MLOps stands for machine learning operations. The term MLOps is derived from DevOps (Development Operations). It is used to streamline the machine learning process from development to deployment. The MLOps include training machine learning models, experiment tracking, model optimization, creating ML pipelines, saving and serving models, and monitoring and maintaining models in production. In short, you are automating all the processes from development to deployment, and you are constantly monitoring the logs, metrics, and performance.

In this week's #DataTalk, we talked about autonomous vehicles and computer vision with Dr. Inmar Givoni, who is the Autonomy Engineering Manager at Uber Advanced Technology Group. Prior to that she was the Director of Machine Learning at Kindred, where her team developed algorithms for machine intelligence, at the intersection of robotics and AI. She was the VP of Big Data at Kobo, where she led her team in applying machine learning and big data techniques to drive e-commerce, customer satisfaction, CRM, and personalization in the e-pubs and e-readers business. She first joined Kobo in 2013 as a senior research scientist working on content analysis, website optimization, and reading modeling among other things. Prior to that, Inmar was a member of technical staff at Altera (now Intel) where she worked on optimization algorithms for cutting-edge programmable logic devices.

Check out his latest research: https://sloanreview.mit.edu/projects/...

DataTalk is a fun show featuring data science leaders from around the world. We talk about artificial intelligence, machine learning, deep learning, computer vision, data visualizations, data ethics, data philanthropy, and much more.

In this week's #DataTalk, we talked with Jenna Lake about how computer vision and machine learning is helping sports teams understand, evaluate and improve their performance.

Every week, we chat with data science leaders from around the world on Facebook Live. This data science video series is part of Experian's effort to help people understand how data-powered decisions can help organizations and people develop innovative solutions to improve our world. In our #DataTalk, we're talked with Dr. Ayesha Khanna about the future of artificial intelligence.

Every week, we talk about important data and analytics topics with top data scientists on Facebook Live. In our upcoming #DataTalk, we're talking with Dr. Lindsey Zuloaga about how recruitment teams are using artificial intelligence to help them with their work. Get the video link and reminder to watch the Facebook Live event. To suggest future data science topics or guests, please contact Mike Delgado. Dr. Lindsey Zuloaga is the Director of Data Science at HireVue.

Beena is the Founder and CEO of Humans for AI and the Global VP of Data, Artificial Intelligence and New Tech Incubation at Hewlett Packard Enterprises. She also serves as a Board Advisor at iguazio, Predii, and Cal Poly University.

We believe that big data is good. In our recent #DataTalk, we had a chance to talk with Jenna Lake about how computer vision and machine learning is helping sports teams understand, evaluate and improve their performance. Jenna Lake is a Computer Vision Engineer at Second Spectrum. She earned a Master of Science in Computer Vision from Carnegie Mellon University and Bachelor's of Science Degrees in Computer Science and Biometric Systems at West Virginia University. She is passionate about finding creative solutions to improve the efficiency of Computer Vision algorithms.