WiFi MSFTGUEST msevent439sh

Transcription

1 #Azure #MicrosoftAIJourney WiFi MSFTGUEST msevent439sh

2 21 st September, th October, th October th November, th November, nd November, th November, th December 2018 What AI means to Microsoft and Microsoft partners AI without a PhD - Exploring speech, text, vision and bots HOL - Create a Cognitive Search solution for Enterprise Documents Getting to grips with AI and Machine Learning HOL - Predictive Maintenance AI - from theory to production Deep into data science and AI AI Conference Day

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4 9:30 Start 9:30 AI on the Edge (Mahesh Balija) 10:30 Coffee 10:45 Data Bricks (Naveed Hussain) 11:45 Machine Learning SDK (Robin Lester) 12:30 13:00 Lunch 13:00 ML.Net (Gabriel Nepomuceno) 14:00 Coffee 14:10 Containers (Ross Smith) 15:00 Panel Q and A (All)

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8 Logical steps driven Key attributes of a data wrangling tool Experimental Exploratory data access Interactive Iterative

9 9 Microsoft Offerings ETL On Premises(+ Limited Cloud) SQL Server Integration Services Cloud Azure Data Factory + Azure Data Bricks ELT Cloud Azure Data Factory, Azure DataBricks, HD Insight Data Wrangling Data Analysts/Engineers - Power Query + Dataflows Data Science Azure ML Data Prep SDK

10 Data Wrangling Demo

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16 Azure ML Experimentation Manage training jobs locally, scaled-up or scaled-out Run distributed TensorFlow or CNTK training jobs Conduct a hyperparameter search on traditional ML or DNN Service side capture of run metrics, output logs and models Leaderboards, side by side run comparison and model selection Use your favorite IDEs, editors, notebooks, and frameworks U S E A N Y F R A M E W O R K O R L I B R A R Y U S E A N Y T O O L U S E T H E M O S T P O P U L A R I N N O V A T I O N S

17 Workspace Core Project Run CNTK Train TensorFlow Script Hyperdrive Connection Data Mount Copy Batch AI Provision DSVM ACI Deploy Model

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19 Compute target Training Deployment Your local computer A Linux VM in Azure (such as the Data Science Virtual Machine) Azure Batch AI Cluster Azure Databricks Azure Data Lake Analytics Apache Spark for HDInsight Azure Container Instance Azure Kubernetes Service Azure IoT Edge Project Brainwave (Field-programmable gate array)

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22 Data Load Typical AI Lifecycle Data Sets & Catalog Data Curation Data Exploration & Preparation Model Development & Training Data Collection Data Engineer Data Scientist Model Development & Training Always try out many different algorithms Always tune the hyperparameters for models Always explore numerous feature representations for data App Flighting & Analytics App Dev Model Package & Deploy App Development & Model Inference

23 I have manually tested a couple of algorithms with hyper-parameter sweeps, but it is time consuming and many models still to be tested

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25 Compute target GPU acceleration Automated hyperparameter tuning Automated model selection Local computer Maybe Can be used in pipelines Data Science Virtual Machine (DSVM) Azure Batch AI Azure Databricks * Azure Data Lake Analytics Azure HDInsight *

26 This material is provided for informational purposes only. MICROSOFT MAKES NO WARRANTIES, EXPRESS OR IMPLIED.