{"id":58135,"date":"2024-02-15T15:26:57","date_gmt":"2024-02-15T14:26:57","guid":{"rendered":"https:\/\/dataconomy.ru\/?p=58135"},"modified":"2024-09-17T14:55:56","modified_gmt":"2024-09-17T13:55:56","slug":"how-to-drive-ai-and-ml-advancements-on-big-tech-with-radhika-kanubaddhi","status":"publish","type":"post","link":"https:\/\/dataconomy.ru\/2024\/02\/15\/how-to-drive-ai-and-ml-advancements-on-big-tech-with-radhika-kanubaddhi\/","title":{"rendered":"How to drive AI and ML advancements on Big Tech with Radhika Kanubaddhi"},"content":{"rendered":"

Radhika Kanubaddhi<\/a> has significantly impacted the world of artificial intelligence (AI) and machine learning (ML) by working for some of the top technology companies. With a solid background in computer science, Radhika has delivered innovative solutions that have transformed how businesses operate. In this interview, she talks about her work at Epsilon, Microsoft, and Amazon, and shares insights on driving AI and ML advancements in Big Tech.<\/span><\/p>\n

Could you tell us about your early work at Epsilon and how you contributed to AI and ML advancements there?
\n<\/b>At Epsilon, I led a team that worked on developing cutting-edge ML recommendation engines<\/a>. One of our most notable achievements was implementing a real-time recommendation engine for an airline, which resulted in a $214 million revenue increase within 30 days. Additionally, I piloted an email marketing campaign for a retail client with a 27% increase in orders over eight weeks. I also developed an email recommendation engine, contributing to a 31% lift in client ticket purchases. My work there focused on bridging technical solutions with measurable business outcomes.<\/span><\/p>\n

\"\"How did your work at Microsoft evolve, and what key projects did you work on there?
\n<\/b>At Microsoft, I focused on building and deploying AI solutions, particularly enterprise-grade chatbots. One of my key projects was developing a cloud-native chatbot solution for a hospitality client, utilizing Azure QnA Maker and Azure LUIS. This project generated $1 million in annual revenue by helping the client adopt cloud solutions. My work at Microsoft involved understanding the needs of our clients and guiding them through implementing AI solutions that would enhance their operations. I was fortunate to work on natural language processing (NLP) technologies, which paved the way for more intuitive customer interactions.<\/span><\/p>\n

What challenges did you face while developing these AI solutions at Microsoft?
\n<\/b>Developing AI solutions often comes with challenges, especially when working on large-scale enterprise systems. One challenge was ensuring that the AI technologies were scalable and adaptable to meet clients’ evolving needs. Understanding AI and machine learning fundamentals helped me navigate these complexities. I also collaborated closely with client executives to ensure our solutions met their strategic goals.<\/span><\/p>\n

What kind of work did you focus on at Amazon, and how does it connect with AI and database technology?
\n<\/b>At Amazon, I developed database technology<\/a> that supports AI applications used across Amazon\u2019s platforms. One of my most significant achievements was the development of a high-efficiency database capable of operating with a single millisecond latency. This is a critical component for AI and machine learning applications, as they require real-time data access and processing capabilities. My work at Amazon centered around optimizing systems for speed and reliability to ensure AI applications function at their best.<\/span><\/p>\n

As a woman in engineering, have you faced any challenges, and how have you worked to overcome them?
\n<\/b>Yes, there have been challenges as a woman in engineering, which is still male-dominated. However, I\u2019ve been fortunate to rely on a solid theoretical foundation in computer science and problem-solving to overcome these challenges. I also dedicate time to teaching high school girls about engineering and computer science to encourage more young women to explore STEM fields. Promoting diversity and inclusion in the tech industry is important, and I try to impact this area positively.<\/span><\/p>\n

What excites you most about the future of AI, and what are your aspirations in this field?
\n<\/b>I\u2019m excited about the advancements in generative AI. AI has immense potential to revolutionize industries and create more intuitive and efficient solutions. Looking ahead, I hope to continue working on cutting-edge AI technologies and contributing to developing solutions that will benefit businesses and society as a whole. I also want to continue mentoring and encouraging more women to enter the AI and technology fields.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Radhika Kanubaddhi has significantly impacted the world of artificial intelligence (AI) and machine learning (ML) by working for some of the top technology companies. With a solid background in computer science, Radhika has delivered innovative solutions that have transformed how businesses operate. In this interview, she talks about her work at Epsilon, Microsoft, and Amazon, […]<\/p>\n","protected":false},"author":625,"featured_media":58137,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jnews-multi-image_gallery":[],"jnews_single_post":{"format":"standard","override":[{"template":"5","layout":"right-sidebar","sidebar":"default-sidebar","second_sidebar":"default-sidebar","share_position":"float","share_float_style":"share-normal","show_share_counter":"1","show_view_counter":"1","show_featured":"1","show_post_meta":"1","show_post_author":"1","show_post_author_image":"1","show_post_date":"1","post_date_format":"default","post_date_format_custom":"Y\/m\/d","show_post_category":"1","show_post_reading_time":"0","post_reading_time_wpm":"300","post_calculate_word_method":"str_word_count","zoom_button_out_step":"2","zoom_button_in_step":"3","show_post_tag":"1","number_popup_post":"1","show_author_box":"0","show_post_related":"1","show_inline_post_related":"0"}],"image_override":[{"single_post_thumbnail_size":"no-crop","single_post_gallery_size":"crop-715"}],"trending_post_position":"meta","trending_post_label":"Trending","sponsored_post_label":"Sponsored by","disable_ad":"0"},"jnews_primary_category":[],"jnews_social_meta":[],"jnews_override_counter":{"view_counter_number":"0","share_counter_number":"0","like_counter_number":"0","dislike_counter_number":"0"},"footnotes":""},"categories":[3206],"tags":[],"coauthors":[16788],"class_list":["post-58135","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-conversations"],"_links":{"self":[{"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/posts\/58135","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/users\/625"}],"replies":[{"embeddable":true,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/comments?post=58135"}],"version-history":[{"count":0,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/posts\/58135\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/media\/58137"}],"wp:attachment":[{"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/media?parent=58135"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/categories?post=58135"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/tags?post=58135"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/coauthors?post=58135"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}