685: Tools for Building Real-Time Machine Learning Applications, with Richmond Alake

Richmond Alake, a Machine Learning Architect at Slalom Build, sits down with Jon to share real-time ML insights, tools and career experiences for a high-energy and high impact episode. From his work at Slalom Build to his two AI startups, discover the software choices, ML tools, and front-end development techniques used by a leader in the field. This episode is brought to you by Posit, the open-source data science company (https://posit.co), by AWS Inferentia (go.aws/3zWS0au), and by https://WithFeeling.ai, the company bringing humanity into AI. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information. In this episode you will learn: • What is a Machine Learning Architect? [03:09] • Richmond's startups [12:07] • Why Richmond started a podcast [29:51] • Richmond's new course on feature stores [38:05] • Why Richmond produces data science content [43:25] • Why All Data Scientists Should Write [51:30] Additional materials: www.superdatascience.com/685

Om Podcasten

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact. Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy. We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.