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🔥 Enrol for FREE Cloud Computing Course & Get your Completion Certificate: https://www.simplilearn.com/skillup-free-online-courses?utm_campaign=Skillup-CloudComputing&utm_medium=DescriptionFirstFold&utm_source=youtube The Google Cloud Platform Tutorial video will cover all the important concepts of Google Cloud Platform. Google Cloud Platform is a set of cloud computing services provided by Google that runs on the same infrastructure that Google uses for its end-user products like, YouTube, Gmail and more. In this video, we will be looking into what is GCP? GCP tutorial, AWS vs GCP, GCP web hosting, Google cloud ML, GCP fundamentals, Google Cloud Platform Fundamentals (CP100A) Certification Training. Below are the topics we will be discussing in the video today: 00:00:00 What is GCP? 01:32:37 GCP Tutorial 03:08:50 AWS vs GCP 03:17:03 GCP Web hosting 03:36:14 Google Cloud ML 03:38:38 GCP Fundamentals (CP100A) Certification Training ✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH ⏩ Check out the Google Cloud Platform(GCP) tutorial videos: https://bit.ly/35r8IPg #GoogleCloudPlatformTutorial #GoogleCloudPlatoformFullCourse #GoogleCloudTraining #GoogleCloudPlatformTutorialForBeginners #GoogleCloudCertification #GoogleCloudTutorial #GCPTutorial #Simplilearn Simplilearn’s Google Cloud Platform (GCP) Architect certification program will empower you with the skills needed to advance your career in cloud architecture and become a certified Google Professional Cloud Architect. The course covers IAM, Networking, cloud storage, and much more. Simplilearn’s online Google Cloud Platform (GCP) Architect certification course introduces you to the flexible infrastructure and platform services provided by Google Cloud Platform. In this course, you will learn how to analyze and deploy infrastructure components such as networks, storage systems, and application services. Key Features: 1. 100% Money Back Guarantee 2. [More]
SAP Chief Technology Officer and Executive Board Member Juergen Mueller takes the stage at #SAPTechEd Las Vegas 2019, to discuss leading concepts and #technologies of the intelligent enterprise including key end-to-end #business processes infused with #intelligence. Together with his guests, Juergen shows how SAP is evolving the digital platform into a business technology platform, ensuring out-of-the-box integration, modularity, ease of extension, and consistent experience across business processes in an intelligent enterprise. http://sap.to/60571GKGl
Neuromation. Distributed Synthetic Data Platform for Deep Learning Applications: https://neuromation.io
What is the best platform to learn skills? Is it udemy, is it coursera or is it skillshare? In this video I talk about the best mooc platforms that you can use to learn skills. The most important thing to understand here is that, there is no one platform that you can use to learn everything. So, if you want to learn web dev, app dev, intro to coding, social media marketing, digital marketing, go with udemy. If you wanna learn machine learning, deep learning, finance, or soft skills like negotiating with people, you can go with coursera. And if you want to learn some creative skills like photography, drawing, illustration, graphic design, go with skillshare. udemy: https://www.udemy.com/ coursera: https://www.coursera.org/ skillshare: https://www.skillshare.com/ The startup I’m working for: codedamn: https://codedamn.com/ Let me know which platform do you enjoy using to learn skills down below! If you want to learn how to get online courses for free or at discounted prices, check out these videos: How To Get Paid Coursera Course Certificates For FREE in 2020?!🔥 | Step by Step | Complete Guide!: https://youtu.be/LywLyuO6OPY 70 FREE Courses By Harvard University🔥🔥 | How To Enroll | Available For Limited Time!: https://youtu.be/Wbq7qEaTvVE Google is Offering 134 FREE Courses!🔥 | How to Sign Up | Google Digital Garage Explained: https://youtu.be/NVP2r5PIwBE 🌟 Please leave a LIKE ❤️ and SUBSCRIBE for more AMAZING content! 🌟 Hey!! I am Ishan Sharma, Second year Student at 📍 BITS Pilani, Goa 🏫 pursuing Electrical Engineering 🔌. I enjoy reading books [More]
Built for business and IT, the UiPath Enterprise RPA Platform will always provide you with a consistent automation edge over your competition. Watch the video for a thorough induction on the Platform’s main components and get ready to start your automation journey.
We invest heavily in machine learning to continually improve our member experience and optimize the Netflix service end-to-end. As researchers, we innovate using machine learning in many areas where we research, design, implement, evaluate, and productionalize models and algorithms through both offline experiments and online A/B testing.
AI Platform Pipelines are an excellent way to improve the reliability and reproducibility of your data science and machine learning workflows. In this episode, Yufeng demonstrates how to set up AI Platform Pipelines to address your MLOps needs. Kubeflow Pipelines Overview → https://goo.gle/3g61BzB TFX on AI Platform Pipelines → https://goo.gle/2YC7kYj Check out the rest of the Cloud AI Adventures playlist → https://goo.gl/UC5usG Subscribe to get all the episodes as they come out → https://goo.gle/GCP #AIAdventures product: AI Platform Pipelines; fullname: Yufeng Guo;
Download Slides: https://www.datacouncil.ai/talks/bighead-airbnbs-end-to-end-machine-learning-platform?hsLang=en WANT TO EXPERIENCE A TALK LIKE THIS LIVE? Barcelona: https://www.datacouncil.ai/barcelona New York City: https://www.datacouncil.ai/new-york-city San Francisco: https://www.datacouncil.ai/san-francisco Singapore: https://www.datacouncil.ai/singapore ABOUT THE TALK Airbnb has a wide variety of ML problems ranging from models on traditional structured data to models built on unstructured data such as user reviews, messages and listing images. The ability to build, iterate on, and maintain healthy machine learning models is critical to Airbnb’s success. Many ML Platforms cover data collection, feature engineering, training, deploying, productionalization, and monitoring but few, if any, do all of the above seamlessly. Bighead aims to tie together various open source and in-house projects to remove incidental complexity from ML workflows. Bighead is built on Python and Spark and can be used in modular pieces as each ML problem presents unique challenges. Through standardization of the path to production, training environments and the methods for collecting and transforming data on Spark, each model is reproducible and iterable. This talk covers the architecture, the problems that each individual component and the overall system aims to solve, and a vision for the future of machine learning infrastructure. It’s widely adapted in Airbnb and we have variety of models running in production. We have seen the overall model development time go down from many months to days on Bighead. We plan to open source Bighead to allow the wider community to benefit from our work. ABOUT THE SPEAKER Andrew Hoh is the Product Manager for the Machine Learning Infrastructure team at Airbnb. [More]
AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech (TTS) with Amazon Polly, and developer-focused machine learning with Amazon Machine Learning. For more in-depth deep learning applications, the AWS Deep Learning AMI lets you run deep learning in the cloud, at any scale. Launch instances of the AMI, pre-installed with open source deep learning engines (Apache MXNet, TensorFlow, Caffe, Theano, Torch and Keras), to train sophisticated, custom AI models, experiment with new algorithms, and learn new deep learning skills and techniques; all backed by auto-scaling clusters of GPU-based instances. Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of how to improve scale and efficiency with the AWS Cloud. Learning Objectives • Learn about the breadth of AI services available on the AWS Cloud • Gain insight into practical use cases for Amazon Lex, Amazon Polly, and Amazon Rekognition • Understand why Amazon has selected MXNet as its deep learning framework of choice due its programmability, portability, and performance
ML Systems Workshop @ NIPS 2017 https://nips.cc/Conferences/2017/Schedule?showEvent=8774 Contributed Talk 3: NSML: A Machine Learning Platform That Enables You to Focus on Your Models by Nako Sung. This Video is by Jung-Woo Ha.
In this episode, Yufeng Guo speaks to how you can use Google Cloud AI Platform Built-in Algorithms to train and deploy machine learning models without writing any training code. Cloud AI Platform Training with Built-in Algorithms → https://goo.gle/2vs1NaN Data pre-processing guide → https://goo.gle/3a8gv5w Check out the rest of the Cloud AI Adventures playlist → https://goo.gl/UC5usG Subscribe to get all the episodes as they come out → https://goo.gl/S0AS51 product: AI Platform Training; fullname: Yufeng Guo; #AIAdventures
#Freelancing #FreelanceDataScience #Upwork Want to get started with freelancing in machine learning but don’t know where to start? In my new series I’ll break it all down for you. What platform to use, how to write your profile, how to screen clients, how to apply to jobs, and how to deliver. Stay tuned for more episodes. Learn how to turn deep reinforcement learning papers into code: Deep Q Learning: https://www.udemy.com/course/deep-q-learning-from-paper-to-code/?couponCode=DQN-NOV-2020 Actor Critic Methods: https://www.udemy.com/course/actor-critic-methods-from-paper-to-code-with-pytorch/?couponCode=AC-NOV-2020 Reinforcement Learning Fundamentals https://www.manning.com/livevideo/reinforcement-learning-in-motion Come hang out on Discord here: https://discord.gg/Zr4VCdv Website: https://www.neuralnet.ai Github: https://github.com/philtabor Twitter: https://twitter.com/MLWithPhil
Learn more on our blog: http://nvda.ly/WUV2T. NVIDIA CEO Jen-Hsun Huang describes the platforms NVIDIA is providing for PCs, drones, in the cloud or in cars to bring artificial intelligence to the world, at the Consumer Electronics Show 2016 in Las Vegas.
Learn more about AWS Startups at – https://amzn.to/2CXQYy2  Yuaho Zheng, Director of Engineering at DataVisor, talks about building a fraud detection platform using AI and Big Data at the 2019 AWS Santa Clara Summit.
We introduce TensorFlow Quantum, an open-source library for the rapid prototyping of novel hybrid quantum-classical ML algorithms. This library will extend the scope of current ML under TensorFlow and provides the necessary toolbox for bringing quantum computing and machine learning research communities together to control and model quantum data. Speaker: Masoud Mohseni – Research Scientist Watch all TensorFlow Dev Summit 2020 sessions → https://goo.gle/TFDS20 Subscribe to the TensorFlow YouTube channel → https://goo.gle/TensorFlow
Presentation slides: http://www.slideshare.net/SessionsEvents/nikhil-garg-engineering-manager-quora-at-mlconf-sf-2016 Building a Machine Learning Platform at Quora: Each month, over 100 million people use Quora to share and grow their knowledge. Machine learning has played a critical role in enabling us to grow to this scale, with applications ranging from understanding content quality to identifying users’ interests and expertise. By investing in a reusable, extensible machine learning platform, our small team of ML engineers has been able to productionize dozens of different models and algorithms that power many features across Quora. In this talk, I’ll discuss the core ideas behind our ML platform, as well as some of the specific systems, tools, and abstractions that have enabled us to scale our approach to machine learning.
Video with transcript included: http://bit.ly/33YX5gH Corey Zumar offers an overview of MLflow – a new open source platform to simplify the machine learning lifecycle from Databricks. MLflow provides APIs for tracking experiment runs between multiple users within a reproducible environment and for managing the deployment of models to production. MLflow is designed to be an open, modular platform. This presentation was recorded at QCon New York 2019: http://bit.ly/2KFk7SO The next QCon is QCon London 2020 – March 2-4, 2020: http://bit.ly/2VfRldq For more awesome presentations on innovator and early adopter topics check InfoQ’s selection of talks from conferences worldwide https://bit.ly/2tm9loz #MLflow #MachineLearning #SOA
The Anatomy of a Production-Scale Continuously-Training Machine Learning Platform Denis Baylor (Google Inc.) Eric Breck (Google Inc.) Heng-Tze Cheng (Google Inc.) Noah Fiedel (Google Inc.) Chuan Yu Foo (Google Inc.) Zakaria Haque (Google Inc.) Salem Haykal (Google Inc.) Mustafa Ispir (Google Inc.) Vihan Jain (Google Inc.) Levent Koc (Google Inc.) Chiu Yuen Koo (Google Inc.) Lukasz Lew (Google Inc.) Clemens Mewald (Google Inc.) Akshay Modi (Google Inc.) Neoklis Polyzotis (Google Inc.) Sukriti Ramesh (Google Inc.) Sudip Roy (Google Inc.) Steven Whang (Google Inc.) Martin Wicke (Google Inc.) Jarek Wilkiewicz (Google Inc.) Xin Zhang (Google Inc.) Martin Zinkevich (Google Inc.) Creating and maintaining a platform for reliably producing and deploying machine learning models requires careful orchestration of many components—-a learner for generating models based on training data, modules for analyzing and validating both data as well as models, and finally infrastructure for serving models in production. This becomes particularly challenging when data changes over time and fresh models need to be produced continuously. Unfortunately, such orchestration is often done ad hoc using glue code and custom scripts developed by individual teams for specific use cases, leading to duplicated effort and fragile systems with high technical debt. We present the anatomy of a general-purpose machine learning platform and one implementation of such a platform at Google. By integrating the aforementioned components into one platform, we were able to standardize the components, simplify the platform configuration, and reduce the time to production from the order of months to weeks, while providing platform stability [More]
NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet aims to provide many of the tools, functionality and implementations that are essential for medical image analysis but missing from standard general purpose toolkits. Due to its modular structure, NiftyNet makes it easier to share networks and pre-trained models, adapt existing networks to new imaging data, and quickly build solutions to your own image analysis problems. This talk will explore the whys, the whats and the hows of this open source framework. I have a BSc in Biomedical Engineering (2006) and an MSc in Medical Electronics and Signal Processing for Biomedical Engineering (2008) from the Universidade do Minho, Portugal, followed by a PhD (2008-2012) and PostDoc (2012-2015) in medical image analysis, machine learning and biomarker development between CMIC and the Dementia Research Centre at UCL. In June 2015 I have been appointed Lecturer in Quantitative Neuroradiology at the Translational Imaging Group, part of CMIC, in collaboration with the National Hospital for Neurology and Neurosurgery, working on developing, translating and integrating artificial intelligence-based quantitative imaging biomarkers into the clinical environment.
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The Ali Baba Group’s Wei Lin, Senior Director of PAI Platform of Artificial Intelligence gives a quick overview of how PAI operates at Ali Baba. Speaker: Wei Lin, Ali Baba See the revamped dev site → https://www.tensorflow.org/ Watch all TensorFlow Dev Summit ’19 sessions → http://bit.ly/TFDS19Sessions Subscribe to the TensorFlow YouTube channel → https://bit.ly/TensorFlow1 Music by Terra Monk → http://bit.ly/TerraMonkTFDS Event Photo Album → http://bit.ly/TFSummit19
In less than two days of effort, TruGreen was able to create and deploy a Virtual Agent for routine inquiries without writing a single line of code. See how TruGreen used Power Virtual Agent and Power Automate to reduce their costs and get near real-time information on their business.​ Learn more: https://cloudblogs.microsoft.com/dynamics365/bdm/2019/11/04/announcing-rpa-enhanced-security-no-code-virtual-agents-and-more-for-microsoft-power-platform/ Subscribe to Microsoft on YouTube here: https://aka.ms/SubscribeToYouTube Follow us on social: LinkedIn: https://www.linkedin.com/company/microsoft/ Twitter: https://twitter.com/Microsoft Facebook: https://www.facebook.com/Microsoft/ Instagram: https://www.instagram.com/microsoft/ For more about Microsoft, our technology, and our mission, visit https://aka.ms/microsoftstories #MSIgnite #Microsoft #PowerPlatform
TensorFlow is a truly open source platform with over 1,900 contributors. On this episode of TensorFlow Meets, Laurence (@lmoroney) talks to Open Source Strategist Edd Wilder-James (@edd) about how things like TensorFlow’s Request for Comments process, Special Interest Groups, and the modularity of its codebase make it easier for the community to build TensorFlow together. They also discuss the upcoming O’Reilly TensorFlow World, which is accepting applications to participate now through April 23rd. TensorFlow community → https://bit.ly/2D14XTB Watch Edd’s talk at TF Dev Summit ‘19 → https://bit.ly/2uTx4zr O’Reilly TensorFlow World 2019 → https://oreil.ly/2OP4Uii Subscribe to the TensorFlow channel → http://bit.ly/TensorFlow1 Watch more episodes of TensorFlow Meets → http://bit.ly/2lbyLDK