The Only Free Course You Need To Become a MLOps Engineer
KDnuggets
FEBRUARY 9, 2024
Unlock the secrets to building, deploying, and monitoring models like a pro.
KDnuggets
FEBRUARY 9, 2024
Unlock the secrets to building, deploying, and monitoring models like a pro.
ProjectPro
SEPTEMBER 29, 2021
Probability and Statistics are two intertwined topics that smoothen one’s path to becoming a Machine Learning pro. In this blog, you will find a detailed description of all you need to learn about probability and statistics for machine learning. That is true but only to some extent.
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ProjectPro
JUNE 13, 2018
Let us now understand why there is such a huge demand for AWS cloud certifications and why you need one to jumpstart your career in cloud computing. Table of Contents Why do you need an AWS Certification? AWS Certification Roadmap - How to Become AWS Certified?
Knowledge Hut
FEBRUARY 7, 2023
Once upon a time, Data Science was something that was restricted only to the tech giants, but in this fast-growing world, it is slowly becoming an integral part of businesses as big companies start to integrate these techniques into their business models. What is a Data Science Platform? They are: 1.
ProjectPro
SEPTEMBER 26, 2021
According to a survey, big data engineering job interviews increased by 40% in 2020 compared to only a 10% rise in Data science job interviews. Table of Contents Big Data Engineer - The Market Demand Who is a Big Data Engineer? Big Data Engineer vs. Data Analyst vs. Data Scientist What does a Big Data Engineer do?
ProjectPro
OCTOBER 20, 2021
With so many pseudo-data scientists cropping up due to numerous data science bootcamps and courses that offer theoretical learning, the interview questions for AI and machine learning jobs are getting streamlined to filter those who understand how real-world implementation works. The value of a company lies solely in the interview process.
Monte Carlo
JANUARY 23, 2024
In the world of data engineering, Maxime Beauchemin is someone who needs no introduction. In short, Maxime argues that to effectively scale data science and analytics in the future, data teams needed a specialized engineer to manage ETL, build pipelines, and scale data infrastructure. Enter, the data engineer.
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