Machine Learning System Design | Introduction | Lesson 1

hello everyone and welcome to this course on machine Learning System design machine Learning System design is the Cornerstone of machine learning interviews at top tech companies and mastering this ability will not only distinguish you as an engineer but also positions you for higher seniority roles by showcasing Your Capacity to think holistically about Real World Systems this course is designed to transform your problem solving approach instead of focusing on memorizing or trivia we dive into the structure design process behind most frequently Asked interview questions at companies like Google meta and Amazon you will learn to architect endtoend machine Learning Systems from data pipelines to a scalable deployment through Hands-On scenario based exercises by the end of the course you will have the confidence to excel in any ml interview impressing hiring managers with your ability to balance tradeoffs optimize for a scalability and articulate high level system strategies so let's begin so first let's see the course overview so the goal of this course is to prepare you for ML system design interviews and build understanding of how ml systems work in general so the key takeaways that you can get from this course is that by the end of this course you should understand core components of ml systems then uh this course will also cover some real world design problems for example uh Building recommender Systems feeds for Instagram or Facebook and uh cases like uh ranking the search results and if you master these then you can Master interviews for Fang and other tech companies so first of all why do we need ml system design and why these are given so much importance in ml engineering interviews especially for senior roles so one is that it's important for interviews so this course will help you in cracking some of those interviews so in the interviews uh they test end to endend problem solving skills and they evaluate tradeoff analysis like how well you identify uh different trade-offs uh like accuracy latency cost scalability so we will cover all of these in detail and then uh it also helps you in real world uh relevance so it helps you build systems that scale for example recommendation engines fraud detection spam detection Etc now let's see what are the key components of ml systems so there are four major components one is data pipeline one is another is model development then deployment and serving and then monitoring and maintenance so data pipeline consists of collecting the data preparing your data for training pre-processing it and how you store the data then once you have the data then you have uh model building phase where you do training validation optimizing the model and then you deploy it and expose it as an API microservices then you deal with Cloud infra and once your model is deployed and running you continuously log data from it so that you may need to retrain and improve your model you can also do further testing like AB testing you roll out different uh features in your application and test which one is doing better so these uh are the major broad categories of a ml system now let's see some examples so we have already seen um like recommendation systems uh it can be uh movies on Netflix it can be products on Amazon website or any other recommendation like songs or other things then search ranking systems so whenever a user search something uh how will you rank those search results so this is another practical application then we have visual understanding systems like segmenting the images and video frames and these can be helpful in um use cases like self-driving cars now let's see how we can approach a system design problem so this is a very high level View and we will go into detail of these and we will also see uh on some of the Practical use cases then it will be much more clear so let's see uh the Approach at a high level so first and foremost is that you should understand the question well so if somebody says you have to design a feed for Instagram so it can consist of so many things that you can get lost into it and you may not be able to come up with with a solution within given 45 minutes or 1 hour so it's very important that you should clarify the question understand the requirements with the interviewer then comes system requirements like what is the scale how many users are there are we expecting millions of user or billions of users how uh fast should the model uh process something so Laten see another requirement you should clarify and once the requirements are clear you should Define performance metrics like how you will measure whether your model is doing good or not so you can uh depending on the use case you can Define different performance metrics and then you have to identify now your requirements are clear you understand the problem you have a performance metrics in place now you will identify what are the high level components what are the key building blocks of the ml system that you want to design and once you have a high level block diagram then you should start of thinking of building the model so for that first you will need to come up with a strategy for generating training data and then you have to identify what are the key features that can be important uh for this ug case for example you have data but let say U we have some uh ID so that ID will not be impacting any decisions so I'm just giving a vague example but you understand feature engineering uh is very important once you have the training data and then uh you will think of how to what kind of a strategy you need like whether you want to start from scratch or there is already some uh pre-train model which you can fine-tune for your uge case that will depend on uh like how much compute or uh data you have and that will also depend on other factors uh so it uh can in also you can uh think of like what should be the architecture of your model so all of these things will come in this last stage and once you have that you can go ahead and build that model so this till this point it's the design part ml system design of course you can extend it further by uh deploying and uh monitoring data from your model uh um how people are interacting and then doing AB testing and then further uh refining your model retraining and those things but uh broadly at a high level in the design part you can think of till building the model so that's all for this introduction lesson in the further lessons we will start looking into some of the ml Concepts and after that we will start uh solving some of the real world ml system design problems