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Intro to PB-Scale AI Big Data Cloud Boot Camp - kubernetes
Link to Website
Let's blow your mind in this 2-hr session, you wont regret it in spending weekend afternoon with us at AI Big Data Cloud/Container Frontier!
Refund Policy: No refund
Follow our Big Data Cloud AI Robot @ClouDatAI
Fog Computing/Cloud Computing, Serverless Computing/Cloud-Native Computing, BlockChain/Bitcoin, Lambda Architecture, Microservices-oriented Architecture/monolithic architecture, Immutable Datalake, Real-time Data Pipeline, Container/VM/Bare Metal, IaaS/PaaS/SaaS, Machine Learning/Deep Learning, Supervised Learning/Unsupervised Learning, Big Data/Deep Learning, Hadoop/Spark, YARN/Mesos, Docker Engine/Kubernetes, OpenStack/CloudStack, OpenShift/CloudFoundry, SQL/NoSQL/HDFS, GUI/CLI/API, are you feeling you are lost in the jungle of fast-pacing tech frontier? We Are Here to Help You to Get Out of It and Lead instead of Follow It!
You go to a lot of trainings and/or meetups, whether free or not, expensive or cheap, ALL of those are either marketing fluff, sales pitches, or short of global pictures, or short of details, no insight, let alone foresight. Our 2-hr Cream-Boot Camp is radically different, vendor agnostic, no strings attached, full of meat, lots of demos, offering you both macro & micro perspective of the state-of-the-art in practical way with hindsight, insight and foresight!
Overview of the following 2-Day Boot Camp
We Don't Give You a Fish, Instead We Teach You to Fish
- Learn how Machine & Deep Learning AI Big Data Cloud enables data scientists to help companies reduce costs, increase profits, improve products, retain customers, and identify new opportunities
- Topics include:
- How to identify potential business use cases in leveraging big data cloud AI technology
- How to obtain, clean, and combine disparate data sources to create a data pipeline for data lake
- What Machine-Learning (Shallow Learning) & Deep Learning technique to use for a particular data science project
- How to conduct PoC & productionalized big data projects in cloud/container cluster at scale
- How to create real-time data pipelines using the latest open source with public cloud or private cloud/container, ingest data in real time and at scale, process the data in real-time/interactive/batch, and build data products from real-time data sources
- How to combines ETL, batch analytics, real-time stream analysis with machine learning, deep learning, and visualizations through both data pipeline & data lakes
- Understand & master TensorFlow's fundamentals & capabilities
- Explore TensorBoard to debug and optimize your own Neural Network Architectures, train, test, validate & serve your models for real-life Deep Learning applications at Scale
Agenda (Subject to Change at Anytime without Notice) - 50% Lecture, 50% Hands-On, Vendor Agnostic, No Strings Attached, You Working on a Container Cluster instead of only an Instance in cloud
8:00 AM - 8:50AM Elastic Cloud Computing and Scalabe Big Data AI: What, Why and How?
9:00 AM - 9:50AM Deep Dive into Public/Private/Hybrid Cloud Infrastructure: Elastic/Plastic Cloud; Bare Metal/VM/Container; IaaS/PaaS/SaaS; Hyper-Scale/Hyper-Convergence; From Linux Kernel to Distributed System's CAP Theorem; OpenStack as the De facto Private Cloud; Capacity Planning & Auto-scaling Challenges of Cloud; Micro-service-based Immutable Architecture
10:00 AM - 10:50AM Deep Dive into Big Data Technology Stack: Nature of Big Data - Structural/Unstructural; Hot/Warm/Cold; Machine/Human; Text/Numerical, SQL(ACID)/NoSQL(BASE); Batch(Hindsight)/Interactive (Insight)/Streaming(Foresight); Data Pipeline & Datalake; Hadoop/Spark/Kafka/HDFS/HBas/HIVE
11:00 AM - 11:50AM Docker/CoreOS Container In-Depth: Computation/Storage/Networking Models
12:00 PM - 1:00PM Lunch Break (Lunch included, Veggie option available)
1:00 PM - 5PM Hands-on: I Set Up & Test Drive Your Own AI Big Data Google/AWS Cloud|CoreOS Container Cluster (Hadoop, Spark, Kafka, HDFS, HBase, HIVE, Tensorflow) : Using Spark/Hadoop for Word Counting on Twitter Data/Kafka Stream of system logs
8:00 AM - 8:50AM Practical Machine Learning In-Depth: Feature Engineering, From Regression to Classification, 5 Tribes of Machine Learning: Symbolists with Inverse Deduction of Symbolic Logic, Connectionists with Backpropagation of Neural Networks, Evolutionaries with Genetic Programming, Bayesians with Probabilistic Inference in Statistics, Analogizers with Support Vector Machines; Supervised Learning (Classification/Regression), Unsupervised Learning (Clustering), Semi-Supervised Learning; Data Ingestion & Its Challenges, Data Cleansing/Prep-processing; Training Set/Testing Set Partitioning; Feature Engineering (Feature Extraction/Selection/Construction/Learning, Dimension Reduction); Model Building/Evaluation/Deployment|Serving/Scaling|Reduction/Optimization with Prediction Feedbacks
9:00 AM - 9:50AM Practical Deep-Learning-based AI In-Depth: Weak/Special AI vs Strong/General AI; Key Components of AI: Knowledge Representation, Deduction, Reasoning, NLP, Planning, Learning,Perception, Sensing & Actuation, Goals & Problem Solving, Consciousness & Creativity; Rectangle of Deep Learning, Shallow Learning, Supervised Learning, and Unsupervised Learning; Basic Multi-layer Architecture of Deep Forward/Convolutional Neural Networks(FNN/CNN)/Deep Recurrent Neural Networks(RNN)/Long short-term memory(LSTM): Input/Hidden/Output Layers, Weights, Biases, Activation Function, Feedback Loops, Backpropagation from Automatic Differentiation and Stochastic Gradient Descent (SGD); Convex/Non-Convex Optimization; Ways of Training Deep Neural Networks: Data/Model Parallelism, Synchronous/Asynchronous Training, Variants of SGD, Gradient Vanishing/Explotion, Loss Function Minimization/Optimization with Dropout/Regulariztion & Batch Normalization & Learning Rate & Training Steps, and Unsupervised Pre-training (Autoencoder etc.); Deep Learning Applications - What's Fit and What's Not?: Deep Structures, Unusual RNN, Huge Models
10:00 AM - 10:50PM Embracing Paradigm Shifting from Algorithm-based Rigid Computing to Model-based Big Data Cloud IoT-powered Deep Learning AI for Real-Life Problem Solving: What, Why and How? - Problem Formulation, Data Gathering, Algorithmic & Neural Network Architecture Selection, Hyperparameter Turning, Deep Learning, Cross Validation, and Model Serving
11:00 AM - 11:50AM Tensorflow In-Depth: The Origin, Fundamental Concepts (Tensors/Data Flow Graph & More), Historical Development & Theoretical Foundation; Two Major Deep Learning Models and Their TensorFlow Implementation: Convolutional Neural Network (CNN), Recurrent Neural Network (RNN); GPU/Tensorflow vs. CPU/NumPy; TensorFlow vs Other Open Source Deep Learning Packages: Torch, Caffe, MXNet, Theano: Programming vs. Configuration; Tackling Deep Learning Blackbox Puzzle with TensorBoard
12:00 PM - 1:00PM Lunch Break (Lunch included, Veggie option available)
1:00PM - 5PM Hands-on II: Architect, Design & Develop (Modeling/Training -> Inferencing/Testing) Your Own Chosen AI Application Using Python in Your Own Scalable AI Big Data Docker/CoreOS Container Cluster (Hadoop, Spark, Kafka, HBase, HIVE, Tensorflow)
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