In this discussion, Başak Eskili, a Machine Learning Engineer at Booking.com and co-founder of Marvelous MLOps, shares her inspiring journey into the world of MLOps. She highlights key components like version control and CI/CD pipelines that are essential for effective machine learning operations. The conversation dives into the significance of feature stores in real-time applications and the best practices for building scalable ML systems. Başak also offers valuable career advice for aspiring ML professionals, emphasizing the importance of soft skills in tech.
Başak Eskili emphasizes the critical role of MLOps in ensuring machine learning models deliver value by integrating continuous monitoring and deployment practices.
Her journey highlights the importance of balancing technical and soft skills for career growth in the evolving tech landscape, particularly in machine learning.
Deep dives
Path to Machine Learning
Initially, pursuing a degree in computer science felt daunting, particularly due to a lack of enthusiasm for programming, stemming from an extroverted personality. However, a pivotal moment occurred during her studies when she had to choose a specialization, leading her to select machine learning over networking. This decision ignited her passion for the field and propelled her into further studies, including a master’s degree in AI from the University of Amsterdam. Her educational journey highlights the importance of adaptability and finding one’s niche, ultimately shaping her career as a machine learning engineer.
The Significance of MLOps
MLOps plays a critical role in enhancing the deployment and monitoring phases of machine learning models, ensuring they deliver value in production environments. It encompasses practices like continuous integration and deployment, as well as model performance monitoring to capture degradation over time. Notably, MLOps integrates principles from DevOps but includes unique challenges related to data management and the need for robust monitoring systems. This expertise is essential as it helps maintain the reliability and effectiveness of machine learning applications in dynamic settings.
Transitioning to Feature Engineering
In her current role at Booking.com, the focus is on feature engineering within a machine learning platform, aimed at facilitating efficient model development and deployment. This involves creating fast-service features that enable real-time predictions, crucial for enhancing user experience on large-scale applications. The architecture supports low-latency requests, integrating various technologies for optimal performance. The transition to this role also underscores her evolution from individual contributor to a position where she influences machine learning practices at an organizational level.
Learning from Experiences
Throughout her career, the importance of balancing technical and soft skills has become evident, as both are vital for professional growth. The realization that soft skills can enhance teamwork, communication, and project management has been formative. Efforts to improve these skills include seeking feedback and practicing communication in diverse environments, fostering personal and professional development. This perspective reinforces the dynamic nature of the tech industry, encouraging continuous learning and adaptation to new challenges.
Our guest today is Başak Eskili, Machine Learning Engineer at Booking.com and C-Founder of Marvelous MLOps.
In our conversation, we first dive into MLOps, its key components and how Başak got into the field. We then talk about Marvelous MLOps and her new course: "End to end MLOps with Databricks". Başak finally shares more about her current role at Booking with a focus on building feature stores.
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