Spark, Ray and Python for Scalable Data Science

Colleagues, the global data science platform market was valued at $31B in 2020, and it is expected to reach $230B by 2026, registering a CAGR of 39.7 % during the forecast period according to Mordor Intelligence. The #10 recommendation on our Top 10 countdown is Spark, Ray and Python for Scalable Data Science from InformIT. Learn to integrate Python and distributed computing, scale data processing with Spark, conduct exploratory data analysis with PySpark, utilize parallel computing with Ray and implement machine learning and artificial intelligence applications with Ray. Skill-based training modules include: 1 - Introduction to Distributed Computing in Python, 2 - Scaling Data Processing with Spark, 3 - Exploratory Data Analysis with PySpark, 4 - Parallel Computing with Ray, and 5 - Scaling AI Applications with Ray. Ray enables you to scale both the evaluation and tuning of our models. You see how Ray makes possible very efficient hyperparameter tuning. You also see how, once you have a trained model, Ray can serve predictions from your machine learning model. Finally, the lesson finishes with an introduction to how Ray can enable you to both deploy machine learning models to production and monitoring.

Enroll today (teams & execs welcome): https://tinyurl.com/muhzvb8j 

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Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (share with your team)  

Graphic source: Course Report