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Machine Learning Engineering
Join now and get tips and tricks directly in your inbox. ๐ฌ Learn how to build ML systems end-to-end. ๐
Build ML Systems End-to-End
Design ยท Develop ยท Deploy ยท Iterate
Find everything you need to know about building machine learning systems end-to-end. Get expert insights on how to design, develop, deploy and iterate on ML-powered software applications.
1. โ๏ธ Setup
- Environment
2. ๐๏ธ Data
- Preparation
- Exploration
- Preprocessing
3. ๐ค Model
- Training
- Tuning
- Evaluation
- Serving
4. ๐ฆ Utilities
- Logging
- Documentation
- Style Guides
5. ๐ฉโ๐ฌ Testing
- Code
- Data
- Models
6. โป๏ธ Reproducibility
- Versioning
7. ๐ MLOps
- Automation
- CI/CD
- Monitoring
- Version Control
Info
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Who is this blog for?
๐จ๐ผโ๐ป Developers ๐งโ๐ฌ Data Scientists ๐จโ๐ง ML Engineers
This blog is tailored for anyone interested in mastering the art of deploying robust, scalable, and efficient machine learning solutions. From foundational concepts to advanced techniques, we will cover a wide range of topics that cater to the needs of developers, data scientists, and ML engineers alike.
๐จ๐ผโ๐ป Developers
Whether you're a seasoned software engineer or a junior developer, the content will help you bridge the gap between traditional software development and machine learning.
๐งโ๐ฌ Data Scientists
If you're passionate about data analysis, predictive modeling, and statistical inference, the blog will provide you with the tools and techniques to bring your models to life in real-world applications.
๐จโ๐ง ML Engineers
For those specializing in machine learning engineering, our content will offer valuable insights into best practices for deploying, monitoring, and maintaining ML systems in production environments.
Meet the author
Hi, I'm Nikola Dakiฤ
I have a bachelor's degree in Software Engineering and a master's in Intelligent Systems.
I'm currently a Senior Software Engineer at SugarCRM, where I'm responsible for overseeing the entire lifecycle of
machine learning projects. Throughout my career, I've been part of several ML projects, working on
predictive modeling, computer vision, and large language models (LLM).
My passion for building end-to-end software applications has given me solid experience in software engineering,
machine learning, and DevOps. I'm thrilled to share my expertise with you, so you can create machine learning systems
that are robust, scalable, and efficient.
What you can expect to learn?
๐ Foundational Concepts
Understand the underlying theories and methodologies that form the backbone of both machine learning and software engineering.
๐ป Practical Tutorials
Step-by-step guides to building ML-powered software applications from scratch. From data preprocessing to model training, deployment, and beyond.
๐ Deployment Strategies
Learn about the various deployment strategies for production ML applications, including containerization, serverless architecture, and cloud-based solutions.