New Edition of Machine Learning Engineering with Python Offers Free Access to Latest ML Engineering Techniques
The second edition of the popular book Machine Learning Engineering with Python is now available for free, providing MLOps and ML engineers with the practical knowledge needed to tackle real-world problems. With the rapid evolution of the field, staying ahead requires the acquisition of cutting-edge skills, and this book aims to equip readers with the necessary expertise.
Unlike many technical guides, this edition takes an examples-based approach, allowing readers to develop their skills through practical application. It delves into the technical concepts, implementation patterns, and development methodologies required to succeed in the field. By following the step-by-step guidance, readers will be able to navigate the ML development lifecycle and establish their own standardized model factory for training and retraining models.
One of the key highlights of this edition is its coverage of advanced topics such as CI/CD (continuous integration/continuous deployment) and drift detection. These concepts are crucial for ensuring the reliability and effectiveness of machine learning models in real-world scenarios. Additionally, readers will have the opportunity to explore the latest deployment architectures and scale up their solutions effectively.
The book also places a significant emphasis on the latest open-source and cloud-based technologies, recognizing their importance in the current ML landscape. Notably, the chapter on deep learning has been expanded to include topics like generative AI and LLMOps (Large Language Models Operations). This addition enables readers to harness the power of tools like LangChain, PyTorch, and Hugging Face for supercharged analysis. Furthermore, the integration of AI assistants like GitHub Copilot assists in increasing productivity while addressing engineering considerations related to deep learning.
To access the second edition of Machine Learning Engineering with Python, readers typically need to purchase it for $39.99. However, for a limited time, BetaNews readers can obtain the book for free. This offer presents an excellent opportunity for MLOps and ML engineers to enhance their skills and stay up-to-date with the latest advancements in the field.
In conclusion, the second edition of Machine Learning Engineering with Python is a comprehensive guide that equips readers with essential skills and knowledge. Its practical approach, coupled with its coverage of advanced topics and emphasis on open-source and cloud-based technologies, makes it a valuable resource for anyone working in the field of machine learning. Don’t miss the chance to access this valuable resource for free and stay ahead in the rapidly evolving world of ML engineering.
