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Machine Learning Pitch Deck

The document discusses how global business value derived from artificial intelligence (AI) will reach $3.9 trillion in 2022. It notes that AI will generate business value through decision support, virtual agents, decision automation, smart products, and other areas.

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Nishant Thacker Microsoft Confidential
3.9$ TGlobal business value derived from AI in 2022 will reach “Forecast: The Business Value of Artificial Intelligence, Worldwide, 2017-2025”, Gartner, April 2018. Decision support Virtual agents Decision automation Smart products 3.9$ T
Where do you see your company today? Where do you see your company in the future? Where do you see your top competitors today? Analytics Capabilities BASIC ADVANCED
My company NOW* My company IN THE FUTURE (24-36 months)* My top competitors NOW* Analytics Capabilities BASIC ADVANCED *Based on independent market research in the form of focus groups and in-depth-interviews
Customer Insights Sales Insights Virtual Assistants Cash Flow Forecasting HR Insights Churn Analytics Dynamic Pricing Waiting line optimization Risk Management Quality Assurance Resource Planning Lead Scoring Intelligent chatbots Financial Forecasting Employee Insights Marketing Sales Service Finance Operations Workforce Product RecommendationProduct Recommendation Predictive MaintenancePredictive Maintenance Demand ForecastingDemand Forecasting
Hybrid solution predicts onboard water usage, saving $200k/ship/year The average size of a single cart has decreased Provide personalized digital content to shoppers Increase cart size Unplanned downtime results in cost overruns Predict when maintenance should be performed Minimize downtime Solar energy production is inconsistent Align energy supply with the optimal markets Maximize revenue Product recommendation Predictive maintenance Demand forecasting Distributed power generation increases revenue by over €100 million ASOS delivers 15.4 million personalized experiences with 33 orders per second
Build a unified and usable data pipeline Train ML and DL models to derive insights Operationalize models and distribute insights at scale Serving business users and end users with intelligent and dynamic applications
And most aren’t satisfied with their current solutions “What it Takes to be Data-Driven: Technologies and Best Practices for Becoming a Smarter Organization”, TDWI, 2017. Are satisfied with ease of use of analytics software46% 21% Are satisfied with access to semi- structured and unstructured data 28% Are satisfied with ability to scale to handle unexpected requirements Complexity of solutions Many options in the marketplace Data silos Incongruent data types Difficult to scale effectively Performance constraints
Data AI
Data AI Data modernization to Azure Globally distributed data Cloud Scale Analytics Data Modernization on-premises AI apps & agents Knowledge mining
Domain specific pretrained models To simplify solution development Azure Databricks Machine Learning VMs Popular frameworks To build advanced deep learning solutions TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Scikit-Learn Familiar Data Science tools To simplify model development CPU GPU FPGA From the Intelligent Cloud to the Intelligent Edge Azure Notebooks JupyterVisual Studio Code Command line
Let’s walk through an actual machine learning scenario…
Flammable equipment near heat source No hardhat Missing equipment
No hardhat
How can we accurately identify hazardous situations with machine learning?
Domain specific pretrained models To simplify solution development Azure Databricks Machine Learning VMs Popular frameworks To build advanced deep learning solutions TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Scikit-Learn Azure Notebooks Jupyter Familiar Data Science tools To simplify model development Visual Studio Code Command line CPU GPU FPGA From the Intelligent Cloud to the Intelligent Edge
Infuse apps with powerful, pre-trained AI models Customize easily and tailor to your needs Vision Speech Language Bing Search … Computer Vision | Video Indexer | Face | Content Moderator Speech to Text | Text to Speech | Speech Translation | Speaker Recognition Text Analytics | Spell Check | Language Understanding | Text Translation | QnA Maker Big Web Search | Video Search | Image Search | Visual Search | Entity Search | News Search | Autosuggest
Domain specific pretrained models To simplify solution development Azure Databricks Machine Learning VMs Popular frameworks To build advanced deep learning solutions TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Scikit-Learn Azure Notebooks Jupyter Familiar Data Science tools To simplify model development Visual Studio Code Command line CPU GPU FPGA From the Intelligent Cloud to the Intelligent Edge
Domain specific pretrained models To simplify solution development Azure Databricks Machine Learning VMs Popular frameworks To build advanced deep learning solutions TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Scikit-Learn Familiar Data Science tools To simplify model development CPU GPU FPGA From the Intelligent Cloud to the Intelligent Edge Azure Notebooks JupyterVisual Studio Code Command line
Choose any python development environment And improve data science productivity PyCharmAzure NotebooksVisual Studio Code Command lineZeppelin Interactive widgets for Jupyter Notebooks Azure Machine Learning for Visual Studio Code extension Jupyter Get started with AML on Azure Notebooks: http://aka.ms/aznotebooks-aml
RPN RoIP R-CNN Needs specialized knowledgeTime-consuming Complex
Domain specific pretrained models To simplify solution development Popular frameworks To build advanced deep learning solutions Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Familiar Data Science tools To simplify model development From the Intelligent Cloud to the Intelligent Edge Azure Databricks Machine Learning VMs TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Scikit-Learn Azure Notebooks JupyterVisual Studio Code Command line CPU GPU FPGA
Domain specific pretrained models To simplify solution development Popular frameworks To build advanced deep learning solutions Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Familiar Data Science tools To simplify model development From the Intelligent Cloud to the Intelligent Edge Azure Databricks Machine Learning VMs TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Scikit-Learn Azure Notebooks JupyterVisual Studio Code Command line CPU GPU FPGA
Build advanced deep learning solutions Use your favorite deep learning frameworks without getting locked into one framework ONNX Community project created by Facebook and Microsoft Use the best tool for the job. Train in one framework and transfer to another for inference TensorFlow PyTorch Scikit-Learn MXNet Chainer Keras
Frameworks Azure Create Deploy Services Devices Azure Machine Learning services Ubuntu VM Windows Server 2019 VM Azure Custom Vision Service ONNX Model Windows devices Other devices (iOS, etc.)
Domain specific pretrained models To simplify solution development Popular frameworks To build advanced deep learning solutions Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Familiar Data Science tools To simplify model development From the Intelligent Cloud to the Intelligent Edge Azure Databricks Machine Learning VMs TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Scikit-Learn Azure Notebooks JupyterVisual Studio Code Command line CPU GPU FPGA
Domain specific pretrained models To simplify solution development Popular frameworks To build advanced deep learning solutions Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Familiar Data Science tools To simplify model development From the Intelligent Cloud to the Intelligent Edge Azure Databricks Machine Learning VMs TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Scikit-Learn Azure Notebooks JupyterVisual Studio Code Command line CPU GPU FPGA
+ To empower data science and development teams Develop models faster with automated machine learning Use any Python environment and ML frameworks Manage models across the cloud and the edge. Prepare data clean data at massive scale Enable collaboration between data scientists and data engineers Access machine learning optimized clusters Azure Machine Learning Python-based machine learning service Azure Databricks Apache Spark-based big-data service
Bring AI to everyone with an end-to-end, scalable, trusted platform Built with your needs in mind Support for open source frameworks Managed compute DevOps for machine learning Simple deployment Tool agnostic Python SDK Automated machine learning Seamlessly integrated with the Azure Portfolio Boost your data science productivity Increase your rate of experimentation Deploy and manage your models everywhere
Fast, easy, and collaborative Apache Spark™-based analytics platform Built with your needs in mind Optimized Apache Spark environmnet Collaborative workspace Integration with Azure data services Autoscale and autoterminate Optimized for distributed processing Support for multiple languages and libraries Seamlessly integrated with the Azure Portfolio Increase productivity Build on a secure, trusted cloud Scale without limits
Leverage your favorite deep learning frameworks AZURE ML SERVICE Increase your rate of experimentation Bring AI to the edge Deploy and manage your models everywhere TensorFlow MS Cognitive Toolkit PyTorch Scikit-Learn ONNX Caffe2 MXNet Chainer AZURE DATABRICKS Accelerate processing with the fastest Apache Spark engine Integrate natively with Azure services Access enterprise-grade Azure security
What to use when? + Customer journey Data Prep Build and Train Manage and Deploy Apache Spark / Big Data Python ML developer Azure ML service (Pandas, NumPy etc. on AML Compute) Azure ML service (OSS frameworks, Hyperdrive, Pipelines, Automated ML, Model Registry) Azure ML service (containerize, deploy, inference and monitor) Azure ML service (containerize, deploy, inference and monitor) Azure Databricks (Apache Spark Dataframes, Datasets, Delta, Pandas, NumPy etc.) Azure Databricks + Azure ML service (Spark MLib and OSS frameworks + Automated ML, Model Registry)
Domain specific pretrained models To simplify solution development Popular frameworks To build advanced deep learning solutions Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Familiar Data Science tools To simplify model development From the Intelligent Cloud to the Intelligent Edge Azure Databricks Machine Learning VMs TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Scikit-Learn Azure Notebooks JupyterVisual Studio Code Command line CPU GPU FPGA
Domain specific pretrained models To simplify solution development Popular frameworks To build advanced deep learning solutions Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Familiar Data Science tools To simplify model development From the Intelligent Cloud to the Intelligent Edge Azure Databricks Machine Learning VMs TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Scikit-Learn Azure Notebooks JupyterVisual Studio Code Command line CPU GPU FPGA
Accelerate deep learning General purpose machine learning D, F, L, M, H Series CPUs Optimized for flexibility Optimized for performance GPUs FPGAs Deep learning N Series Specialized hardware accelerated deep learning AML hardware accelerated models (Project Brainwave)
Simplify deployment + accelerate inferencing with ONNX runtime Track models in production Capture model telemetry From the Intelligent Cloud to the Intelligent Edge On-premisesCloud
Flammable equipment near heat source Missing equipment No hardhat
Automated machine learning Machine learning DevOps Machine Learning 95 % Accelerated model building Azure DevOps integration for CI/CD
Automated machine learning Machine learning DevOps Machine Learning 95 % Accelerated model building Azure DevOps integration for CI/CD
How much is this car worth? Azure Machine Learning Automated machine learning
Model creation is typically a time consuming process Mileage Condition Car brand Year of make Regulations … Parameter 1 Parameter 2 Parameter 3 Parameter 4 … Gradient Boosted Nearest Neighbors SGD Bayesian Regression LGBM … Mileage Gradient Boosted Criterion Loss Min Samples Split Min Samples Leaf XYZ Model Which algorithm? Which parameters?Which features? Car brand Year of make
Which algorithm? Which parameters?Which features? Mileage Condition Car brand Year of make Regulations … Gradient Boosted Nearest Neighbors SGD Bayesian Regression LGBM … Nearest Neighbors Criterion Loss Min Samples Split Min Samples Leaf XYZ Model Iterate Gradient Boosted N Neighbors Weights Metric P ZYX Mileage Car brand Year of make Model creation is typically a time consuming process Car brand Year of make Condition
Which algorithm? Which parameters?Which features? Iterate Model creation is typically a time consuming process
Enter data Define goals Apply constraints Azure Machine Learning accelerates model development with automated machine learning Input Intelligently test multiple models in parallel Optimized model
70%95% Azure Machine Learning accelerates model selection with model explainability Feature importance Mileage Condition Car brand Year of make Regulations Model B (70%) Mileage Condition Car brand Year of make Regulations Feature importance Model A (95%)
Automated machine learning Machine learning DevOps Machine Learning 95 % Accelerated model building Azure DevOps integration for CI/CD
Register and Manage Model Build Image Build model (your favorite IDE) Deploy Service Monitor Model Train & Test Model Integrated with Azure DevOps
ServeStore Prep and trainIngest Batch data Streaming data Azure Kubernetes service Power BI Azure analysis services Azure SQL data warehouse Cosmos DB, SQL DB Azure Data Lake Storage Azure Data Factory Azure Event Hubs Azure Databricks Azure Machine Learning service Apps Model Serving Ad-hoc Analysis Operational Databases
PREP & TRAIN Collect and prepare data Train and evaluate model A B C Operationalize and manage
Ingest Azure Data Factory Store Azure Blob Storage Understand and transform Azure Databricks • Leverage open source technologies • Collaborate within teams • Use ML on batch streams • Build in the language of your choice • Leverage scale out topology • Scale compute and storage separately • Integrate with all of your data sources • Create hybrid pipelines • Orchestrate in a code-free environment Leverage best-in-class analytics capabilities Scale without limits Connect to data from any source
• Easily scale up or scale out • Autoscale on serverless infrastructure • Leverage commodity hardware • Determine the best algorithm • Tune hyperparameters to optimize models • Rapidly prototype in agile environments • Collaborate in interactive workspaces • Access a library of battle-tested models • Automate job execution Scale compute resources to meet your needs Quickly determine the right model for your data Simplify model development Automated ML capabilities Automated ML Scale out clusters Infrastructure Machine learning Tools Azure Databricks Azure ML service Azure ML service Azure Databricks Azure ML service
• Identify and promote your best models • Capture model telemetry • Retrain models with APIs • Deploy models anywhere • Scale out to containers • Infuse intelligence into the IoT edge • Build and deploy models in minutes • Iterate quickly on serverless infrastructure • Easily change environments Proactively manage model performance Deploy models closer to your data Bring models to life quickly Train and evaluate models Model MGMT, experimentation, and run history Azure ML service Containers AKS ACI IoT edge Docker Azure ML service
2022202120202019 Deep neural networks will be a standard tool for 80% of data scientists1 More than 40% of data science tasks will be automated1 20% of companies will dedicate workers to monitor neural networks1 90% of modern analytics platforms will feature natural- language generation1 30% of net new revenue growth from industry-specific solutions will include AI1 1 in 5 workers engaged in mostly nonroutine tasks will rely on AI to do their jobs2 1 “100 Data and Analytics Predictions Through 2021”, Gartner, 2017. 2 “Predicts 2018: AI and the Future of Work”, Gartner, 2018. 2018
• Choose VMs for your modeling needs • Process video using GPU-based VMs • Run experiments in parallel • Provision resources automatically • Leverage popular deep learning toolkits • Develop your language of choice Scale compute resources in any environment Quickly evaluate and identify the right model Streamline AI development efforts AI Ready Virtual Machines Data Science VMs Notebooks, IDEs, Command Line Scale out clusters AML Compute MS Cognitive Toolkit Keras TensorFlow PyTorch Azure ML service SDK
Deploy Azure ML models at scale Azure Machine Learning service
Prepare Data Register and Manage Model Train & Test Model Build Image … Build model (your favorite IDE) Deploy Service Monitor Model Prepare Experiment Deploy DevOps loop for data science
DevOps loop for data science Prepare Data Prepare Register and Manage Model Build Image … Build model (your favorite IDE) Deploy Service Monitor Model Train & Test Model
Model management in Azure Machine Learning Create/retrain model Create scoring files and dependencies Create and register image Monitor Register model Cloud Light Edge Heavy Edge Deploy image
Use leaderboards, side by side run comparison and model selection Conduct a hyperparameter search on traditional ML or DNN Leverage service-side capture of run metrics, output logs and models Manage training jobs locally, scaled-up or scaled-out Experimentation 95 % 80% 75% 90% 85%
Azure Databricks Remote support for other IDEs outside of native notebooks MLFlow for better DevOps with Azure Databricks and other ML pipelines Azure Machine Learning Python SDK support for popular IDEs & notebooks, including Azure Databricks Azure Machine Learning managed compute capabilities Introduce new models for FPGA scoring Robust ONNX support - runtime engine in AML, model operationalization in SQL Server Automated machine learning Deploy and manage models to IoT edge Extend Machine Learning services to SQL DB
© Microsoft Corporation Get started quickly with AI
© Microsoft Corporation Intelligent apps Leverage AI to create the future of business applications Conversational agents Transform your engagements with customers and employees Business processes Transform critical business processes with AI Of customer interactions powered by AI bots by 2025 Applications to include AI by the end of this year 75% 85% Of enterprises using AI by 202095%
© Microsoft Corporation Smart factory Smart kitchen Smart bath Voice-activated speakers Smart cameras Drones Smart kiosks
© Microsoft Corporation Employment Facilitating development of professional skills Modern life Delivering personalized experiences to improve independence Human connection Providing equal access to information and opportunity How Microsoft is helping the visually-impaired How Helpicto is helping non-verbal children How RIT is helping the hearing-impaired Empowering people with tools that amplify human capability
Customer analytics Financial modeling Risk, fraud, threat detection Credit analytics Marketing analytics Faster innovation for a better customer experience Improved consumer outcomes and increased revenue Enhanced customer experience with machine learning Transform growth with predictive analytics Improved customer engagement with machine learning Customer profiles Credit history Transactional data LTV Loyalty Customer segmentation CRM data Credit data Market data CRM Credit Risk Merchant records Products and services Clickstream data Products Services Customer service data Customer 360 degree evaluation Customer segmentation Reduced customer churn Underwriting, servicing and delinquency handling Insights for new products Commercial/retail banking, securities, trading and investment models Decision science, simulations and forecasting Investment recommendations Real-time anomaly detection Card monitoring and fraud detection Security threat identification Risk aggregation Enterprise DataHub Regulatory and compliance analysis Credit risk management Automated credit analytics Recommendation engine Predictive analytics and targeted advertising Fast marketing and multi- channel engagement Customer sentiment analysis Transaction data Demographics Purchasing history Trends Effective customer engagement Decision services management Risk and revenue management Risk and compliance management Recommendation engine
Genomics and precision medicine Clinical and claims data GPU image processing IoT device analytics Social analytics Faster innovation for drug development Improved outcomes and increased revenue Diagnostics leveraging machine learning Predictive analytics transforms quality of care Improved patient communications and feedback FAST-Q BAM SAM VCF Expression HL7/CCD 837 Pharmacy Registry EMR Readings Time series Event data Social media Adverse events Unstructured Single cell sequencing Biomarker, genetic, variant and population analytics ADAM and HAIL on Databricks Claims data warehouse Readmission predictions Efficacy and comparative analytics Prescription adherence Market access analysis Graphic intensive workloads Deep learning using Tensor Flow Pattern recognition Aggregation of streaming events Predictive maintenance Anomaly detection Real-time patient feedback via topic modelling Analytics across publication data MRI X-RAY CT Ultrasound DNA sequences Real world analytics Image deep learning Sensor data Social data listening
Content personalization Customer churn prevention Recommendation engine Predictive analytics Sentiment analysis Faster innovation for customer experience Improved consumer outcomes and increased revenue Enhance user experience with machine learning Predictive analytics transforms growth Improved consumer engagement with machine learning Customer profiles Viewing history Online activity Content sources Channels Customer profiles Online activity Content distribution Services data Transactions Subscriptions Demographics Credit data Content metadata Ratings Comments Social media activity Personalized viewing and engagement experience Click-path optimization Next best content analysis Improved real time ad targeting Quality of service and operational efficiency Market basket analysis Customer behavior analysis Click-through analysis Ad effectiveness Content monetization Fraud detection Information-as-a-service High value user engagement Predict audience interests Network performance and optimization Pricing predictions Nielsen ratings and projections Mobile spatial analytics Demand-elasticity Social network analysis Promotion events time-series analysis Multi-channel marketing attribution Consumption logs Clickstream and devices Marketing campaign responses Personalized recommendations Effective customer retention Information optimization Inventory allocation Consumer engagement analysis
Next best and personalized offers Store design and ergonomics Data-driven stock, inventory, ordering Assortment optimization Real-time pricing optimization Faster innovation for customer experience Improved consumer outcomes and increased revenue Omni-channel shopping experience with machine learning Predictive analytics transforms growth Improved consumer engagement with machine learning Customer profiles Shopping history Online activity Social network analysis Shopping history Online activity Floor plans App data Demographics Buyer perception Consumer research Market/competitive analysis Historical sales data Price scheduling Segment level price changes Customer 360/consumer personalization Right product, promotion, at right time Multi-channel promotion Path to purchase In-store experience Workforce and manpower optimization Predict inventory positions and distribution Fraud detection Market basket analysis Economic modelling Optimization for foot traffic, Online interactions Flat and declining categories Demand-elasticity Personal pricing schemes Promotion events Multi-channel engagement Demand plans Forecasts Sales history Trends Local events/weather patterns Recommendation engine Effective customer engagement Inventory optimization Inventory allocation Consumer engagement
Customer value analytics Next best and personalized offers Risk and fraud management Sales and campaign optimization Sentiment analysis Faster innovation for customer growth Improved outcomes and increased revenue Risk management with machine learning Predictive analytics transforms growth Improved customer engagement with machine learning Customer profiles Online history Transaction data Loyalty Customer segmentation CRM data Credit data Market data CRM Merchant records Products Services Marketing data Social media Online history Customer service data Customer 360, segmentation aggregation and attribution Audience modelling/index report Reduce customer churn Insights for new products Historical bid opportunity as a service Right product, promotion, at right time Real time ad bidding platform Personalized ad targeting Ad performance reporting Real-time anomaly detection Fraud prevention Customer spend and risk analysis Data relationship maps Optimizing return on ad spend and ad placement Multi-channel promotion Ideal customer traits Optimized ad placement Opinion mining/social media analysis Deeper customer insights Customer loyalty programs Shopping cart analysis Transaction data Demographics Purchasing history Trends Effective customer engagement Recommendation engine Risk and fraud analysis Campaign reporting analytics Brand promotion and customer experience
Digital oil field/ oil production Industrial IoT Supply-chain optimization Safety and security Sales and marketing analytics Faster innovation for revenue growth Improved outcomes and increased revenue Optimizing supply- chain with machine learning Predictive analytics transforms safety and security Improved customer engagement with machine learning Field data Asset data Demographics Production data Sensor stream data UAVs images Inventory data Production data Sensor stream data Transport Retail data Grid production data Refinery tuning parameters Clickstream data Products Services Market data Competitive data Demographics Production optimization Integrate exploration and seismic data Minimize lease operating expenses Decline curve analysis Pipeline monitoring Preventive maintenance Smart grids and microgrids Grid operations, field service Asset performance as a service Trade monitoring, optimization Retail mobile applications Vendor management - construction, transportation, truck and delivery optimization Real-time anomaly detection Predictive analytics Industrial safety Environment health and safety Fast marketing and multi-channel engagement Develop new products and monitor acceptance of rates Predictive energy trading Deep customer insights Transaction data Demographics Purchasing history Trends Upstream optimization, maximize well life Grid operations, asset inventory optimization Supply-chain optimization Risk optimization Recommendations engine
Intrusion detection and predictive analytics Security intelligence Fraud detection and prevention Security compliance reporting Fine-grained data analytics security Prevent complex threats with machine learning Faster innovation for threat prevention Risk management with machine learning Transform security with improved visibility Limit malicious insiders to transform growth Firewall/network logs Apps Data access layers Firewall/network logs Network flows Authentications Firewall/network logs Web Applications Devices OS Files Tables Clusters Reports Dashboards Notebooks Prevention of DDoS attacks Threat classifications Data loss/anomaly detection in streaming Cybermetrics and changing use patterns Real-time data correlation Anomaly detection Security context, enrichment Offence scoring, prioritization Security orchestration e-Tailing Inventory monitoring Social media monitoring Phishing scams Piracy protection Ad-hoc/historic incident reports SOC/NOC dashboards Deep OS auditing Data loss detection in IoT User behavior analytics Role-based access controls Auditing and governance File integrity monitoring Row level and column level access permissions Firewall/network logs Web/app logs Social media content Security controls to leverage all data Actionable threat intelligence Risk and fraud analysis Compliance management Identity and access management for analytics
Vision Speech Language
Vision Speech Language
Use any language, any development tool and any framework >90% of Fortune 500 companies use Microsoft Cloud Benefit from industry-leading security, privacy, compliance, transparency, and AI ethics standards Accelerate time to value with agile tools and services Powerful tools Pretrained AI services Comprehensive platform On-premisesEdgeCloud Innovate with AI everywhere – in the cloud, at edge and on-premises
© Microsoft Corporation It’s all on MicrosoftAzure
© Microsoft Corporation Accelerate time to market Productive Optimize your infrastructure Hybrid Intelligent Innovate at scale Develop with confidence Trusted Microsoft Azure
© Microsoft Corporation
© Microsoft Corporation System integrationData integration BI and analytics
Machine Learning Pitch Deck

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Machine Learning Pitch Deck

  • 1.
  • 2.
    3.9$ TGlobal businessvalue derived from AI in 2022 will reach “Forecast: The Business Value of Artificial Intelligence, Worldwide, 2017-2025”, Gartner, April 2018. Decision support Virtual agents Decision automation Smart products 3.9$ T
  • 3.
    Where do yousee your company today? Where do you see your company in the future? Where do you see your top competitors today? Analytics Capabilities BASIC ADVANCED
  • 4.
    My company NOW* Mycompany IN THE FUTURE (24-36 months)* My top competitors NOW* Analytics Capabilities BASIC ADVANCED *Based on independent market research in the form of focus groups and in-depth-interviews
  • 5.
    Customer Insights SalesInsights Virtual Assistants Cash Flow Forecasting HR Insights Churn Analytics Dynamic Pricing Waiting line optimization Risk Management Quality Assurance Resource Planning Lead Scoring Intelligent chatbots Financial Forecasting Employee Insights Marketing Sales Service Finance Operations Workforce Product RecommendationProduct Recommendation Predictive MaintenancePredictive Maintenance Demand ForecastingDemand Forecasting
  • 6.
    Hybrid solution predictsonboard water usage, saving $200k/ship/year The average size of a single cart has decreased Provide personalized digital content to shoppers Increase cart size Unplanned downtime results in cost overruns Predict when maintenance should be performed Minimize downtime Solar energy production is inconsistent Align energy supply with the optimal markets Maximize revenue Product recommendation Predictive maintenance Demand forecasting Distributed power generation increases revenue by over €100 million ASOS delivers 15.4 million personalized experiences with 33 orders per second
  • 7.
    Build a unifiedand usable data pipeline Train ML and DL models to derive insights Operationalize models and distribute insights at scale Serving business users and end users with intelligent and dynamic applications
  • 8.
    And most aren’tsatisfied with their current solutions “What it Takes to be Data-Driven: Technologies and Best Practices for Becoming a Smarter Organization”, TDWI, 2017. Are satisfied with ease of use of analytics software46% 21% Are satisfied with access to semi- structured and unstructured data 28% Are satisfied with ability to scale to handle unexpected requirements Complexity of solutions Many options in the marketplace Data silos Incongruent data types Difficult to scale effectively Performance constraints
  • 10.
  • 11.
    Data AI Data modernizationto Azure Globally distributed data Cloud Scale Analytics Data Modernization on-premises AI apps & agents Knowledge mining
  • 13.
    Domain specific pretrainedmodels To simplify solution development Azure Databricks Machine Learning VMs Popular frameworks To build advanced deep learning solutions TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Scikit-Learn Familiar Data Science tools To simplify model development CPU GPU FPGA From the Intelligent Cloud to the Intelligent Edge Azure Notebooks JupyterVisual Studio Code Command line
  • 14.
    Let’s walk throughan actual machine learning scenario…
  • 16.
    Flammable equipment near heatsource No hardhat Missing equipment
  • 17.
  • 18.
    How can weaccurately identify hazardous situations with machine learning?
  • 19.
    Domain specific pretrainedmodels To simplify solution development Azure Databricks Machine Learning VMs Popular frameworks To build advanced deep learning solutions TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Scikit-Learn Azure Notebooks Jupyter Familiar Data Science tools To simplify model development Visual Studio Code Command line CPU GPU FPGA From the Intelligent Cloud to the Intelligent Edge
  • 20.
    Infuse apps withpowerful, pre-trained AI models Customize easily and tailor to your needs Vision Speech Language Bing Search … Computer Vision | Video Indexer | Face | Content Moderator Speech to Text | Text to Speech | Speech Translation | Speaker Recognition Text Analytics | Spell Check | Language Understanding | Text Translation | QnA Maker Big Web Search | Video Search | Image Search | Visual Search | Entity Search | News Search | Autosuggest
  • 21.
    Domain specific pretrainedmodels To simplify solution development Azure Databricks Machine Learning VMs Popular frameworks To build advanced deep learning solutions TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Scikit-Learn Azure Notebooks Jupyter Familiar Data Science tools To simplify model development Visual Studio Code Command line CPU GPU FPGA From the Intelligent Cloud to the Intelligent Edge
  • 22.
    Domain specific pretrainedmodels To simplify solution development Azure Databricks Machine Learning VMs Popular frameworks To build advanced deep learning solutions TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Scikit-Learn Familiar Data Science tools To simplify model development CPU GPU FPGA From the Intelligent Cloud to the Intelligent Edge Azure Notebooks JupyterVisual Studio Code Command line
  • 23.
    Choose any pythondevelopment environment And improve data science productivity PyCharmAzure NotebooksVisual Studio Code Command lineZeppelin Interactive widgets for Jupyter Notebooks Azure Machine Learning for Visual Studio Code extension Jupyter Get started with AML on Azure Notebooks: http://aka.ms/aznotebooks-aml
  • 24.
    RPN RoIP R-CNN Needsspecialized knowledgeTime-consuming Complex
  • 25.
    Domain specific pretrainedmodels To simplify solution development Popular frameworks To build advanced deep learning solutions Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Familiar Data Science tools To simplify model development From the Intelligent Cloud to the Intelligent Edge Azure Databricks Machine Learning VMs TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Scikit-Learn Azure Notebooks JupyterVisual Studio Code Command line CPU GPU FPGA
  • 26.
    Domain specific pretrainedmodels To simplify solution development Popular frameworks To build advanced deep learning solutions Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Familiar Data Science tools To simplify model development From the Intelligent Cloud to the Intelligent Edge Azure Databricks Machine Learning VMs TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Scikit-Learn Azure Notebooks JupyterVisual Studio Code Command line CPU GPU FPGA
  • 27.
    Build advanced deeplearning solutions Use your favorite deep learning frameworks without getting locked into one framework ONNX Community project created by Facebook and Microsoft Use the best tool for the job. Train in one framework and transfer to another for inference TensorFlow PyTorch Scikit-Learn MXNet Chainer Keras
  • 28.
    Frameworks Azure Create Deploy Services Devices AzureMachine Learning services Ubuntu VM Windows Server 2019 VM Azure Custom Vision Service ONNX Model Windows devices Other devices (iOS, etc.)
  • 29.
    Domain specific pretrainedmodels To simplify solution development Popular frameworks To build advanced deep learning solutions Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Familiar Data Science tools To simplify model development From the Intelligent Cloud to the Intelligent Edge Azure Databricks Machine Learning VMs TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Scikit-Learn Azure Notebooks JupyterVisual Studio Code Command line CPU GPU FPGA
  • 30.
    Domain specific pretrainedmodels To simplify solution development Popular frameworks To build advanced deep learning solutions Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Familiar Data Science tools To simplify model development From the Intelligent Cloud to the Intelligent Edge Azure Databricks Machine Learning VMs TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Scikit-Learn Azure Notebooks JupyterVisual Studio Code Command line CPU GPU FPGA
  • 31.
    + To empower datascience and development teams Develop models faster with automated machine learning Use any Python environment and ML frameworks Manage models across the cloud and the edge. Prepare data clean data at massive scale Enable collaboration between data scientists and data engineers Access machine learning optimized clusters Azure Machine Learning Python-based machine learning service Azure Databricks Apache Spark-based big-data service
  • 32.
    Bring AI toeveryone with an end-to-end, scalable, trusted platform Built with your needs in mind Support for open source frameworks Managed compute DevOps for machine learning Simple deployment Tool agnostic Python SDK Automated machine learning Seamlessly integrated with the Azure Portfolio Boost your data science productivity Increase your rate of experimentation Deploy and manage your models everywhere
  • 33.
    Fast, easy, andcollaborative Apache Spark™-based analytics platform Built with your needs in mind Optimized Apache Spark environmnet Collaborative workspace Integration with Azure data services Autoscale and autoterminate Optimized for distributed processing Support for multiple languages and libraries Seamlessly integrated with the Azure Portfolio Increase productivity Build on a secure, trusted cloud Scale without limits
  • 34.
    Leverage your favoritedeep learning frameworks AZURE ML SERVICE Increase your rate of experimentation Bring AI to the edge Deploy and manage your models everywhere TensorFlow MS Cognitive Toolkit PyTorch Scikit-Learn ONNX Caffe2 MXNet Chainer AZURE DATABRICKS Accelerate processing with the fastest Apache Spark engine Integrate natively with Azure services Access enterprise-grade Azure security
  • 35.
    What to usewhen? + Customer journey Data Prep Build and Train Manage and Deploy Apache Spark / Big Data Python ML developer Azure ML service (Pandas, NumPy etc. on AML Compute) Azure ML service (OSS frameworks, Hyperdrive, Pipelines, Automated ML, Model Registry) Azure ML service (containerize, deploy, inference and monitor) Azure ML service (containerize, deploy, inference and monitor) Azure Databricks (Apache Spark Dataframes, Datasets, Delta, Pandas, NumPy etc.) Azure Databricks + Azure ML service (Spark MLib and OSS frameworks + Automated ML, Model Registry)
  • 36.
    Domain specific pretrainedmodels To simplify solution development Popular frameworks To build advanced deep learning solutions Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Familiar Data Science tools To simplify model development From the Intelligent Cloud to the Intelligent Edge Azure Databricks Machine Learning VMs TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Scikit-Learn Azure Notebooks JupyterVisual Studio Code Command line CPU GPU FPGA
  • 37.
    Domain specific pretrainedmodels To simplify solution development Popular frameworks To build advanced deep learning solutions Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Familiar Data Science tools To simplify model development From the Intelligent Cloud to the Intelligent Edge Azure Databricks Machine Learning VMs TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Scikit-Learn Azure Notebooks JupyterVisual Studio Code Command line CPU GPU FPGA
  • 38.
    Accelerate deep learning Generalpurpose machine learning D, F, L, M, H Series CPUs Optimized for flexibility Optimized for performance GPUs FPGAs Deep learning N Series Specialized hardware accelerated deep learning AML hardware accelerated models (Project Brainwave)
  • 39.
    Simplify deployment +accelerate inferencing with ONNX runtime Track models in production Capture model telemetry From the Intelligent Cloud to the Intelligent Edge On-premisesCloud
  • 41.
    Flammable equipment near heatsource Missing equipment No hardhat
  • 42.
    Automated machine learningMachine learning DevOps Machine Learning 95 % Accelerated model building Azure DevOps integration for CI/CD
  • 43.
    Automated machine learningMachine learning DevOps Machine Learning 95 % Accelerated model building Azure DevOps integration for CI/CD
  • 44.
    How much isthis car worth? Azure Machine Learning Automated machine learning
  • 45.
    Model creation istypically a time consuming process Mileage Condition Car brand Year of make Regulations … Parameter 1 Parameter 2 Parameter 3 Parameter 4 … Gradient Boosted Nearest Neighbors SGD Bayesian Regression LGBM … Mileage Gradient Boosted Criterion Loss Min Samples Split Min Samples Leaf XYZ Model Which algorithm? Which parameters?Which features? Car brand Year of make
  • 46.
    Which algorithm? Whichparameters?Which features? Mileage Condition Car brand Year of make Regulations … Gradient Boosted Nearest Neighbors SGD Bayesian Regression LGBM … Nearest Neighbors Criterion Loss Min Samples Split Min Samples Leaf XYZ Model Iterate Gradient Boosted N Neighbors Weights Metric P ZYX Mileage Car brand Year of make Model creation is typically a time consuming process Car brand Year of make Condition
  • 47.
    Which algorithm? Whichparameters?Which features? Iterate Model creation is typically a time consuming process
  • 48.
    Enter data Define goals Applyconstraints Azure Machine Learning accelerates model development with automated machine learning Input Intelligently test multiple models in parallel Optimized model
  • 49.
    70%95% Azure Machine Learningaccelerates model selection with model explainability Feature importance Mileage Condition Car brand Year of make Regulations Model B (70%) Mileage Condition Car brand Year of make Regulations Feature importance Model A (95%)
  • 50.
    Automated machine learningMachine learning DevOps Machine Learning 95 % Accelerated model building Azure DevOps integration for CI/CD
  • 51.
    Register and Manage Model BuildImage Build model (your favorite IDE) Deploy Service Monitor Model Train & Test Model Integrated with Azure DevOps
  • 53.
    ServeStore Prep andtrainIngest Batch data Streaming data Azure Kubernetes service Power BI Azure analysis services Azure SQL data warehouse Cosmos DB, SQL DB Azure Data Lake Storage Azure Data Factory Azure Event Hubs Azure Databricks Azure Machine Learning service Apps Model Serving Ad-hoc Analysis Operational Databases
  • 54.
    PREP & TRAIN Collectand prepare data Train and evaluate model A B C Operationalize and manage
  • 55.
    Ingest Azure Data Factory Store Azure Blob Storage Understandand transform Azure Databricks • Leverage open source technologies • Collaborate within teams • Use ML on batch streams • Build in the language of your choice • Leverage scale out topology • Scale compute and storage separately • Integrate with all of your data sources • Create hybrid pipelines • Orchestrate in a code-free environment Leverage best-in-class analytics capabilities Scale without limits Connect to data from any source
  • 56.
    • Easily scaleup or scale out • Autoscale on serverless infrastructure • Leverage commodity hardware • Determine the best algorithm • Tune hyperparameters to optimize models • Rapidly prototype in agile environments • Collaborate in interactive workspaces • Access a library of battle-tested models • Automate job execution Scale compute resources to meet your needs Quickly determine the right model for your data Simplify model development Automated ML capabilities Automated ML Scale out clusters Infrastructure Machine learning Tools Azure Databricks Azure ML service Azure ML service Azure Databricks Azure ML service
  • 57.
    • Identify andpromote your best models • Capture model telemetry • Retrain models with APIs • Deploy models anywhere • Scale out to containers • Infuse intelligence into the IoT edge • Build and deploy models in minutes • Iterate quickly on serverless infrastructure • Easily change environments Proactively manage model performance Deploy models closer to your data Bring models to life quickly Train and evaluate models Model MGMT, experimentation, and run history Azure ML service Containers AKS ACI IoT edge Docker Azure ML service
  • 58.
    2022202120202019 Deep neural networkswill be a standard tool for 80% of data scientists1 More than 40% of data science tasks will be automated1 20% of companies will dedicate workers to monitor neural networks1 90% of modern analytics platforms will feature natural- language generation1 30% of net new revenue growth from industry-specific solutions will include AI1 1 in 5 workers engaged in mostly nonroutine tasks will rely on AI to do their jobs2 1 “100 Data and Analytics Predictions Through 2021”, Gartner, 2017. 2 “Predicts 2018: AI and the Future of Work”, Gartner, 2018. 2018
  • 59.
    • Choose VMsfor your modeling needs • Process video using GPU-based VMs • Run experiments in parallel • Provision resources automatically • Leverage popular deep learning toolkits • Develop your language of choice Scale compute resources in any environment Quickly evaluate and identify the right model Streamline AI development efforts AI Ready Virtual Machines Data Science VMs Notebooks, IDEs, Command Line Scale out clusters AML Compute MS Cognitive Toolkit Keras TensorFlow PyTorch Azure ML service SDK
  • 60.
    Deploy Azure MLmodels at scale Azure Machine Learning service
  • 61.
    Prepare Data Register and Manage Model Train& Test Model Build Image … Build model (your favorite IDE) Deploy Service Monitor Model Prepare Experiment Deploy DevOps loop for data science
  • 62.
    DevOps loop fordata science Prepare Data Prepare Register and Manage Model Build Image … Build model (your favorite IDE) Deploy Service Monitor Model Train & Test Model
  • 63.
    Model management inAzure Machine Learning Create/retrain model Create scoring files and dependencies Create and register image Monitor Register model Cloud Light Edge Heavy Edge Deploy image
  • 64.
    Use leaderboards, sideby side run comparison and model selection Conduct a hyperparameter search on traditional ML or DNN Leverage service-side capture of run metrics, output logs and models Manage training jobs locally, scaled-up or scaled-out Experimentation 95 % 80% 75% 90% 85%
  • 66.
    Azure Databricks Remotesupport for other IDEs outside of native notebooks MLFlow for better DevOps with Azure Databricks and other ML pipelines Azure Machine Learning Python SDK support for popular IDEs & notebooks, including Azure Databricks Azure Machine Learning managed compute capabilities Introduce new models for FPGA scoring Robust ONNX support - runtime engine in AML, model operationalization in SQL Server Automated machine learning Deploy and manage models to IoT edge Extend Machine Learning services to SQL DB
  • 67.
    © Microsoft Corporation Getstarted quickly with AI
  • 68.
    © Microsoft Corporation Intelligentapps Leverage AI to create the future of business applications Conversational agents Transform your engagements with customers and employees Business processes Transform critical business processes with AI Of customer interactions powered by AI bots by 2025 Applications to include AI by the end of this year 75% 85% Of enterprises using AI by 202095%
  • 69.
    © Microsoft Corporation Smartfactory Smart kitchen Smart bath Voice-activated speakers Smart cameras Drones Smart kiosks
  • 70.
    © Microsoft Corporation Employment Facilitatingdevelopment of professional skills Modern life Delivering personalized experiences to improve independence Human connection Providing equal access to information and opportunity How Microsoft is helping the visually-impaired How Helpicto is helping non-verbal children How RIT is helping the hearing-impaired Empowering people with tools that amplify human capability
  • 72.
    Customer analytics Financial modeling Risk, fraud, threat detection Credit analytics Marketing analytics Fasterinnovation for a better customer experience Improved consumer outcomes and increased revenue Enhanced customer experience with machine learning Transform growth with predictive analytics Improved customer engagement with machine learning Customer profiles Credit history Transactional data LTV Loyalty Customer segmentation CRM data Credit data Market data CRM Credit Risk Merchant records Products and services Clickstream data Products Services Customer service data Customer 360 degree evaluation Customer segmentation Reduced customer churn Underwriting, servicing and delinquency handling Insights for new products Commercial/retail banking, securities, trading and investment models Decision science, simulations and forecasting Investment recommendations Real-time anomaly detection Card monitoring and fraud detection Security threat identification Risk aggregation Enterprise DataHub Regulatory and compliance analysis Credit risk management Automated credit analytics Recommendation engine Predictive analytics and targeted advertising Fast marketing and multi- channel engagement Customer sentiment analysis Transaction data Demographics Purchasing history Trends Effective customer engagement Decision services management Risk and revenue management Risk and compliance management Recommendation engine
  • 73.
    Genomics and precision medicine Clinicaland claims data GPU image processing IoT device analytics Social analytics Faster innovation for drug development Improved outcomes and increased revenue Diagnostics leveraging machine learning Predictive analytics transforms quality of care Improved patient communications and feedback FAST-Q BAM SAM VCF Expression HL7/CCD 837 Pharmacy Registry EMR Readings Time series Event data Social media Adverse events Unstructured Single cell sequencing Biomarker, genetic, variant and population analytics ADAM and HAIL on Databricks Claims data warehouse Readmission predictions Efficacy and comparative analytics Prescription adherence Market access analysis Graphic intensive workloads Deep learning using Tensor Flow Pattern recognition Aggregation of streaming events Predictive maintenance Anomaly detection Real-time patient feedback via topic modelling Analytics across publication data MRI X-RAY CT Ultrasound DNA sequences Real world analytics Image deep learning Sensor data Social data listening
  • 74.
    Content personalization Customer churn prevention Recommendation engine Predictive analytics Sentiment analysis Faster innovation forcustomer experience Improved consumer outcomes and increased revenue Enhance user experience with machine learning Predictive analytics transforms growth Improved consumer engagement with machine learning Customer profiles Viewing history Online activity Content sources Channels Customer profiles Online activity Content distribution Services data Transactions Subscriptions Demographics Credit data Content metadata Ratings Comments Social media activity Personalized viewing and engagement experience Click-path optimization Next best content analysis Improved real time ad targeting Quality of service and operational efficiency Market basket analysis Customer behavior analysis Click-through analysis Ad effectiveness Content monetization Fraud detection Information-as-a-service High value user engagement Predict audience interests Network performance and optimization Pricing predictions Nielsen ratings and projections Mobile spatial analytics Demand-elasticity Social network analysis Promotion events time-series analysis Multi-channel marketing attribution Consumption logs Clickstream and devices Marketing campaign responses Personalized recommendations Effective customer retention Information optimization Inventory allocation Consumer engagement analysis
  • 75.
    Next best and personalizedoffers Store design and ergonomics Data-driven stock, inventory, ordering Assortment optimization Real-time pricing optimization Faster innovation for customer experience Improved consumer outcomes and increased revenue Omni-channel shopping experience with machine learning Predictive analytics transforms growth Improved consumer engagement with machine learning Customer profiles Shopping history Online activity Social network analysis Shopping history Online activity Floor plans App data Demographics Buyer perception Consumer research Market/competitive analysis Historical sales data Price scheduling Segment level price changes Customer 360/consumer personalization Right product, promotion, at right time Multi-channel promotion Path to purchase In-store experience Workforce and manpower optimization Predict inventory positions and distribution Fraud detection Market basket analysis Economic modelling Optimization for foot traffic, Online interactions Flat and declining categories Demand-elasticity Personal pricing schemes Promotion events Multi-channel engagement Demand plans Forecasts Sales history Trends Local events/weather patterns Recommendation engine Effective customer engagement Inventory optimization Inventory allocation Consumer engagement
  • 76.
    Customer value analytics Next bestand personalized offers Risk and fraud management Sales and campaign optimization Sentiment analysis Faster innovation for customer growth Improved outcomes and increased revenue Risk management with machine learning Predictive analytics transforms growth Improved customer engagement with machine learning Customer profiles Online history Transaction data Loyalty Customer segmentation CRM data Credit data Market data CRM Merchant records Products Services Marketing data Social media Online history Customer service data Customer 360, segmentation aggregation and attribution Audience modelling/index report Reduce customer churn Insights for new products Historical bid opportunity as a service Right product, promotion, at right time Real time ad bidding platform Personalized ad targeting Ad performance reporting Real-time anomaly detection Fraud prevention Customer spend and risk analysis Data relationship maps Optimizing return on ad spend and ad placement Multi-channel promotion Ideal customer traits Optimized ad placement Opinion mining/social media analysis Deeper customer insights Customer loyalty programs Shopping cart analysis Transaction data Demographics Purchasing history Trends Effective customer engagement Recommendation engine Risk and fraud analysis Campaign reporting analytics Brand promotion and customer experience
  • 77.
    Digital oil field/ oilproduction Industrial IoT Supply-chain optimization Safety and security Sales and marketing analytics Faster innovation for revenue growth Improved outcomes and increased revenue Optimizing supply- chain with machine learning Predictive analytics transforms safety and security Improved customer engagement with machine learning Field data Asset data Demographics Production data Sensor stream data UAVs images Inventory data Production data Sensor stream data Transport Retail data Grid production data Refinery tuning parameters Clickstream data Products Services Market data Competitive data Demographics Production optimization Integrate exploration and seismic data Minimize lease operating expenses Decline curve analysis Pipeline monitoring Preventive maintenance Smart grids and microgrids Grid operations, field service Asset performance as a service Trade monitoring, optimization Retail mobile applications Vendor management - construction, transportation, truck and delivery optimization Real-time anomaly detection Predictive analytics Industrial safety Environment health and safety Fast marketing and multi-channel engagement Develop new products and monitor acceptance of rates Predictive energy trading Deep customer insights Transaction data Demographics Purchasing history Trends Upstream optimization, maximize well life Grid operations, asset inventory optimization Supply-chain optimization Risk optimization Recommendations engine
  • 78.
    Intrusion detection and predictive analytics Security intelligence Frauddetection and prevention Security compliance reporting Fine-grained data analytics security Prevent complex threats with machine learning Faster innovation for threat prevention Risk management with machine learning Transform security with improved visibility Limit malicious insiders to transform growth Firewall/network logs Apps Data access layers Firewall/network logs Network flows Authentications Firewall/network logs Web Applications Devices OS Files Tables Clusters Reports Dashboards Notebooks Prevention of DDoS attacks Threat classifications Data loss/anomaly detection in streaming Cybermetrics and changing use patterns Real-time data correlation Anomaly detection Security context, enrichment Offence scoring, prioritization Security orchestration e-Tailing Inventory monitoring Social media monitoring Phishing scams Piracy protection Ad-hoc/historic incident reports SOC/NOC dashboards Deep OS auditing Data loss detection in IoT User behavior analytics Role-based access controls Auditing and governance File integrity monitoring Row level and column level access permissions Firewall/network logs Web/app logs Social media content Security controls to leverage all data Actionable threat intelligence Risk and fraud analysis Compliance management Identity and access management for analytics
  • 80.
  • 81.
  • 82.
    Use any language,any development tool and any framework >90% of Fortune 500 companies use Microsoft Cloud Benefit from industry-leading security, privacy, compliance, transparency, and AI ethics standards Accelerate time to value with agile tools and services Powerful tools Pretrained AI services Comprehensive platform On-premisesEdgeCloud Innovate with AI everywhere – in the cloud, at edge and on-premises
  • 83.
    © Microsoft Corporation It’sall on MicrosoftAzure
  • 84.
    © Microsoft Corporation Acceleratetime to market Productive Optimize your infrastructure Hybrid Intelligent Innovate at scale Develop with confidence Trusted Microsoft Azure
  • 85.
  • 86.
    © Microsoft Corporation SystemintegrationData integration BI and analytics