Sustainable & Scalable Machine Learning Project Development
What you'll learn
- How To Efficiently Build Sustainable And Scalable Machine Learning Projects Using The Best Practices
- Data Versioning
- Distributed Data Processing
- Feature Extraction
- Distributed Model Training
- Model Evaluation
- Experiment Tracking
- Error analysis
- Model Inference
- Creating An Application Using The Model We Train
- Metadata management
- Reproducibility
Requirements
- Basic Understanding Of Machine Learning
- Python Programming Language
- You Will Learn The Rest In The Course
Description
Are you ready to take your Machine Learning skills to the next level and develop projects that have real-world impact and are sustainable for the future? Look no further! This course is designed to give you the comprehensive knowledge and hands-on experience you need to design, build and maintain successful Machine Learning projects at scale.
In this course, you will learn how to tackle the most pressing challenges faced by ML professionals today, such as handling increasing amounts of data and ensuring that model and project development are both scalable and sustainable in the long run. Throughout the course, you will gain hands-on experience with the latest ideas and techniques used by top ML practitioners, and learn how to apply these techniques to real-world projects. From data versioning and data pre-processing, to model training, evaluation and versioning, you will acquire a deep understanding of each stage of the ML project development process.
You will also delve into the practical aspects of building scalable and sustainable ML projects, including designing robust pipelines and workflows. Throughout the course, you will work on a real-world project that will put your knowledge to test, and you will receive feedback and guidance from an experienced instructor who has worked on large-scale ML projects in the industry. You will also learn how to work with cloud-based ML infrastructure to ensure your projects are easily scalable. By the end of the course, you will have a powerful completed project in your portfolio that showcase your skills and demonstrate your ability to build and maintain scalable and sustainable ML solutions.
In this course, a strong emphasis is placed on sustainability, helping you avoid common pitfalls and ensuring that your projects can handle growing complexity, while remaining scalable and efficient in the long run. You will learn how to design projects that are robust and adaptable, and how to ensure that they will continue to provide value even as the industry evolves.
Join us today and become part of a vibrant community of ML professionals, through our chat platform (Slack), who are driving innovation and change in the industry. By the end of the course, you will have the confidence and skills needed to turn your ideas into successful and scalable ML solutions. Start your journey towards becoming a top ML professional!
Who this course is for:
- Students who are interested in pursuing a career in machine learning project development and want to gain expertise in sustainable and scalable development practices
- Machine learning engineers who are interested in developing machine learning solutions that are scalable and sustainable in the long run
- Data scientists who are looking to expand their skill set to include machine learning project development that is scalable and sustainable
- Researchers who are interested in developing machine learning models more efficiently
- Software developers who want to gain expertise in developing sustainable and scalable machine learning projects
- Start-up founders who want to develop machine learning projects that can be scaled up to meet future demands while also being sustainable
- Technical project managers who want to learn how to effectively manage and oversee sustainable and scalable machine learning projects
- Professionals in the technology industry who want to stay up-to-date with the latest trends and advancements in machine learning project development
- Companies and organizations that want to implement sustainable and scalable machine learning projects to improve their operations, efficiency, and profitability
Instructor
- 4.9 Instructor Rating
- 11 Reviews
- 50 Students
- 1 Course
I have always been interested in machines and software. One of the earliest memories I can remember is me building imaginary machines with big building blocks in the kindergarten I was attending.
My interest grew with me. This is the reason why I choose to study Electrical and Electronics Engineering in my Bachalor's degree. Later on, I focused mainly on Computer Vision applications during my Master's degree. During my studies I published 4 papers and graduated with a very good GPA.
Right after my studies, I started to work as a Machine Learning Engineer. Since then, I have worked on many different Computer Vision, NLP, and audio processing applications which have been used by many people.
I have experience both in academia as a Machine Learning Researcher, and in industry as a Machine Learning Engineer. Therefore I know how to combine theory and practice in a well suited way. One of the biggest issues I had in my studies was that most of the instructors just didn't care enough about the lectures, and they didn't care about whether or not the students were getting what they were talking about. I suffered from that a lot, and my intention is to make my courses as clear and detailed as possible, and my goal is to make everyone truly understand the content of my lectures.