際際滷shows by User: hussainsultan / http://www.slideshare.net/images/logo.gif 際際滷shows by User: hussainsultan / Thu, 23 Jun 2022 21:24:44 GMT 際際滷Share feed for 際際滷shows by User: hussainsultan Ibis + Substrait.pdf /slideshow/ibis-substraitpdf/252048714 ibissubstrait-220623212444-d5e7c36b
Ibis and Substrait are driving standardization in analytics system]]>
Ibis and Substrait are driving standardization in analytics system]]> Thu, 23 Jun 2022 21:24:44 GMT /slideshow/ibis-substraitpdf/252048714 hussainsultan@slideshare.net(hussainsultan) Ibis + Substrait.pdf hussainsultan Ibis and Substrait are driving standardization in analytics system <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ibissubstrait-220623212444-d5e7c36b-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Ibis and Substrait are driving standardization in analytics system
Ibis + Substrait.pdf from Hussain Sultan
]]> 46 0 https://cdn.slidesharecdn.com/ss_thumbnails/ibissubstrait-220623212444-d5e7c36b-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 How to make your data scientists happy /slideshow/how-to-make-your-data-scientists-happy-93855635/93855635 howtomakeyourdatascientistshappy-vf-180414022716
Enabling data scientists within an enterprise requires a well-thought out approach from an organization, technology, and business results perspective. In this talk, Tim and Hussain will share common pitfalls to data science enablement in the enterprise and provide their recommendations to avoid them. Taking an example, actionable use case from the financial services industry, they will focus on how Anaconda plays a pivotal role in setting up big data infrastructure, integrating data science experimentation and production environments, and deploying insights to production. Along the way, they will highlight opportunities for leveraging open source and unleashing data science teams while meeting regulatory and compliance challenges.]]>
Enabling data scientists within an enterprise requires a well-thought out approach from an organization, technology, and business results perspective. In this talk, Tim and Hussain will share common pitfalls to data science enablement in the enterprise and provide their recommendations to avoid them. Taking an example, actionable use case from the financial services industry, they will focus on how Anaconda plays a pivotal role in setting up big data infrastructure, integrating data science experimentation and production environments, and deploying insights to production. Along the way, they will highlight opportunities for leveraging open source and unleashing data science teams while meeting regulatory and compliance challenges.]]> Sat, 14 Apr 2018 02:27:16 GMT /slideshow/how-to-make-your-data-scientists-happy-93855635/93855635 hussainsultan@slideshare.net(hussainsultan) How to make your data scientists happy hussainsultan Enabling data scientists within an enterprise requires a well-thought out approach from an organization, technology, and business results perspective. In this talk, Tim and Hussain will share common pitfalls to data science enablement in the enterprise and provide their recommendations to avoid them. Taking an example, actionable use case from the financial services industry, they will focus on how Anaconda plays a pivotal role in setting up big data infrastructure, integrating data science experimentation and production environments, and deploying insights to production. Along the way, they will highlight opportunities for leveraging open source and unleashing data science teams while meeting regulatory and compliance challenges. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/howtomakeyourdatascientistshappy-vf-180414022716-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Enabling data scientists within an enterprise requires a well-thought out approach from an organization, technology, and business results perspective. In this talk, Tim and Hussain will share common pitfalls to data science enablement in the enterprise and provide their recommendations to avoid them. Taking an example, actionable use case from the financial services industry, they will focus on how Anaconda plays a pivotal role in setting up big data infrastructure, integrating data science experimentation and production environments, and deploying insights to production. Along the way, they will highlight opportunities for leveraging open source and unleashing data science teams while meeting regulatory and compliance challenges.
How to make your data scientists happy from Hussain Sultan
]]> 170 3 https://cdn.slidesharecdn.com/ss_thumbnails/howtomakeyourdatascientistshappy-vf-180414022716-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Dask glm-scipy2017-final /slideshow/dask-glmscipy2017final/77805448 dask-glm-scipy2017-final-170712195451
Oftentimes data scientists have specific modeling problems that call for highly customized solutions, which can lead to writing new optimization routines. In this talk we will discuss writing large-scale optimization algorithms in Python. Starting from a quick review of the math behind convex optimization, we will implement some common algorithms with custom tweaks, first in NumPy and then at scale with Dask arrays. Leveraging the distributed dask scheduler, we will also look at asynchronous variants of these algorithms. While looking at these implementations, we will discuss the challenges of properly testing optimization routines. The focus will be on applications to large scale generalized linear models and will include a demo of the currently in-development dask-glm project. We will end with some benchmarks comparing dask-glm with the SciPy stack (statsmodels, scikit-learn) as well as other popular big data tools such as H20. This talk is written from the perspective of a data scientist, not a nuts-and-bolts computer scientist, and so is focused on customizing and extending the SciPy stack for large scale data science problems. This talk will be co-presented by Chris White (Capital One) and Hussain Sultan (AQN Strategies).]]>
Oftentimes data scientists have specific modeling problems that call for highly customized solutions, which can lead to writing new optimization routines. In this talk we will discuss writing large-scale optimization algorithms in Python. Starting from a quick review of the math behind convex optimization, we will implement some common algorithms with custom tweaks, first in NumPy and then at scale with Dask arrays. Leveraging the distributed dask scheduler, we will also look at asynchronous variants of these algorithms. While looking at these implementations, we will discuss the challenges of properly testing optimization routines. The focus will be on applications to large scale generalized linear models and will include a demo of the currently in-development dask-glm project. We will end with some benchmarks comparing dask-glm with the SciPy stack (statsmodels, scikit-learn) as well as other popular big data tools such as H20. This talk is written from the perspective of a data scientist, not a nuts-and-bolts computer scientist, and so is focused on customizing and extending the SciPy stack for large scale data science problems. This talk will be co-presented by Chris White (Capital One) and Hussain Sultan (AQN Strategies).]]> Wed, 12 Jul 2017 19:54:51 GMT /slideshow/dask-glmscipy2017final/77805448 hussainsultan@slideshare.net(hussainsultan) Dask glm-scipy2017-final hussainsultan Oftentimes data scientists have specific modeling problems that call for highly customized solutions, which can lead to writing new optimization routines. In this talk we will discuss writing large-scale optimization algorithms in Python. Starting from a quick review of the math behind convex optimization, we will implement some common algorithms with custom tweaks, first in NumPy and then at scale with Dask arrays. Leveraging the distributed dask scheduler, we will also look at asynchronous variants of these algorithms. While looking at these implementations, we will discuss the challenges of properly testing optimization routines. The focus will be on applications to large scale generalized linear models and will include a demo of the currently in-development dask-glm project. We will end with some benchmarks comparing dask-glm with the SciPy stack (statsmodels, scikit-learn) as well as other popular big data tools such as H20. This talk is written from the perspective of a data scientist, not a nuts-and-bolts computer scientist, and so is focused on customizing and extending the SciPy stack for large scale data science problems. This talk will be co-presented by Chris White (Capital One) and Hussain Sultan (AQN Strategies). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dask-glm-scipy2017-final-170712195451-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Oftentimes data scientists have specific modeling problems that call for highly customized solutions, which can lead to writing new optimization routines. In this talk we will discuss writing large-scale optimization algorithms in Python. Starting from a quick review of the math behind convex optimization, we will implement some common algorithms with custom tweaks, first in NumPy and then at scale with Dask arrays. Leveraging the distributed dask scheduler, we will also look at asynchronous variants of these algorithms. While looking at these implementations, we will discuss the challenges of properly testing optimization routines. The focus will be on applications to large scale generalized linear models and will include a demo of the currently in-development dask-glm project. We will end with some benchmarks comparing dask-glm with the SciPy stack (statsmodels, scikit-learn) as well as other popular big data tools such as H20. This talk is written from the perspective of a data scientist, not a nuts-and-bolts computer scientist, and so is focused on customizing and extending the SciPy stack for large scale data science problems. This talk will be co-presented by Chris White (Capital One) and Hussain Sultan (AQN Strategies).
Dask glm-scipy2017-final from Hussain Sultan
]]> 436 2 https://cdn.slidesharecdn.com/ss_thumbnails/dask-glm-scipy2017-final-170712195451-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-hussainsultan-48x48.jpg?cb=1656019299 I am interested in data science/software engineering and its applications in finance, credit risk, and valuations. I love making new professional acquaintances. Reach out if you want to talk technology, data or basketball. Specialties: Distributed Systems, Data Analysis, Machine Learning, Software Engineering, Agile Product Development https://cdn.slidesharecdn.com/ss_thumbnails/ibissubstrait-220623212444-d5e7c36b-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/ibis-substraitpdf/252048714 Ibis + Substrait.pdf https://cdn.slidesharecdn.com/ss_thumbnails/howtomakeyourdatascientistshappy-vf-180414022716-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/how-to-make-your-data-scientists-happy-93855635/93855635 How to make your data ... https://cdn.slidesharecdn.com/ss_thumbnails/dask-glm-scipy2017-final-170712195451-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/dask-glmscipy2017final/77805448 Dask glm-scipy2017-final