�ݺ�ߣshows by User: NishaTalagala

�ݺ�ߣshows by User: NishaTalagala / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: NishaTalagala / Mon, 24 Aug 2020 23:43:53 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: NishaTalagala Ml ops past_present_future /slideshow/ml-ops-pastpresentfuture/238201735 mlopspastpresentfuture-200824234353
Given at the MLOps. Summit 2020 - I cover the origins of MLOps in 2018, how MLOps has evolved from 2018 to 2020, and what I expect for the future of MLOps]]>
Given at the MLOps. Summit 2020 - I cover the origins of MLOps in 2018, how MLOps has evolved from 2018 to 2020, and what I expect for the future of MLOps]]> Mon, 24 Aug 2020 23:43:53 GMT /slideshow/ml-ops-pastpresentfuture/238201735 NishaTalagala@slideshare.net(NishaTalagala) Ml ops past_present_future NishaTalagala Given at the MLOps. Summit 2020 - I cover the origins of MLOps in 2018, how MLOps has evolved from 2018 to 2020, and what I expect for the future of MLOps <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mlopspastpresentfuture-200824234353-thumbnail.jpg?width=120&height=120&fit=bounds" /> Given at the MLOps. Summit 2020 - I cover the origins of MLOps in 2018, how MLOps has evolved from 2018 to 2020, and what I expect for the future of MLOps

Ml ops past_present_future from Nisha Talagala

]]> 506 0 https://cdn.slidesharecdn.com/ss_thumbnails/mlopspastpresentfuture-200824234353-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 Storage Challenges for Production Machine Learning /slideshow/storage-challenges-for-production-machine-learning/166089887 ntalagalafms2019v3-190824155250
Intersection of Storage and Machine Learning. The storage problems that AI/ML faces and how to use AI/ML to improve storage]]>
Intersection of Storage and Machine Learning. The storage problems that AI/ML faces and how to use AI/ML to improve storage]]> Sat, 24 Aug 2019 15:52:50 GMT /slideshow/storage-challenges-for-production-machine-learning/166089887 NishaTalagala@slideshare.net(NishaTalagala) Storage Challenges for Production Machine Learning NishaTalagala Intersection of Storage and Machine Learning. The storage problems that AI/ML faces and how to use AI/ML to improve storage <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ntalagalafms2019v3-190824155250-thumbnail.jpg?width=120&height=120&fit=bounds" /> Intersection of Storage and Machine Learning. The storage problems that AI/ML faces and how to use AI/ML to improve storage

Storage Challenges for Production Machine Learning from Nisha Talagala

]]> 203 1 https://cdn.slidesharecdn.com/ss_thumbnails/ntalagalafms2019v3-190824155250-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 Msst 2019 v4 /slideshow/msst-2019-v4/146958031 msst2019v4-190521191835
Storage and Data challenges for production machine learning.]]>
Storage and Data challenges for production machine learning.]]> Tue, 21 May 2019 19:18:35 GMT /slideshow/msst-2019-v4/146958031 NishaTalagala@slideshare.net(NishaTalagala) Msst 2019 v4 NishaTalagala Storage and Data challenges for production machine learning. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/msst2019v4-190521191835-thumbnail.jpg?width=120&height=120&fit=bounds" /> Storage and Data challenges for production machine learning.

Msst 2019 v4 from Nisha Talagala

]]> 226 2 https://cdn.slidesharecdn.com/ss_thumbnails/msst2019v4-190521191835-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 Global ai conf_final /NishaTalagala/global-ai-conffinal globalaiconffinal-190427004540
Building trustworthy and effective AI solutions. - Many cloud vendor AI services (AWS, GCP, Azure) - Demo of a workflow with AWS Sagemaker - What is AI Trust - What is explainability - How to add this to a workflow with S3, Sagemaker, Lambda (server less) and Postman]]>
Building trustworthy and effective AI solutions. - Many cloud vendor AI services (AWS, GCP, Azure) - Demo of a workflow with AWS Sagemaker - What is AI Trust - What is explainability - How to add this to a workflow with S3, Sagemaker, Lambda (server less) and Postman]]> Sat, 27 Apr 2019 00:45:40 GMT /NishaTalagala/global-ai-conffinal NishaTalagala@slideshare.net(NishaTalagala) Global ai conf_final NishaTalagala Building trustworthy and effective AI solutions. - Many cloud vendor AI services (AWS, GCP, Azure) - Demo of a workflow with AWS Sagemaker - What is AI Trust - What is explainability - How to add this to a workflow with S3, Sagemaker, Lambda (server less) and Postman <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/globalaiconffinal-190427004540-thumbnail.jpg?width=120&height=120&fit=bounds" /> Building trustworthy and effective AI solutions. - Many cloud vendor AI services (AWS, GCP, Azure) - Demo of a workflow with AWS Sagemaker - What is AI Trust - What is explainability - How to add this to a workflow with S3, Sagemaker, Lambda (server less) and Postman

Global ai conf_final from Nisha Talagala

]]> 159 3 https://cdn.slidesharecdn.com/ss_thumbnails/globalaiconffinal-190427004540-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 Rest microservice ml_deployment_ntalagala_ai_conf_2019 /slideshow/rest-microservice-mldeploymentntalagalaaiconf2019/129414932 restmicroservicemldeploymentntalagalaaiconf2019-190127013348
Simple design pattern for production grade ML deployment as REST based micro service with scikit learn, python, docker containers and kubernetes ]]>
Simple design pattern for production grade ML deployment as REST based micro service with scikit learn, python, docker containers and kubernetes ]]> Sun, 27 Jan 2019 01:33:48 GMT /slideshow/rest-microservice-mldeploymentntalagalaaiconf2019/129414932 NishaTalagala@slideshare.net(NishaTalagala) Rest microservice ml_deployment_ntalagala_ai_conf_2019 NishaTalagala Simple design pattern for production grade ML deployment as REST based micro service with scikit learn, python, docker containers and kubernetes <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/restmicroservicemldeploymentntalagalaaiconf2019-190127013348-thumbnail.jpg?width=120&height=120&fit=bounds" /> Simple design pattern for production grade ML deployment as REST based micro service with scikit learn, python, docker containers and kubernetes

Rest microservice ml_deployment_ntalagala_ai_conf_2019 from Nisha Talagala

]]> 186 1 https://cdn.slidesharecdn.com/ss_thumbnails/restmicroservicemldeploymentntalagalaaiconf2019-190127013348-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 Pm.ais ummit 180917 final /slideshow/pmais-ummit-180917-final/115843948 pm-180921195258
MLOps: From Data Science to Business ROI This deck describes why operationalizing ML (running ML and DL in production and managing the full production lifecycle) is challenging. We also describe MCenter and how it manages the ML lifecycle]]>
MLOps: From Data Science to Business ROI This deck describes why operationalizing ML (running ML and DL in production and managing the full production lifecycle) is challenging. We also describe MCenter and how it manages the ML lifecycle]]> Fri, 21 Sep 2018 19:52:57 GMT /slideshow/pmais-ummit-180917-final/115843948 NishaTalagala@slideshare.net(NishaTalagala) Pm.ais ummit 180917 final NishaTalagala MLOps: From Data Science to Business ROI This deck describes why operationalizing ML (running ML and DL in production and managing the full production lifecycle) is challenging. We also describe MCenter and how it manages the ML lifecycle <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pm-180921195258-thumbnail.jpg?width=120&height=120&fit=bounds" /> MLOps: From Data Science to Business ROI This deck describes why operationalizing ML (running ML and DL in production and managing the full production lifecycle) is challenging. We also describe MCenter and how it manages the ML lifecycle

Pm.ais ummit 180917 final from Nisha Talagala

]]> 227 1 https://cdn.slidesharecdn.com/ss_thumbnails/pm-180921195258-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 Fms invited talk_2018 v5 /slideshow/fms-invited-talk2018-v5/110699392 fmsinvitedtalk2018v5-180820162816
Invited talk at Flash Memory Summit on the intersection of Machine Learning and Storage]]>
Invited talk at Flash Memory Summit on the intersection of Machine Learning and Storage]]> Mon, 20 Aug 2018 16:28:16 GMT /slideshow/fms-invited-talk2018-v5/110699392 NishaTalagala@slideshare.net(NishaTalagala) Fms invited talk_2018 v5 NishaTalagala Invited talk at Flash Memory Summit on the intersection of Machine Learning and Storage <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fmsinvitedtalk2018v5-180820162816-thumbnail.jpg?width=120&height=120&fit=bounds" /> Invited talk at Flash Memory Summit on the intersection of Machine Learning and Storage

Fms invited talk_2018 v5 from Nisha Talagala

]]> 113 2 https://cdn.slidesharecdn.com/ss_thumbnails/fmsinvitedtalk2018v5-180820162816-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 Strata parallel m-ml-ops_sept_2017 /slideshow/strata-parallel-mmlopssept2017-80541138/80541138 strata-parallelm-mlopssept2017-171006190737
Machine Learning in Production The era of big data generation is upon us. Devices ranging from sensors to robots and sophisticated applications are generating increasing amounts of rich data (time series, text, images, sound, video, etc.). For such data to benefit a business’s bottom line, insights must be extracted, a process that increasingly requires machine learning (ML) and deep learning (DL) approaches deployed in production applications use cases. Production ML is complicated by several challenges, including the need for two very distinct skill sets (operations and data science) to collaborate, the inherent complexity and uniqueness of ML itself, when compared to other apps, and the varied array of analytic engines that need to be combined for a practical deployment, often across physically distributed infrastructure. Nisha Talagala shares solutions and techniques for effectively managing machine learning and deep learning in production with popular analytic engines such as Apache Spark, TensorFlow, and Apache Flink.]]>
Machine Learning in Production The era of big data generation is upon us. Devices ranging from sensors to robots and sophisticated applications are generating increasing amounts of rich data (time series, text, images, sound, video, etc.). For such data to benefit a business’s bottom line, insights must be extracted, a process that increasingly requires machine learning (ML) and deep learning (DL) approaches deployed in production applications use cases. Production ML is complicated by several challenges, including the need for two very distinct skill sets (operations and data science) to collaborate, the inherent complexity and uniqueness of ML itself, when compared to other apps, and the varied array of analytic engines that need to be combined for a practical deployment, often across physically distributed infrastructure. Nisha Talagala shares solutions and techniques for effectively managing machine learning and deep learning in production with popular analytic engines such as Apache Spark, TensorFlow, and Apache Flink.]]> Fri, 06 Oct 2017 19:07:37 GMT /slideshow/strata-parallel-mmlopssept2017-80541138/80541138 NishaTalagala@slideshare.net(NishaTalagala) Strata parallel m-ml-ops_sept_2017 NishaTalagala Machine Learning in Production The era of big data generation is upon us. Devices ranging from sensors to robots and sophisticated applications are generating increasing amounts of rich data (time series, text, images, sound, video, etc.). For such data to benefit a business’s bottom line, insights must be extracted, a process that increasingly requires machine learning (ML) and deep learning (DL) approaches deployed in production applications use cases. Production ML is complicated by several challenges, including the need for two very distinct skill sets (operations and data science) to collaborate, the inherent complexity and uniqueness of ML itself, when compared to other apps, and the varied array of analytic engines that need to be combined for a practical deployment, often across physically distributed infrastructure. Nisha Talagala shares solutions and techniques for effectively managing machine learning and deep learning in production with popular analytic engines such as Apache Spark, TensorFlow, and Apache Flink. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/strata-parallelm-mlopssept2017-171006190737-thumbnail.jpg?width=120&height=120&fit=bounds" /> Machine Learning in Production The era of big data generation is upon us. Devices ranging from sensors to robots and sophisticated applications are generating increasing amounts of rich data (time series, text, images, sound, video, etc.). For such data to benefit a business’s bottom line, insights must be extracted, a process that increasingly requires machine learning (ML) and deep learning (DL) approaches deployed in production applications use cases. Production ML is complicated by several challenges, including the need for two very distinct skill sets (operations and data science) to collaborate, the inherent complexity and uniqueness of ML itself, when compared to other apps, and the varied array of analytic engines that need to be combined for a practical deployment, often across physically distributed infrastructure. Nisha Talagala shares solutions and techniques for effectively managing machine learning and deep learning in production with popular analytic engines such as Apache Spark, TensorFlow, and Apache Flink.

Strata parallel m-ml-ops_sept_2017 from Nisha Talagala

]]> 494 4 https://cdn.slidesharecdn.com/ss_thumbnails/strata-parallelm-mlopssept2017-171006190737-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 Parallel machines flinkforward2017 /NishaTalagala/parallel-machines-flinkforward2017 parallelmachinesflinkforward2017-170412154920
Experiences with Streaming and Micro-Batch for Online Machine Learning]]>
Experiences with Streaming and Micro-Batch for Online Machine Learning]]> Wed, 12 Apr 2017 15:49:20 GMT /NishaTalagala/parallel-machines-flinkforward2017 NishaTalagala@slideshare.net(NishaTalagala) Parallel machines flinkforward2017 NishaTalagala Experiences with Streaming and Micro-Batch for Online Machine Learning <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/parallelmachinesflinkforward2017-170412154920-thumbnail.jpg?width=120&height=120&fit=bounds" /> Experiences with Streaming and Micro-Batch for Online Machine Learning

Parallel machines flinkforward2017 from Nisha Talagala

]]> 691 4 https://cdn.slidesharecdn.com/ss_thumbnails/parallelmachinesflinkforward2017-170412154920-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 Nisha talagala keynote_inflow_2016 /slideshow/nisha-talagala-keynoteinflow2016/72065053 nishatalagalakeynoteinflow2016-170212210015
INFLOW Keynote 2016]]>
INFLOW Keynote 2016]]> Sun, 12 Feb 2017 21:00:15 GMT /slideshow/nisha-talagala-keynoteinflow2016/72065053 NishaTalagala@slideshare.net(NishaTalagala) Nisha talagala keynote_inflow_2016 NishaTalagala INFLOW Keynote 2016 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nishatalagalakeynoteinflow2016-170212210015-thumbnail.jpg?width=120&height=120&fit=bounds" /> INFLOW Keynote 2016

Nisha talagala keynote_inflow_2016 from Nisha Talagala

]]> 614 3 https://cdn.slidesharecdn.com/ss_thumbnails/nishatalagalakeynoteinflow2016-170212210015-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-NishaTalagala-48x48.jpg?cb=1598312569 I am the CEO of Pyxeda AI. Previously, I co-founded ParallelM and defined MLOps (Production Machine Learning and Deep Learning). I am also the co-chair for USENIX OpML 2019 - the first conference dedicated to production AI deployment and management. My background is in distributed software, systems and applications. Prior to PM, I was Lead Architect/Fellow at Fusion-io (acquired by SanDisk), Architect at Intel, and CTO at Gear6. I got my PhD from UC Berkeley in Computer Science. https://cdn.slidesharecdn.com/ss_thumbnails/mlopspastpresentfuture-200824234353-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/ml-ops-pastpresentfuture/238201735 Ml ops past_present_fu... https://cdn.slidesharecdn.com/ss_thumbnails/ntalagalafms2019v3-190824155250-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/storage-challenges-for-production-machine-learning/166089887 Storage Challenges for... https://cdn.slidesharecdn.com/ss_thumbnails/msst2019v4-190521191835-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/msst-2019-v4/146958031 Msst 2019 v4