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🚀 Java devs doing ML inference: Check out how InMobi harnessed SIMD Vector API + FMA for dense layers—up to 10x faster than scalar, outpacing…
🚀 Java devs doing ML inference: Check out how InMobi harnessed SIMD Vector API + FMA for dense layers—up to 10x faster than scalar, outpacing…
Liked by Ankit Kailaswar
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𝐁𝐨𝐚𝐫𝐝 𝐌𝐞𝐦𝐛𝐞𝐫𝐬 & 𝐂𝐗𝐎𝐬 | 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐀𝐥𝐞𝐫𝐭 ❗ Making others happy doesn’t require grand gestures. It’s the small things…
𝐁𝐨𝐚𝐫𝐝 𝐌𝐞𝐦𝐛𝐞𝐫𝐬 & 𝐂𝐗𝐎𝐬 | 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐀𝐥𝐞𝐫𝐭 ❗ Making others happy doesn’t require grand gestures. It’s the small things…
Liked by Ankit Kailaswar
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To all Amazon folks if you are impacted in recent layoffs in #Bangalore, we have all kinds of role at InMobi and Glance . Please do reach out to us .
To all Amazon folks if you are impacted in recent layoffs in #Bangalore, we have all kinds of role at InMobi and Glance . Please do reach out to us .
Liked by Ankit Kailaswar
Experience & Education
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XR Extreme Reach
View Ankit’s full experience
See their title, tenure and more.
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Projects
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Apache Falcon (Commiter)
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See projectFalcon is a feed processing and feed management system aimed at making it easier for end consumers to onboard their feed processing and feed management on hadoop clusters.
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Apache Hive (Commiter)
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Apache Lens (Contributer)
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See projectLens provides a Unified Analytics interface. Lens aims to cut the Data Analytics silos by providing a single view of data across multiple-tiered data stores and an optimal execution environment for the analytical query. It seamlessly integrates Hadoop with traditional data warehouses to appear like one.
At a high level, the project provides these features -
Simple metadata layer which provides an abstract view over tiered data stores
Single shared schema server based on the…Lens provides a Unified Analytics interface. Lens aims to cut the Data Analytics silos by providing a single view of data across multiple-tiered data stores and an optimal execution environment for the analytical query. It seamlessly integrates Hadoop with traditional data warehouses to appear like one.
At a high level, the project provides these features -
Simple metadata layer which provides an abstract view over tiered data stores
Single shared schema server based on the Hive Metastore - This schema is shared by data pipelines (HCatalog) and analytics applications.
OLAP Cube QL which is a high-level SQL like language to query and describe data sets organized in data cubes.
A JDBC driver and Java client libraries to issue queries, and a CLI for ad hoc queries.
Lens application server - a REST server which allows users to query data, make schema changes, schedule queries and enforce quota limits on queries.
The driver-based architecture allows plugging in reporting systems like Hive, Columnar data warehouses, Redshift etc.
Cost-based engine selection - allows optimal use of resources by selecting the best execution engine for a given query based on the query cost. -
WebCrawler-Pagerank
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Given a root URL and depth limit the crawler starts the crawling by fetching the page at the root URL and processing it. The processing of the page is basically composed of 2 tasks:
Calculating the page rank (more on that in the next section).
Crawling (fetching and processing) its statically linked pages until the depth limit is reached.
Languages
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English
Full professional proficiency
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Hindi
Full professional proficiency
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