Machine Learning System Design (Self-study)
ML's value is evident in predictions for service, resource, and cost efficiency. This course sharpens ML system design, from problem framing to deployment, covering principles and hands-on challenges.
Course Information
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Contents
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About 35 hours of video lectures & 10 hours of assignment review |
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Instructor
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Various FAANG+ instructors |
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Prerequisite |
High-level understanding with machine learning process and techniques |
Machine learning's growing importance is driven by its potent predictive capabilities, benefiting customer service, resource management, and cost savings. Recent strides in large language models and deep learning further amplify this trend.
This course aims to cultivate candidates' ability to architect software, infrastructure, algorithms, and data for machine learning systems. This encompasses framing business issues as machine learning problems, defining requirements and metrics, data exploration, algorithm selection, optimization, and deployment.
The course includes:
- Fundamentals review
- A framework for approaching ML design problems
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Hands-on practice with key ML design challenges
Expert instructors who've developed scalable systems lead the sessions, fostering independent problem-solving, idea exchange, and feedback. This cultivates the skills to excel and differentiate oneself in a competitive landscape.
Course Outline
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Introducing Machine Learning System Design
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Common issues in ML system design
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Master template for ML System design
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Framing a business problem using ML
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Training Data Collection
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Feature Engineering
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Model Selection, development, and training
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Model Evaluation
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Deployment and Maintenance
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Search Ranking
FREE PREVIEW -
Week1: ML System Design - Workflow and Steps
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Week 1: Search Engine
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Week2: Harmful Content Detection/Content moderation
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Week2: Recommendation System
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Week3: Object Detection
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Week3: Maps Image Blurring
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Week4: Ad Click prediction
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Week4: Personalized News feeds
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Week 5: Fraud Detection
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Week5: Text Summarization
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Week 1 - Ranking Specialization
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Week 2: Recommendation Engines Specialization
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Week 3: Image Understanding Specialization
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Week 4: Social Graph Learning Specialization
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Week 5: NLP Specialization
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About this course
- $399.00
- 25 lessons
Student Reviews
Very good course in ML System Design with good instructor
Chi Hoang
- This course teaches ML System Design (and NOT about basic ML) to solve actual problems such as: recommendation systems, ranking systems, searching systems, NLP problems, that you may see in real interviews. - Learners should have basic knowled...
Read More- This course teaches ML System Design (and NOT about basic ML) to solve actual problems such as: recommendation systems, ranking systems, searching systems, NLP problems, that you may see in real interviews. - Learners should have basic knowledge about ML to take full advantages of the course. - Overall this is a must-take course for learners aimed to tackle ML SD interviews in FAANG companies. The course will provide you with a general framework with 8 steps to handle almost any ML SD problems. Instructors are current FAANG software engineers, very knowledgeable and responsive in answering questions.
Read LessGreat aid in interview preparation!
Aum Rajadnye
I found the case studies covered in the ML System Design course to be not only very relevant to the current state of ML/DL but also covered in depth, especially the NLP use cases. Also, the mid-week practice sessions helped to reinforce what was t...
Read MoreI found the case studies covered in the ML System Design course to be not only very relevant to the current state of ML/DL but also covered in depth, especially the NLP use cases. Also, the mid-week practice sessions helped to reinforce what was taught on the Sunday class.
Read LessGreat Course for Software Engineers making the Transition to ML Engg. or ML Related roles
Roop G
I am a Full Stack Cloud Native Engineer(15+ years) making the transition, and I had completed Machine Learning Certificate and Deep Learning Certificate already. The course helped me answer the questions around ML in practice and Project Situati...
Read MoreI am a Full Stack Cloud Native Engineer(15+ years) making the transition, and I had completed Machine Learning Certificate and Deep Learning Certificate already. The course helped me answer the questions around ML in practice and Project Situations. The System Design Course elucidates the key steps in ML Projects and a Deep Dive on each of the Key Steps. Explaining and Rationalizing decisions/prospective-options at each step. The Instructors/specialists were loaded with both scientific and practical knowledge of ML Use Cases. All of them were Leading Key Projects in Focus ML areas at leading organizations at the cutting edge of ML(so kind of them to share the experience and knowhow). Silicon Insider mentor kept tab on the progress of the class in the session and steer the conversation so as to focus on what the students need at that juncture, I would Highly recommend this course for all Software Engineers in General irrespective of if they want to work as ML Engineers or not.
Read LessExcellent Course for ML System Design. Highly Recommend!
Alan Varghese
This course teaches how to use machine learning for various use cases. I find the course and the instructors collaborative. The instructors were knowledgeable in their field and could quickly and thoroughly address any questions. I would encourage...
Read MoreThis course teaches how to use machine learning for various use cases. I find the course and the instructors collaborative. The instructors were knowledgeable in their field and could quickly and thoroughly address any questions. I would encourage anyone who knows the basics of machine learning and the classical machine learning fundamentals to take this course to understand how the design of an ML system will work. As for me, this course allowed me to restart my ML learning after a long break.
Read Less