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Data Queries
data queries
7 episodes
2 days ago
We discuss everything about a career in data science, with individuals from unorthodox backgrounds about their journey into becoming a data science. The gap between academia and data science and how to prepare yourself to make that career transition from your current role into a Data science, must know frameworks and tools, debunking complex statically and machine learning concepts into everyday life examples to make data science less of a theoretical concept but more like your favorite movie.
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Technology
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All content for Data Queries is the property of data queries and is served directly from their servers with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
We discuss everything about a career in data science, with individuals from unorthodox backgrounds about their journey into becoming a data science. The gap between academia and data science and how to prepare yourself to make that career transition from your current role into a Data science, must know frameworks and tools, debunking complex statically and machine learning concepts into everyday life examples to make data science less of a theoretical concept but more like your favorite movie.
Show more...
Technology
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Meet Prince Canuma an MLOPs and DevRel with Neptune AI, whose curiosity into computer science took him to India and presently in Poland working with a leading Ai StartUp.
Data Queries
1 hour 18 minutes 37 seconds
2 years ago
Meet Prince Canuma an MLOPs and DevRel with Neptune AI, whose curiosity into computer science took him to India and presently in Poland working with a leading Ai StartUp.

"In this episode of the Data Queries podcast, hosts William and Ama engage with MLOps expert, Prince Caduma. Prince takes us through his personal journey into the field of Machine Learning operations, sharing his early fascination with software's global impact and how a youthful determination to learn English allowed him to study in India and access world-class tech knowledge. He discusses the crucial role of ML Ops in deploying models into production from the typically isolated data science environment of Jupyter notebooks. Drawing on his extensive experience and vivid anecdotes, Prince breaks down the essence and significance of ML Ops in machine learning, illustrating his own mishap as a Freelance Data Scientist losing a critical JSON file, resulting in losing the client and discovering Neptune Ai. This episode promises insightful and engaging conversation around ML Ops, perfect for data scientists, MLOPs, and enthusiasts alike."


Connect with Prince

Twitter: https://twitter.com/CanumaGdt

LinkedIn: https://www.linkedin.com/in/prince-canuma-05814b121/

Medium: https://prince-canuma.medium.com/


Articles, talks and podcast:

What is MLOPs by Prince: https://neptune.ai/blog/mlops

Lightening talk at MLOPs: https://t.co/LlMuFfLXXM

Implementing MLOPs at a reasonable scale: https://t.co/lTtbebDt2h



Recommended Resources:


Community

MLOPs community: https://mlops.community/meetups/

Courses

Machine Learning Engineering for Production (MLOps) Specialization by Andrew Ng : https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops


Books

  1. Deep Learning with Python by François Chollet: https://www.amazon.co.uk/Deep-Learning-Python-Francois-Chollet/dp/1617294438
  2. Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples by Andrew P. McMahon: https://www.amazon.co.uk/Machine-Learning-Engineering-Python-production-ebook/dp/B09CHHK2RJ
  3. Designing Machine Learning Systems By Chip Huyen: https://www.amazon.com/Designing-Machine-Learning-Systems-Production-Ready/dp/1098107969
  4. Machine Learning Design Patterns By Valliappa Lakshmanan, Sara Robinson, Michael Munn: https://learning.oreilly.com/library/view/machine-learning-design/9781098115777/


Data Queries
We discuss everything about a career in data science, with individuals from unorthodox backgrounds about their journey into becoming a data science. The gap between academia and data science and how to prepare yourself to make that career transition from your current role into a Data science, must know frameworks and tools, debunking complex statically and machine learning concepts into everyday life examples to make data science less of a theoretical concept but more like your favorite movie.