importantly, he goes through how he's built 30+ side project apps with 3 of them getting 500k+ users, getting him interviews at top companies like OpenAI, Google, Uber, Instacart, and more

- We walk you through the course objectives. By the end of the course, you will:

  - 1️⃣ Understand what makes a great first version

  - 😯 Learn easy, low-effort ways to improve a product

  - 🗣️ Set up the right mindset for processing user feedback

  - 💻 Know how to build a smooth user interface

  - 📈 Get tactics to acquire users organically

You can find the first side project course here: [Build Side Projects With 500k+ Users: Coming Up With An Idea](https://www.jointaro.com/course/build-side-projects-with-500k-users-coming-up-with-an-idea/)

Ace The ML System Design Interview [Part 10] - Final Thoughts

Congrats on making it all the way to the end of this course! We've now covered the key elements to help you succeed in your machine learning system design interviews. Remember, having a structured approach is essential. Here are the core points from this lesson:

- Use a system to guide you through the interview, helping to avoid common pitfalls and ensuring key aspects of the problem are addressed
- Treat time estimates for each stage as a guide, but prioritize the core sections: big-picture system design, data, and modeling
- Communicate your approach to the interviewer at the beginning of the interview to set clear expectations
- Demonstrate structure and a systematic approach to impress interviewers and showcase engineering capabilities

If you like what Ilya has to say, subscribe to his YouTube for more high-quality ML/AI career guidance: [MLEpath - YouTube](https://go.mlepath.com/taro-youtube-mlepath)

If you want hands-on support from Ilya to crack the FAANG ML interview, join his coaching program: [MLEpath - Coaching Program](https://go.mlepath.com/taro-march-25)

Ace The ML System Design Interview [Part 9] - Common Problems

While specific interview questions vary, common themes emerge, and preparing for these can significantly improve readiness. Here are the core points from this lesson:

- Scaled recommendation systems are frequently encountered, with core components like filtering, ranking, and embedding computation remaining relevant despite variations in specific use cases
- Adapt recommendation system designs to different contexts, such as local recommendations, where rule-based systems might complement or replace certain machine learning components
- Harmful content detection is another common topic, requiring a feedback loop to generate training data, particularly from ambiguous cases in the "maybe" category
- Be prepared to explain how to handle ambiguous cases, as this demonstrates a deeper understanding of the problem
- Understanding the general concepts behind these two question types will provide a strong foundation for tackling many other interview questions

If you like what Ilya has to say, subscribe to his YouTube for more high-quality ML/AI career guidance: [MLEpath - YouTube](https://go.mlepath.com/taro-youtube-mlepath)

If you want hands-on support from Ilya to crack the FAANG ML interview, join his coaching program: [MLEpath - Coaching Program](https://go.mlepath.com/taro-march-25)

Ace The ML System Design Interview [Part 8] - How To Prepare

Preparing for a machine learning system design interview can feel overwhelming, but a structured approach makes it manageable. Here are the core points from this lesson:

- Identify the specific areas of machine learning likely to be covered in your interview to focus your preparation
- Understand the trade-offs between different machine learning techniques by creating comparison tables outlining their pros and cons
- Conduct a mock interview early in your preparation to establish a baseline and identify areas for improvement
- Address feedback from mock interviews and consider a second mock to gauge progress
- Avoid last-minute cramming—structured preparation over a week or month is far more effective

If you want a mock interview from Ilya, you can book time with him here: [MLEpath - Mock Interviews](https://go.mlepath.com/taro-mocks)

If you like what Ilya has to say, subscribe to his YouTube for more high-quality ML/AI career guidance: [MLEpath - YouTube](https://go.mlepath.com/taro-youtube-mlepath)

If you want hands-on support from Ilya to crack the FAANG ML interview, join his coaching program: [MLEpath - Coaching Program](https://go.mlepath.com/taro-march-25)

Ace The ML System Design Interview [Part 6] - Deployment & Beyond

Discussing deployment and monitoring is a crucial signal of real-world machine learning experience in an interview. Here are the core points from this lesson:

- Familiarize yourself with deployment patterns like shadow mode, A/B testing, and blue-green deployments, understanding their trade-offs and when to apply them
- Monitor both offline (ML engineer-focused) and online (business-focused) metrics, paying close attention to the distribution of inputs and outputs at each stage of a multi-stage system
- Recognize that significant shifts in input or output distributions should trigger immediate attention, as they often indicate real-world changes affecting model performance
- Avoid suggesting online learning unless you have deep expertise, as it can lead to complex follow-up questions
- Be prepared to discuss retraining strategies, as models inevitably need updates over time

If you like what Ilya has to say, subscribe to his YouTube for more high-quality ML/AI career guidance: [MLEpath - YouTube](https://go.mlepath.com/taro-youtube-mlepath)

If you want hands-on support from Ilya to crack the FAANG ML interview, join his coaching program: [MLEpath - Coaching Program](https://go.mlepath.com/taro-march-25)

Ace The ML System Design Interview [Part 7] - Questions For Your Interviewers

Interviews are a two-way street, providing an opportunity to assess whether a company and team are a good fit. Here are the core points from this lesson:

- Prepare thoughtful questions that focus on factors directly impacting your work experience, such as on-call responsibilities and decision-making processes
- Avoid vague questions like "What's the work-life balance like?" and instead probe for root causes, such as the frequency and intensity of on-call duties or whether decisions are made top-down or bottom-up
- Understand the company's decision-making culture to gain insights into potential work-life balance challenges
- Ask targeted questions to get a clearer picture of the team's dynamics and the day-to-day realities of the role
- Take advantage of engineers’ straightforward answers to learn what it’s really like to work at the company

If you like what Ilya has to say, subscribe to his YouTube for more high-quality ML/AI career guidance: [MLEpath - YouTube](https://go.mlepath.com/taro-youtube-mlepath)

If you want hands-on support from Ilya to crack the FAANG ML interview, join his coaching program: [MLEpath - Coaching Program](https://go.mlepath.com/taro-march-25)

Ace The ML System Design Interview [Part 5] - Modeling

Modeling plays a critical role in machine learning system design, with classification and regression being the most frequently encountered problem types in interviews. Here are the core points from this lesson:

- Understand various evaluation metrics, such as precision, recall, F1 score, NDCG, and loss metrics like cross-entropy and focal loss, and know when to apply each
- Be prepared to discuss trade-offs between metrics, particularly precision and recall, and avoid over-optimizing for a single metric
- Recognize the significance of loss metrics, memorizing the equations for cross-entropy and focal loss, and understanding their applications in balanced and imbalanced datasets
- Be ready to discuss model selection, including architecture choices like two-tower models or matrix factorization, and anticipate follow-up questions on optimization techniques and troubleshooting issues like overfitting and vanishing gradients
- Consider a baseline model for comparison if no current system exists, and if there is an existing system, use it as a reference point

If you like what Ilya has to say, subscribe to his YouTube for more high-quality ML/AI career guidance: [MLEpath - YouTube](https://go.mlepath.com/taro-youtube-mlepath)

If you want hands-on support from Ilya to crack the FAANG ML interview, join his coaching program: [MLEpath - Coaching Program](https://go.mlepath.com/taro-march-25)

Ace The ML System Design Interview [Part 4] - Data

Data is the foundation of machine learning, and a thorough discussion of data is essential in any machine learning system design interview. Here are the core points from this lesson:

- Discuss labels, features, and data set splitting, understanding the trade-offs involved in each area
- Explore various methods for generating data labels, such as human annotation, synthetic data, and LLMs, while considering the pros and cons of each approach
- Focus on high-level feature sources, providing relevant examples and selecting predictive features while avoiding sensitive topics
- Understand feature encoding techniques, such as one-hot encoding and embeddings, and their impact on model performance
- Weigh the trade-offs between cross-validation and train-validate-test splits, while considering issues like data leakage and imbalances in the data set

If you like what Ilya has to say, subscribe to his YouTube for more high-quality ML/AI career guidance: [MLEpath - YouTube](https://go.mlepath.com/taro-youtube-mlepath)

If you want hands-on support from Ilya to crack the FAANG ML interview, join his coaching program: [MLEpath - Coaching Program](https://go.mlepath.com/taro-march-25)

Ace The ML System Design Interview [Part 1] - Introduction

We've created this course to help you navigate the machine learning system design interview, drawing from our extensive experience in big tech. Our goal is to demystify the process and provide you with the tools to succeed. Here's what you'll learn from this course:

- A structured approach to tackling complex machine learning system design interviews
- How to showcase your machine learning expertise, seniority, and communication skills beyond just designing a system
- Techniques for effectively communicating complex machine learning concepts to both technical and non-technical stakeholders
- Strategies to evaluate trade-offs and analyze the pros and cons of different engineering techniques
- How to stay calm, focused, and confident throughout the interview process

If you like what Ilya has to say, subscribe to his YouTube for more high-quality ML/AI career guidance: [MLEpath - YouTube](https://go.mlepath.com/taro-youtube-mlepath)

If you want hands-on support from Ilya to crack the FAANG ML interview, join his coaching program: [MLEpath - Coaching Program](https://go.mlepath.com/taro-march-25)

Ace The ML System Design Interview [Part 3] - Big Picture Design

A strong big-picture system design is crucial for success in machine learning system design interviews. It's not about drawing a perfect diagram immediately, but about demonstrating an understanding of the system's components and their interactions. Here are the core points from this lesson:

- Identify the inputs and outputs of the system during the alignment phase to establish a foundation for building the diagram
- Start with the user and the service, then progressively add components, especially those involving machine learning, to illustrate the flow of data and processes
- Break down machine learning components beyond a single "ML system" box to show the different stages and types of models involved
- Include essential infrastructure elements, such as feature stores, to demonstrate a comprehensive understanding of the system
- Iterate on the diagram, adding or modifying components as a deeper understanding of the problem and its requirements develops

If you like what Ilya has to say, subscribe to his YouTube for more high-quality ML/AI career guidance: [MLEpath - YouTube](https://go.mlepath.com/taro-youtube-mlepath)

If you want hands-on support from Ilya to crack the FAANG ML interview, join his coaching program: [MLEpath - Coaching Program](https://go.mlepath.com/taro-march-25)

Ace The ML System Design Interview [Part 2] - Why You Need A System

The machine learning system design interview can be daunting, but understanding common pitfalls can help you succeed. Here are the core points from this lesson:

- Align with the interviewer on the problem statement before diving into design to ensure the right problem is being solved
- Use a structured approach: start with big-picture system design, then address data considerations and modeling, only discussing deployment and operations if time allows
- Clearly articulate reasoning by explaining the "why" behind design choices and considering trade-offs between different techniques
- Take control of the interview by making assumptions, validating them with the interviewer, and simplifying the problem to fit within the given time
- Write down key assumptions and decisions to maintain clarity and provide a reference for both yourself and the interviewer

If you like what Ilya has to say, subscribe to his YouTube for more high-quality ML/AI career guidance: [MLEpath - YouTube](https://go.mlepath.com/taro-youtube-mlepath)

If you want hands-on support from Ilya to crack the FAANG ML interview, join his coaching program: [MLEpath - Coaching Program](https://go.mlepath.com/taro-march-25)

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