About Anmol Rajpurohit

Anmol is an experienced software developer with focus on Cloud Monitoring and Reliability. As a Senior Software Engineer in Splunk Enterprise Cloud, he oversees the monitoring and alerting pipelines across all Splunk Cloud stacks. Besides software development, he is actively involved with product management and data science. Before joining Splunk, he led the development and maintenance of several tools for performance engineering, data science, and predictive analytics at Salesforce. He holds a MS in CS from UC Irvine.

Anmol Rajpurohit Posts (326)

• Strata SF day 2 Highlights: AI and Politics, Chatbots Insights, Forecasting Uncertainty, Scalable Video Analysis, and more - 03 May 2019
  
  AI influencing Politics, insights from Chatbots, Enterprise Data Cloud, handling Video Big Data, and more takeaways from Strata Data Conference 2019, San Francisco.
• Strata SF day 1 Highlights: from Edge to AI, scoring AI projects, cyberconflict, cryptography - 29 Apr 2019
  
  Journey from “Edge to AI”, scoring your AI projects, cyberconflict, role of cryptography in AI and more insights from a leading conference.
• Key Takeaways from AI Conference SF, Day 2: AI and Security, Adversarial Examples, Innovation - 30 Oct 2018
  
  Highlights and key takeaways from selected keynote sessions on day 2 of AI Conference San Francisco 2018.
• Key Takeaways from AI Conference SF, Day 1: Domain Specific Architectures, Emerging China, AI Risks - 29 Oct 2018
  
  Highlights and key takeaways include Domain Specific Architectures – the next big thing, Emerging China – evolving from copying ideas to true innovation, and Addressing Risks in AI – Security, Privacy, and Ethics.
• Key Takeaways from KDD 2018: a Deconfounder, Machine Learning at Pinterest, Knowledge Graph - 11 Sep 2018
  
  Highlights and key takeaways from KDD 2018, 24th ACM SIGKDD conference on Data Science and Data Mining: including what is a deconfounder, how Pinterest approaches Machine Learning, Knowledge Graph for Products, and Differential Privacy.
• Key Takeaways from AI Conference in San Francisco 2017 – Day 2 - 02 Oct 2017
  
  Highlights and key takeaways from day 2 of AI Conference San Francisco 2017, including current state review, future trends, and top recommendations for AI initiatives.
• Key Takeaways from AI Conference in San Francisco 2017 – Day 1 - 29 Sep 2017
  
  Highlights and key takeaways from day 1 of AI Conference San Francisco 2017, including current state review, future trends, and top recommendations for AI initiatives.
• Must-Know: What are common data quality issues for Big Data and how to handle them? - 16 May 2017
  
  Let's have a look at common quality issues facing Big Data in terms of the key characteristics of Big Data – Volume, Velocity, Variety, Veracity, and Value.
• Must-Know: When can parallelism make your algorithms run faster? When could it make your algorithms run slower? - 25 Apr 2017
  
  Efficient implementation is key to achieving the benefits of parallelization, even though parallelism is a good idea when the task can be divided into sub-tasks that can be executed independent of each other without communication or shared resources.
• Must-Know: Why it may be better to have fewer predictors in Machine Learning models? - 04 Apr 2017
  
  There are a few reasons why it might be a better idea to have fewer predictor variables rather than having many of them. Read on to find out more.