Join the next episode of Data Collab Lab hosted by Lee Blackwell and Franco Patano.
Details: Are you struggling with managing the lifecycle of your Data and AI projects in the cloud? Want to learn how to build Continuous Improvement and Continuous Delivery pipelines for your Machine Learning products? Join us on the next Data Collab Lab, when Micheal and Ivan show Lee and Franco how to build CI/CD pipelines on Databricks!
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Guests
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Michael Shtelma is Solutions Architect at Databricks and ex-Teradata Data Engineer with focus on operationalizing Machine Learning workloads in cloud.
Ivan Trusov is a Solutions Architect at Databricks. Main topics of experience are Apache Spark, machine learning and data processing applications. He is an active contributor to Databricks Labs projects which are focused on CI/CD and ML workloads operationalisation.
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Hosts
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Lee Blackwell is a Solutions Architect at Databricks where she enables other Engineers, Analysts, and Data Scientists to build scalable and reliable data systems. She has focused her career around Data Engineering, having built production pipelines across many sectors including Finance, AdTech, Retail, and Healthcare. Lee studied Data Science in school and quickly recognized the need for high quality, curated data once she joined the workforce. She enjoys being able to bring her bubbly personality and over 10 years of deep yet diverse experience to the table.
Franco Patano is a Solutions Architect at Databricks, where he brings over 10 years of industry experience in data engineering and analytics. He has architected, managed, and analyzed data applications both big and small, with open source and proprietary software, utilizing SQL, Python, Scala, Java, and Apache Spark, as well as experimenting with data science. Prior to Databricks, Franco worked as a Data Architect and Analyst in the Commercial Real Estate, Banking, and Education industries for organizations large and small.
Check out the Data Collab Lab Playlist: https://youtube.com/playlist?list=PLTPXxbhUt-YVlgVRQMYRbMwMVLu2JUioa
Details: Are you struggling with managing the lifecycle of your Data and AI projects in the cloud? Want to learn how to build Continuous Improvement and Continuous Delivery pipelines for your Machine Learning products? Join us on the next Data Collab Lab, when Micheal and Ivan show Lee and Franco how to build CI/CD pipelines on Databricks!
-----------------
Guests
-----------------
Michael Shtelma is Solutions Architect at Databricks and ex-Teradata Data Engineer with focus on operationalizing Machine Learning workloads in cloud.
Ivan Trusov is a Solutions Architect at Databricks. Main topics of experience are Apache Spark, machine learning and data processing applications. He is an active contributor to Databricks Labs projects which are focused on CI/CD and ML workloads operationalisation.
-----------------
Hosts
-----------------
Lee Blackwell is a Solutions Architect at Databricks where she enables other Engineers, Analysts, and Data Scientists to build scalable and reliable data systems. She has focused her career around Data Engineering, having built production pipelines across many sectors including Finance, AdTech, Retail, and Healthcare. Lee studied Data Science in school and quickly recognized the need for high quality, curated data once she joined the workforce. She enjoys being able to bring her bubbly personality and over 10 years of deep yet diverse experience to the table.
Franco Patano is a Solutions Architect at Databricks, where he brings over 10 years of industry experience in data engineering and analytics. He has architected, managed, and analyzed data applications both big and small, with open source and proprietary software, utilizing SQL, Python, Scala, Java, and Apache Spark, as well as experimenting with data science. Prior to Databricks, Franco worked as a Data Architect and Analyst in the Commercial Real Estate, Banking, and Education industries for organizations large and small.
Check out the Data Collab Lab Playlist: https://youtube.com/playlist?list=PLTPXxbhUt-YVlgVRQMYRbMwMVLu2JUioa
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