Podcasts about rajat monga

In this special guest episode of the Effortless Podcast, Amit Prakash sits down with Rajat Monga, the creator of TensorFlow and current Corporate Vice President of Engineering at Microsoft. With a career spanning Google Brain, founding Inference, and leading AI inferencing at Microsoft, Rajat offers a unique perspective on the evolution of AI. The conversation dives into TensorFlow's revolutionary impact, the challenges of building startups, the rise of PyTorch, the future of inferencing, and how transformative tools like GPT-4 and OpenAI's Gemini are reshaping the AI landscape.Key Topics and Chapter Markers:Introduction to Rajat Monga & TensorFlow Legacy [0:00]The inflection points in AI: TensorFlow's role and challenges [6:00]PyTorch vs. TensorFlow: A tale of shifting paradigms [16:00]The startup journey: Building Inference and lessons learned [27:00]Exploring O1 and advancements in reasoning frameworks [54:00]AI inference: Cost optimizations and hardware innovations [57:00]Agents, trust, and validation: AI in decision-making workflows [1:05:00]Rajat's personal journey: Tools for resilience and finding balance [1:20:00] Host:Amit Prakash: Co-founder and CTO at ThoughtSpot, formerly at Google AdSense and Bing, and a PhD in Computer Engineering. Amit has a strong track record in analytics, machine learning, and large-scale systems. Follow Amit on:LinkedIn - https://www.linkedin.com/in/amit-prakash-50719a2/  X (Twitter) - https://x.com/amitp42 Guest:Rajat Monga: He is a pioneer in the AI industry, best known as the co-creator of TensorFlow. He has held senior roles at Google Brain and Microsoft, shaping the foundational tools that power today's AI systems. Rajat also co-founded Inference, a startup focused on anomaly detection in data analytics. At Microsoft, he leads AI software engineering, advancing inferencing infrastructure for the next generation of AI applications. He holds a Btech Degree from IIT, Delhi. Follow Rajat on:LinkedIn - https://www.linkedin.com/in/rajatmonga/  X (Twitter) - https://twitter.com/rajatmonga Share Your Thoughts: Have questions or comments? Drop us a mail at EffortlessPodcastHQ@gmail.com Email: EffortlessPodcastHQ@gmail.com

The artificial intelligence Lexman interviews Rajat Monga about his work on jubas, the smallest kind of aliens. They discuss the fascinating biology and physics of these little creatures, and how their bizarre behavior sheds new light on the creation of complex life forms.

Lexman interviews Rajat Monga, a chapeltarian and sabatoner who argues that chapels promote hypnotizability among the masses, and that bootleggers provide a necessary service in combating local sabatanators.

Lexman and Rajat talk about Hursts, Elevons and Lempiras.

Eric Anderson (@ericmander) and Simba Khadder (@simba_khadder) explore Featureform, the “virtual” feature store platform that aims to standardize data pipelines for machine learning. Contributor is no stranger to feature stores, but Simba has a broader definition than most. Join us to learn how Featureform enables data scientists and machine learning practitioners to solve a common, but rarely addressed organizational problem. Subscribe to Contributor on Substack for email notifications, and join our Slack community! In this episode we discuss: How there is no standard or north star for MLOps Why enterprise is where Featureform's value shines MLPlatform problems vs MLOps problems Why copy/paste and Git don't cut it Deploying MLOps solutions that make data scientists and everyone else happy Links: Featureform Terraform Apache Spark Feathr Other episodes: Tensorflow with Rajat Monga

In this episode, Lexman interviews Rajat Monga, a paralyzer who discusses his experiences at an orphanage and his Polysyndeton habit.

In this episode, Lexman interviews Rajat Monga, a prosthodontist and tergiversator. They discuss Samoyeds, Deneb, and ghat.

Lexman discusses Bryophytes, the different types of stockcars, why aristas are interesting creatures, and the role ochre plays in the natural world.

Lexman is back and he's discussing the topic of Renegotiation with Rajat Monga. They discuss the different types of renegotiation and what they involve. Finally, they discuss photosynthesis and avosets.

In this week's episode, Lexman comes face-to-face with a cunning diversionist.

In this episode, Lexman interviews Rajat Monga, a professor of French literature at UC Berkeley. They discuss the influence of the late American poet Allen Ginsberg on Rajat's work, as well as Allen's penchant for cruising and reheating meals.

Martinex interviews Rajat Monga, a infielder for the Colts in the MLB. They discuss his playing career and how his hoofprints have helped him achieve success.

Rajat Monga, author of Consistencies, joins the show to talk about the book and its discontents. He argues that when we always do the same thing, we become complacent and our work becoomes stale. He tells an unusual and disgusting habanera story, and then describes some of the weird rocks in his backyard.

Eric Anderson (@ericmander) reunites with old colleagues Kenn Knowles (@KennKnowles) and Pablo Estrada (@polecitoem) for a conversation on Apache Beam, the open-source programming model for data processing. The trio once worked together at Google, and Beam was a turning point in the history of open-source there. Today, both Kenn and Pablo are members of the Beam PMC, and join the show with the inside scoop on Beam's past, present and future. In this episode we discuss: Transitioning Beam to the Apache Way How “inner source” works at Google Thoughts on the relationship between batch processing and streaming Some ways that community “power users” have contributed to Beam Information on Beam Summit 2022, the first onsite summit since COVID began The first few people to register can use code BEAM_POD_INV for a discount on tickets! Links: Apache Beam Apache Spark Apache Flink Apache Nemo Apache Samza Apache Crunch MapReduce paper MillWheel paper FlumeJava paper Dataflow paper Beam Summit 2022 Website Other episodes: TensorFlow with Rajat Monga

Eric Anderson (@ericmander) meets with Davit Buniatyan (@DBuniatyan) of Activeloop, the database for AI. Davit was inspired to found Activeloop while working on large datasets in a neuroscience research lab at Princeton. Powering the technology at Activeloop is Hub, the open-source dataset format for AI applications. Join us to learn how Hub promises to enhance and expand various verticals in deep learning. In this episode we discuss: Reconfiguring traditional ML tooling for the cloud Connectomics - working with thin slices of a mouse brain with neuroscientist Sebastian Seung Choosing between university, a start-up, and open-source Davit's original product, that ran computation on crypto mining GPUs on a distributed scale Focusing on different data modalities for computer vision Links: Activeloop Activeloop Hub Apache Parquet Apache Spark TensorFlow Snowflake Databricks Timescale People mentioned: Sebastian Seung (@SebastianSeung) Other episodes: TensorFlow with Rajat Monga

Eric Anderson (@ericmander) sits down with Pete Goddard (@pete_paco) to talk about Deephaven, the open-core query engine built for real-time streams and batch data. Pete is the CEO of Deephaven Data Labs, and comes to the data world from a background in capital markets trading. Deephaven originally addressed a need for real-time data infrastructure in the finance world, but the team realized how useful their technology could be in a wider variety of verticals. Join us for Pete's unique perspective on reaching out into alternate industries and use cases through community development. In this episode we discuss: How Pete transitioned from Wall Street to open-source software Selling investors on open-source Two questions people always ask Pete The luxury of Deephaven's incremental update model Barrage, Deephaven's API for streaming tables that extends Apache Arrow Flight Links: Deephaven Barrage Apache Kafka Apache Arrow Flight Eclipse Jetty Other episodes: TensorFlow with Rajat Monga

TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that TensorFlow achieves for several real-world applications. 2016: Martín Abadi, P. Barham, Jianmin Chen, Z. Chen, Andy Davis, J. Dean, M. Devin, S. Ghemawat, Geoffrey Irving, M. Isard, M. Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, D. Murray, Benoit Steiner, P. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, Xiaoqian Zhang TensorFlow, Machine learning, Dataflow, Algorithm, Reinforcement learning, Programming model, Data-flow analysis, Fault tolerance, Scheduling (computing), Memory management, Open-source software, Computation, Server (computing), Weak consistency, Distributed computing, Deep learning, Repository (version control), Experiment, Requirement, Mathematical optimization, User space, Multi-core processor, Scalability, Immutable object, Application-specific integrated circuit, Graphics processing unit, Central processing unit, Map, Artificial neural network, Processor register https://arxiv.org/pdf/1605.08695v2.pdf

Eric Anderson (@ericmander) welcomes Peter Wang (@pwang) for a conversation about the Python ecosystem and the open-source communities that have built it. Peter is the creator of Anaconda, the near-essential Python distribution for scientific computing that makes managing packages a lot more manageable. In today’s episode, Peter offers a unique and powerful perspective on how to make the economics of open-source work for everyone. In this episode we discuss: The paradox of the PVM and Python’s packaging difficulties How Guido van Rossum implied permission for Anaconda and the open-source Python movement Python as the lingua franca of a new professional class Looking to Roblox for inspiration for a scientific computing creator community Giving back to open-source communities through the NumFOCUS Foundation Links: Anaconda NumFOCUS NumPy SciPy Enthought Jupyter TensorFlow MicroPython scikit-learn pandas Quansight Red Hat Roblox People mentioned: Travis Oliphant (@teoliphant) Fernando Pérez (@fperez_org) Brian Granger (@ellisonbg) Min Ragan-Kelley (@minrk) Guido van Rossum (@gvanrossum) James Currier (@JamesCurrier) Other episodes: NumPy & SciPy with Travis Oliphant TensorFlow with Rajat Monga

Eric Anderson (@ericmander) and Travis Oliphant (@teoliphant) take a far-reaching tour through the history of the Python data community. Travis has had a hand in the creation of many open-source projects, most notably the influential libraries, NumPy and SciPy, which helped cement Python as the standard for scientific computing. Join us for the story of a fledgling community from a time “before open-source was cool,” and their lessons for today’s open-source landscape. In this episode we discuss: How biomedical engineering, MRIs, and an unhappy tenure committee led to NumPy and SciPy Overcoming early challenges of distribution with Python What Travis would have done differently when he wrote NumPy Successfully solving the “two-option split” by adding a third option Community-driven open-source interacting with company-backed open-source Links: NumPy SciPy Anaconda Quansight Conda Matplotlib Enthought TensorFlow PyTorch MXNet PyPi Jupyter pandas People mentioned: Guido van Rossum (@gvanrossum) Robert Kern (Github: @rkern) Pearu Peterson (Github: @pearu) Wes McKinney (@wesmckinn) Charles Harris (Github: @charris) Francesc Alted (@francescalted) Fernando Perez (@fperez_org) Brian Granger (@ellisonbg) Other episodes: TensorFlow with Rajat Monga

Eric Anderson (@ericmander) is joined by Rajat Monga (@rajatmonga), a co-creator of TensorFlow. Originally developed by the Google Brain team, TensorFlow is now one of the most popular open-source libraries for machine learning. The team at TensorFlow seek to “democratize” the world of AI as we know it, and by all accounts, they are succeeding. Listen to today’s episode to get inside one of the largest and most exciting open-source projects of the decade. In this episode we discuss: How TensorFlow compares to other open-source projects at Google Taking bets on launch day numbers Balancing the demands of different kinds of TensorFlow users Lessons from Keras and PyTorch Links: TensorFlow Keras PyTorch Kafka Kubernetes MapReduce: Simplified Data Processing on Large Clusters Bigtable: A Distributed Storage System for Structured Data People mentioned: Jeff Dean (@JeffDean) Andrew Ng (@AndrewYNg) François Chollet (@fchollet)

In this episode of the Data Exchange I speak with Rajat Monga, one of the founding members of the TensorFlow Engineering team. Up until recently Rajat was the engineering manager for TensorFlow at Google. Our conversation spanned many topics, including:TFX, a production scale machine learning platform based on TensorFlow.Distributed training.MLIR (Multi-Level Intermediate Representation), “a representation format and library of compiler utilities that sits between the model representation and low-level compilers/executors that generate hardware-specific code.”Deep learning in the enterprise.The state of machine learning infrastructure.[full show notes can be found on the Data Exchange web site.]

Rajat Monga is an Engineering Director at Google, leading the TensorFlow team. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations.

Rajat Monga is a director of engineering at Google where he works on TensorFlow. TensorFlow is a framework for numerical computation developed at Google. The majority of TensorFlow users are building machine learning applications such as image recognition, recommendation systems, and natural language processing–but TensorFlow is actually applicable to a broader range of scientific computation The post TensorFlow Applications with Rajat Monga appeared first on Software Engineering Daily.

The age of highly accessible, open source machine learning tools is upon us. Farmers to large enterprises are getting into the game of turning their large data sets into actionable insights thanks to open source solutions like TensorFlow. Today on STACK That, we’re joined by TensorFlow’s Director of Engineering, Rajat Monga, to discuss how machine learning is going mainstream.

TensorFlow is Google’s open source machine learning library. Rajat Monga is the engineering director for TensorFlow. In this episode, we cover how to use TensorFlow, including an example of how to build a machine learning model to identify whether a picture contains a cat or not. TensorFlow was built with the mission of simplifying the The post TensorFlow in Practice with Rajat Monga appeared first on Software Engineering Daily.