Pranav RastogiProduct Specialist for Big Data, Microsoft

With host Andrew BrustMarket Strategy Advisor, Io-TahoeCEO, Blue Badge Insights

Big Data and AI – does one serve the other?

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Speaker bios

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Pranav Rastogi• Leader on team for HDInsight, Microsoft Azure’s

Hadoop/Spark Big Data-as-a-Service offering• Developer ecosystem experience; tenure includes

roles on Azure Redis Cache and WebJobs services• Key contributor to .NET Web dev tools and platform

Andrew Brust• Covers Big Data and analytics for ZDNet• Strategy Advisor to Io-Tahoe• Data-focused tech career started in 1985

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Analytics analysis

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Evolution of analytics

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1. First there was query, drill-down and basic charts

2. Then came the Yahoo-like Big Data use casesBig Internet companies had the compute Cloud made this power accessible to other companies

3. Streaming data? Same story

4. Now analytics is about running stuff at scale

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Standardization of analytics

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Modern analytics is breaking down into four standardized workloads:• Batch• Query• Streaming• Data Science

If Data Science is just one of four standard workloads why does it seem that everyone is saying that’s the whole show?

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Deconstructing the AI

juggernaut

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Data Science audiences

Consumers• App developers• Apple Core ML, Tensor#, Mobius• Non-determinism is paradigm-

dissonant for developers• How to standardize conveyance

of accuracy/confidence level

Producers• Databricks, Sagemaker,

Azure Machine Learning (ML) are geared to this audience

• What about operationalization?

Users• Can accept/reject and

further train the model

Developer-Producers?

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Trends and market factors

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The move from batch to streaming

Also marks shift to time-series data

ML/predictive analytics follows, in turn

Spark SQLSnowflake

Databricks DeltaKSQL

SQL 2017 Native Scoring

Appeals to "Consumers" and "Users"

The SQL gold standardRecommendations

Time Series predictions (e.g. for revenue in Excel)

Smarter Tools:Excel

PowerPointIo-Tahoe

“Mainstream ML” examples

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Analytics as AI’s apprentice

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Analytics for analytics’ sakeUnderstanding truth is often

more important than predicting likelihood

Basic discovery helps identify what to predict

Common goal is to discover what the data can reveal

It’s all about AIAnalysis serves feature

engineering

Data prep serves to cleanse training data

Everything subservient to prediction

Embedded AI or DIY?

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Thank you

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