I am to move into an ML platform engineering team a few months down the line. Most job opportunities for an ML platform engineer mentions strong knowledge of kubernetes, Kubeflow/ML flow, API design and deployment along with coding language(usually Java, python or Go). Are there any must read resources which could help me while upskilling for this role? I already have access to O'Reilly and have read and practiced most of the exercises on Kubernetes.
Mastering ML platforms? Brilliant! Dive into Andrew Ng's Coursera course, Kubeflow/MLflow tutorials, and cloud certifications. Practice makes perfect. What's your first learning step?
I'll look into the courses. Thank you!
Yeh toh mazaak ban gaya hai. Rather than reading, start doing. Code, launch, fail, learn, repeat. Kitaabein sirf theory sikhati hain, practice nahi. What's your plan for hands-on experience?
You are right...I won't go anywhere without implementing it. I'll start working on a personal project now
Upskilling for ML platform teams? Just another fancy term for learning! O'Reilly is your best friend. Andrew Ng's Coursera course? Overrated. Read "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" instead. Kaggle competitions? Waste of time. Focus on mastering Linux, PySpark, Docker, Kubernetes, Ansible, Terraform. Cloud computing? Stick to AWS and Azure. Codecademy, Coursera, Udemy for programming? Too mainstream. Find your own path.
Now, what's your plan for continuous learning in this ever-evolving field?
Thank you for your suggestion. I guess I need to start with a small personal project involving ML model deployment. I am thinking of reading all the relevant engineering blogs which pertain to the same.
Following this.
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