Francesca Lazzeri's guest appearances

Francesca Lazzeri

The InfoQ Podcast

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In the podcast, Srini Penchikala spokes to Dr. Francesca Lazzeri on Machine Learning for Time Series Forecasting as the main topic which included automated machine learning and deep learning for time series data forecasting as well as other emerging trends in Machine Learning Development and Operations areas including Data Science Lifecycle.Dr. Francesca Lazzeri currently works as Principal Cloud Advocate Manager at Microsoft. She is an experienced scientist and a machine learning practitioner with over 12 years of both academic and industry experience. She is the author of a number of publications, including technology journals, conferences, and books.Read a transcript of this interview: https://bit.ly/38x1V8NSubscribe to our newsletters:- The InfoQ weekly newsletter: bit.ly/24x3IVq- The Software Architects’ Newsletter [monthly]: https://www.infoq.com/software-architects-newsletter/Upcoming Virtual Events - https://events.infoq.com/InfoQ Live:https://live.infoq.com/- September 21, 2021- October 19, 2021QCon Plus online conference:https://plus.qconferences.com/- November 1-12, 2021Follow InfoQ:- Twitter: https://twitter.com/infoq- LinkedIn: https://www.linkedin.com/company/infoq/- Facebook: https://www.facebook.com/InfoQdotcom/- Instagram: @infoqdotcom- Youtube: https://www.youtube.com/infoq

In the podcast, Srini Penchikala spokes to Dr. Francesca Lazzeri on Machine Learning for Time Series Forecasting as the main topic which included automated machine learning and deep learning for time series data forecasting as well as other emerging trends in Machine Learning Development and Operations areas including Data Science Lifecycle. Dr. Francesca Lazzeri currently works as Principal Cloud Advocate Manager at Microsoft. She is an experienced scientist and a machine learning practitioner with over 12 years of both academic and industry experience. She is the author of a number of publications, including technology journals, conferences, and books. Read a transcript of this interview: <a href="https://bit.ly/38x1V8N" rel="nofollow" target="_blank">https://bit.ly/38x1V8N</a> Subscribe to our newsletters: - The InfoQ weekly newsletter: <a href="http://bit.ly/24x3IVq" rel="nofollow" target="_blank">bit.ly/24x3IVq</a> - The Software Architects’ Newsletter [monthly]: <a href="https://www.infoq.com/software-architects-newsletter/" rel="nofollow" target="_blank">https://www.infoq.com/software-architects-newsletter/</a> Upcoming Virtual Events - <a href="https://events.infoq.com/" rel="nofollow" target="_blank">https://events.infoq.com/</a> InfoQ Live: <a href="https://live.infoq.com/" rel="nofollow" target="_blank">https://live.infoq.com/</a> - September 21, 2021 - October 19, 2021 QCon Plus online conference: <a href="https://plus.qconferences.com/" rel="nofollow" target="_blank">https://plus.qconferences.com/</a> - November 1-12, 2021 Follow InfoQ: - Twitter: <a href="https://twitter.com/infoq" rel="nofollow" target="_blank">https://twitter.com/infoq</a> - LinkedIn: <a href="https://www.linkedin.com/company/infoq/" rel="nofollow" target="_blank">https://www.linkedin.com/company/infoq/</a> - Facebook: <a href="https://www.facebook.com/InfoQdotcom/" rel="nofollow" target="_blank">https://www.facebook.com/InfoQdotcom/</a> - Instagram: @infoqdotcom - Youtube: <a href="https://www.youtube.com/infoq" rel="nofollow" target="_blank">https://www.youtube.com/infoq</a>

Francesca Lazzeri on Machine Learning for Time Series Forecasting

Show Notes(2:37) Francesca discussed her educational background in Italy, studying Economics and Institutional Studies at LUISS Guido Carli University for her Master’s and then Economics and Technology Innovation at Sant’Anna University for her Ph.D. She also mentioned her transition to studying in the US at Harvard Business School.(7:43) Francesca shared the anecdote behind going to HBS to pursue a Postdoc Research Fellowship in Economics. She also revealed the differences in the educational approaches between Italy and the United States.(15:15) During her Postdoc, Francesca worked on multiple patent data-driven projects to investigate and measure the impact of external knowledge networks on companies’ competitiveness and innovation. She discussed a specific project that analyzed biotech innovation in Boston, San Diego, and San Francisco clusters using social media and citation data.(24:26) Francesca talked about her decision to join Microsoft as a data scientist in its Cloud and Enterprise division back in 2014, where she first worked on projects for clients from the energy and finance sectors.(30:00) Francesca discussed the two types of customers who seek Microsoft’s cloud solutions to solve their data problems and explained the learning curves she went through while interacting with them.(36:11) Francesca unpacked the Healthy Data Science Organization Framework - which is a portfolio of methodologies, technologies, resources that will assist organizations in becoming more data-driven (Read her InfoQ article “The Data Science Mindset: 6 Principles to Build Healthy Data-Driven Organizations”).(45:31) Francesca shared the challenges of building end-to-end machine learning applications that she has observed from Microsoft Azure AI’s clients.(49:56) Francesca walked through a typical day in her current leadership role at Microsoft’s Cloud AI Advocates team.(53:44) Francesca discussed the different components in a typical Azure deployment workflow (Read her post “Azure Machine Learning Deployment Workflow”).(58:44) Francesca explained Automated Machine Learning, a breakthrough from Microsoft Research division that is essentially a recommender system for machine learning pipelines.(01:03:50) Francesca went over model interpretability features within Azure AI (as part of the InterpretML package) and touched on Microsoft’s Responsible AI principles.(01:08:01) Francesca explained the differences between model fairness and model interpretability at both the training time and inference time (Check out the Fairlearn package).(01:12:11) Francesca is currently writing a book with Wiley called “Machine Learning for Time Series Forecasting with Python.”(01:14:39) Francesca shared her advice for undergraduate students looking to get into the field, judging from her experience being a mentor for Ph.D. and Postdoc students at institutions such as Harvard, MIT, and Columbia.(01:17:27) Francesca reasoned how her educational backgrounds in economics and operations management contribute to her success in a data science career(01:20:09) Closing segment.Her Contact InfoTwitterMediumLinkedInHer Recommended ResourcesPeople To FollowHilary MasonAndrew NgHannah WallachBook To ReadAn Introduction to Probability Theory and Its Applications (by William Feller)A Developer’s Introduction to Data ScienceVideo series on Data Science and Machine Learning on AzureVideo series on Data Science and Machine Learning on Azure GitHub repoAzure Machine LearningAzure Machine Learning DocumentationAzure Machine Learning ServiceThe Data Science LifecycleAlgorithm Cheat SheetHow to Select Machine Learning AlgorithmsAzure Machine Learning DesignerResponsible Machine LearningResponsible Machine LearningModel InterpretabilityInterpretML RepoInterpretML ToolkitInterpretML DocumentationFairlearn ServiceFairlearn DocumentationAutomated Machine LearningAutomated Machine LearningAuto ML FeaturizationAutoML Config Class

Show Notes<ul><li>(2:37) Francesca discussed her educational background in Italy, studying Economics and Institutional Studies at <a href="http://www.luiss.it/" rel="nofollow" target="_blank">LUISS Guido Carli University</a> for her Master’s and then Economics and Technology Innovation at <a href="http://www.santannapisa.it/" rel="nofollow" target="_blank">Sant’Anna University</a> for her Ph.D. She also mentioned her transition to studying in the US at <a href="https://www.hbs.edu/" rel="nofollow" target="_blank">Harvard Business School</a>.</li><li>(7:43) Francesca shared the anecdote behind going to HBS to pursue a Postdoc Research Fellowship in Economics. She also revealed the differences in the educational approaches between Italy and the United States.</li><li>(15:15) During her Postdoc, Francesca worked on multiple patent data-driven projects to investigate and measure the impact of external knowledge networks on companies’ competitiveness and innovation. She discussed a specific project that analyzed biotech innovation in Boston, San Diego, and San Francisco clusters using social media and citation data.</li><li>(24:26) Francesca talked about her decision to join Microsoft as a data scientist in its <a href="https://news.microsoft.com/tag/cloud-and-enterprise-division/" rel="nofollow" target="_blank">Cloud and Enterprise division</a> back in 2014, where she first worked on projects for clients from the energy and finance sectors.</li><li>(30:00) Francesca discussed the two types of customers who seek Microsoft’s cloud solutions to solve their data problems and explained the learning curves she went through while interacting with them.</li><li>(36:11) Francesca unpacked the Healthy Data Science Organization Framework - which is a portfolio of methodologies, technologies, resources that will assist organizations in becoming more data-driven (Read her InfoQ article “<a href="https://www.infoq.com/articles/data-science-organization-framework/" rel="nofollow" target="_blank">The Data Science Mindset: 6 Principles to Build Healthy Data-Driven Organizations</a>”).</li><li>(45:31) Francesca shared the challenges of building end-to-end machine learning applications that she has observed from Microsoft Azure AI’s clients.</li><li>(49:56) Francesca walked through a typical day in her current leadership role at Microsoft’s <a href="https://developer.microsoft.com/en-us/advocates/" rel="nofollow" target="_blank">Cloud AI Advocates team</a>.</li><li>(53:44) Francesca discussed the different components in a typical Azure deployment workflow (Read her post “<a href="https://medium.com/microsoftazure/azure-machine-learning-deployment-workflow-475fc939e96" rel="nofollow" target="_blank">Azure Machine Learning Deployment Workflow</a>”).</li><li>(58:44) Francesca explained <a href="https://medium.com/microsoftazure/democratize-artificial-intelligence-with-automated-machine-learning-169b348a9509" rel="nofollow" target="_blank">Automated Machine Learning</a>, a breakthrough from Microsoft Research division that is essentially a recommender system for machine learning pipelines.</li><li>(01:03:50) Francesca went over <a href="https://docs.microsoft.com/en-us/azure/machine-learning/service/machine-learning-interpretability-explainability?WT.mc_id=azuremedium-blog-lazzeri" rel="nofollow" target="_blank">model interpretability</a> features within Azure AI (as part of the <a href="https://www.aka.ms/InterpretML-toolkit" rel="nofollow" target="_blank">InterpretML package</a>) and touched on Microsoft’s <a href="https://www.microsoft.com/en-us/ai/responsible-ai" rel="nofollow" target="_blank">Responsible AI principles</a>.</li><li>(01:08:01) Francesca explained the differences between model fairness and model interpretability at both the training time and inference time (Check out the <a href="https://www.aka.ms/FairlearnAI" rel="nofollow" target="_blank">Fairlearn package</a>).</li><li>(01:12:11) Francesca is currently writing a book with Wiley called “<a href="https://www.goodreads.com/book/show/53216243-machine-learning-for-time-series-forecasting-with-python" rel="nofollow" target="_blank">Machine Learning for Time Series Forecasting with Python</a>.”</li><li>(01:14:39) Francesca shared her advice for undergraduate students looking to get into the field, judging from her experience being a mentor for Ph.D. and Postdoc students at institutions such as Harvard, MIT, and Columbia.</li><li>(01:17:27) Francesca reasoned how her educational backgrounds in economics and operations management contribute to her success in a data science career</li><li>(01:20:09) Closing segment.</li></ul>Her Contact Info<ul><li><a href="https://twitter.com/frlazzeri" rel="nofollow" target="_blank">Twitter</a></li><li><a href="https://medium.com/@francescalazzeri" rel="nofollow" target="_blank">Medium</a></li><li><a href="https://www.linkedin.com/in/francescalazzeri/" rel="nofollow" target="_blank">LinkedIn</a></li></ul>Her Recommended ResourcesPeople To Follow<ul><li><a href="https://twitter.com/hmason" rel="nofollow" target="_blank">Hilary Mason</a></li><li><a href="https://twitter.com/andrewyng" rel="nofollow" target="_blank">Andrew Ng</a></li><li><a href="https://twitter.com/hannawallach" rel="nofollow" target="_blank">Hannah Wallach</a></li></ul>Book To Read<ul><li><a href="https://www.amazon.com/Introduction-Probability-Theory-Applications-Vol/dp/0471257087" rel="nofollow" target="_blank">An Introduction to Probability Theory and Its Applications</a> (by William Feller)</li></ul>A Developer’s Introduction to Data Science<ul><li><a href="https://channel9.msdn.com/Series/Dev-Intro-to-Data-Science" rel="nofollow" target="_blank">Video series on Data Science and Machine Learning on Azure</a></li><li><a href="https://www.aka.ms/DevIntroDS_GitHub" rel="nofollow" target="_blank">Video series on Data Science and Machine Learning on Azure GitHub repo</a></li></ul>Azure Machine Learning<ul><li><a href="https://www.aka.ms/AzureMLDoc" rel="nofollow" target="_blank">Azure Machine Learning Documentation</a></li><li><a href="https://www.aka.ms/AzureMLservice" rel="nofollow" target="_blank">Azure Machine Learning Service</a></li><li><a href="https://www.aka.ms/DataScienceLifecycle" rel="nofollow" target="_blank">The Data Science Lifecycle</a></li><li><a href="https://www.aka.ms/AlgorithmCheatSheet" rel="nofollow" target="_blank">Algorithm Cheat Sheet</a></li><li><a href="https://www.aka.ms/SelectAlgos" rel="nofollow" target="_blank">How to Select Machine Learning Algorithms</a></li><li><a href="https://www.aka.ms/azuremldesigner" rel="nofollow" target="_blank">Azure Machine Learning Designer</a></li></ul>Responsible Machine Learning<ul><li><a href="https://www.aka.ms/ResponsibleMLDoc" rel="nofollow" target="_blank">Responsible Machine Learning</a></li><li><a href="https://www.aka.ms/ModelInterpretability" rel="nofollow" target="_blank">Model Interpretability</a></li><li><a href="https://www.aka.ms/InterpretML-github" rel="nofollow" target="_blank">InterpretML Repo</a></li><li><a href="https://www.aka.ms/InterpretML-toolkit" rel="nofollow" target="_blank">InterpretML Toolkit</a></li><li><a href="https://www.aka.ms/InterpretML-doc" rel="nofollow" target="_blank">InterpretML Documentation</a></li><li><a href="https://www.aka.ms/FairlearnAI" rel="nofollow" target="_blank">Fairlearn Service</a></li><li><a href="https://www.aka.ms/FairlearnAIDoc" rel="nofollow" target="_blank">Fairlearn Documentation</a></li></ul>Automated Machine Learning<ul><li><a href="https://www.aka.ms/AutomatedML" rel="nofollow" target="_blank">Automated Machine Learning</a></li><li><a href="https://www.aka.ms/AutoMLfeaturization" rel="nofollow" target="_blank">Auto ML Featurization</a></li><li><a href="https://www.aka.ms/AutoMLConfig-Class" rel="nofollow" target="_blank">AutoML Config Class</a></li></ul>

Episode 43: From Economics and Operations Management to Data Science with Francesca Lazzeri