Practical Machine Learning in Information Security

Sven Krasser
Sven KrasserChief Scientist at CrowdStrike
PRACTICAL MACHINE LEARNING
IN INFORMATION SECURITY
DR. SVEN KRASSER CHIEF SCIENTIST
@SVENKRASSER
2017	CROWDSTRIKE,	INC.	ALL	RIGHTS	RESERVED.	
MACHINE LEARNING AT CROWDSTRIKE
§ ~40 billion events per day
§ ~800 thousand events per second peak
§ ~700 trillion bytes of sample data
§ Local decisions on endpoint and large scale analysis in cloud
§ Static and dynamic analysis techniques, various rich data sources
§ Analysts generating new ground truth 24/7
BRIEF ML
EXAMPLE
“Buttock Circumference” [mm]
Weight[10-1kg]
• What’s this?
http://tinyurl.com/MLprimer
• Two features
• Two classes
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
MODEL
FIT
“Buttock Circumference” [mm]
Weight[10-1kg]
• Support Vector
Machine
• Real world:
more features
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
2017	CROWDSTRIKE,	INC.	ALL	RIGHTS	RESERVED.	
ML IN INFOSEC APPLICATIONS
§ Not a single model solving everything
§ But many models working on the data in scope
§ Endpoint vs cloud
§ Fast response vs long observation
§ Lean vs resource intensive
§ Effectiveness vs interpretability
§ Avoid ML blinders
§ The guy in your store at 2am wielding a crowbar is not a customer
CHALLENGES FOR
APPLIED ML
2017	CROWDSTRIKE,	INC.	ALL	RIGHTS	RESERVED.	
FALSE POSITIVE RATE
§ Most events are associated with clean executions
§ Most files on a given system are clean
§ Therefore, even low FPRs cause large numbers of FPs
§ Industry expectations driven by performance of narrow signatures
TRUE POSITIVE RATE
8
Chanceofatleastone
successforadversary
Number of attempts at 99% detection rate
1%
>99.3%
500
UNWIELDY DATA
2017	CROWDSTRIKE,	INC.	ALL	RIGHTS	RESERVED.	
§ Many outliers
§ Multimodal distributions
§ Sometimes narrow modes far
apart
§ Adversary-controlled features
§ Mix of sparse/dense and
discrete/continuous features
2017	CROWDSTRIKE,	INC.	ALL	RIGHTS	RESERVED.	
Training set distribution generally differs from…
DIFFERENCE IN DISTRIBUTIONS
§ Real-world distribution (customer networks)
§ Evaluations (what customers test)
§ Testing houses (various 3rd party testers with varying methodologies)
§ Community resources (e.g. user submissions to CrowdStrike scanner on
VirusTotal)
2017	CROWDSTRIKE,	INC.	ALL	RIGHTS	RESERVED.	
Or: the second model needs to be cheaper
REPEATABLE SUCCESS
§ Retraining cadence
§ Concept drift
§ Changes in data content (e.g. event field definitions)
§ Changes in data distribution (e.g. event disposition)
§ Data cleansing is expensive (conventional wisdom)
§ Needs automation
§ Labeling can be expensive
§ Ephemeral instances (data content or distribution changed)
§ Lack of sufficient observations
§ Embeddings and intermediate models
§ Keep track of input data
§ Keep track of ground truth budget
GLOBAL BEHAVIORAL
ANALYSIS
2017	CROWDSTRIKE,	INC.	ALL	RIGHTS	RESERVED.	
CLASSIFYING EVENT DATA
§ Idea: global classification
§ Observe all executions for a file, not just a single one
§ Initially only behavioral event data
§ In later versions also combined with static analysis data
§ Early project, focus on the data already there
§ Events fall into various categories, mainly:
§ Process data (hub)
§ Network data
§ DNS data
§ File system data
§ Capping data at 100 seconds since process start
§ Carving out a smaller problem
§ Ignoring classes of malware that are idle initially
2017	CROWDSTRIKE,	INC.	ALL	RIGHTS	RESERVED.	
RELEVANT ARCHITECTURE IN A NUTSHELL
Event	
collector
Message	
bus
Sensor	
population
S3
Spark
Hash	
DB
Cloud
15
HIGH-LEVEL JOB FLOW
Read	in	event	
data
•Filter	by	event	type
•Filter	unneeded	
fields
Aggregate	per-
process	data
•Add	derived	features
•One-to-one:	
combine	events	
process	creation	and	
termination
•One-to-many:	
combine	events	such	
as	DNS	requests	
(many	per	process)	
and	add	result	to	
process	record
Direct	children
•Join	to	parents	and	
copy	parent	data	
into	children
•Aggregate	children	
features	by	their	
parent
Second	order	
children
•Aggregate	second	
order	children	by	
their	parent’s	parent
•Aggregate	with	
direct	children
Process	features
•Combine	process	
data	with	children	
data
Hash	features
•Roll	up	all	process	
data	by	hash
•Output	per-hash	
statistics	as	
behavioral	features
2017	CrowdStrike,	Inc.	All	rights	reserved.
Process records
Children records
Hash record
...
(a)
(a)
(b)
(b)
(c)
(d)
AGGREGATION
2015	CrowdStrike,	Inc.	All	rights	reserved.16
LABELS
2017	CrowdStrike,	Inc.	All	rights	reserved.
Clean
training data
Dirty
training data
Sandbox
deployment
Unlabeled
data
Large scale
field
deployment
§ Field data contains too little
malware
§ Extra malware executions in
sandbox
§ Need to consider bias introduced
by sandbox
§ Parent process
§ Execution time
§ Location of file
2017	CROWDSTRIKE,	INC.	ALL	RIGHTS	RESERVED.	
SPARKJOBDAG
2017	CROWDSTRIKE,	INC.	ALL	RIGHTS	RESERVED.	
Challenges & Lessons Learned
PROCESSING WITH SPARK
§ Issues due to data size
§ Lots of cycles sunk into tuning memory parameters to address job failures
§ Job structure and recovery considerations (reprocessing not always viable)
§ Issues due to input data model
§ Highly referential event data, spreading information across many real-time events
§ Flattened tree/graph-based data
§ Complex to handle in Spark’s RDD model (see DAG)
§ Abstractions such as GraphX may help
§ Processing overhead
§ Job based on Pyspark RDDs – most time spent on serialization/deserialization
§ Initial investment in migrating to Scala would have paid off in deployment
§ Life is now better with Dataframe API
§ Development velocity with Spark
§ Trivial to set up a local dev environment
§ Trivial to add unit tests
Smaller Larger
2017	CROWDSTRIKE,	INC.	ALL	RIGHTS	RESERVED.	
EVOLUTION
§ Operating on
fewer events
§ Rich event
data
§ Very fast
decisions
§ Moving event
correlation
into graph
database
§ Operating on
large event
volumes
ML-BASED ANTI-
MALWARE
VIRUSTOTAL INTEGRATION
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
FILE
ANALYSIS
AKA Static Analysis
• THE GOOD
– Relatively fast
– Scalable
– No need to detonate
– Platform independent, can be done at gateway or cloud
• THE BAD
– Limited insight due to narrow view
– Different file types require different techniques
– Different subtypes need special consideration
– Packed files
– .Net
– Installers
– EXEs vs DLLs
– Obfuscations (yet good if detectable)
– Ineffective against exploitation and malware-less attacks
– Asymmetry: a fraction of a second to decide for the
defender, months to craft for the attacker
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
ENGINEERED FEATURES
32/64BIT
EXECUTABLE
GUI
SUBSYSTEM
COMMAND
LINE
SUBSYSTEM
FILESIZE TIMESTAMP
DEBUG
INFORMATION
PRESENT
PACKERTYPE FILEENTROPY
NUMBEROF
SECTIONS
NUMBER
WRITABLE
NUMBER
READABLE
NUMBER
EXECUTABLE
DISTRIBUTION
OFSECTION
ENTROPY
IMPORTED
DLLNAMES
IMPORTED
FUNCTION
NAMES
COMPILER
ARTIFACTS
LINKER
ARTIFACTS
RESOURCE
DATA
PROTOCOL
STRINGS
IPS/DOMAINS
PATHS
PRODUCT
METADATA
DIGITAL
SIGNATURE
ICON
CONTENT
…
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
LEARNED
FEATURES
• Unstructured file
content
• Translated into
embeddings
• Vastly larger
corpus (no labels
needed)
String-based feature
Executablesectionsize-based
feature
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
COMBINING
FEATURES
Subspace Projection A
SubspaceProjectionB
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
COMBINING
FEATURES
PRODUCTIONFLOW
2017	CROWDSTRIKE,	INC.	ALL	RIGHTS	RESERVED.	
Sample	Data
Labels
Cloud	FX	
Engine
Model
Embed
Embed
PRODUCTIONFLOW
2017	CROWDSTRIKE,	INC.	ALL	RIGHTS	RESERVED.	
Sample	Data
Labels
Learned	
Features	and	
Embeddings
Cloud	FX	
Engine
Sensor	FX	
Engine
Feed	
Processing
Re-
processing
μService FX	Worker Endpoints
Docker
Feature rankings
2017	CROWDSTRIKE,	INC.	ALL	RIGHTS	RESERVED.	
Challenges & Lessons Learned
STATIC ANALYSIS
§ Performance
§ Acceptable results can be achieved quickly
§ State-of-the art results require a bit more tweaking and feature engineering
§ Staying current requires a maintainable data pipeline
§ Hostile data
§ Wild outliers, e.g. PNG width is encoded in 4 bytes
§ All sorts of obfuscations and malformations
§ PE format !(ಠ益ಠ!)
§ What the standard says, what the loader allows…
§ Layers upon layers in an electronic archeological excavation
§ Not everything is documented
§ Tons of subtypes
§ More work
§ More opportunity
Practical Machine Learning in Information Security
1 of 31

Recommended

IJCNN 2017 by
IJCNN 2017IJCNN 2017
IJCNN 2017Sven Krasser
1.7K views9 slides
A Sober Look at Machine Learning by
A Sober Look at Machine LearningA Sober Look at Machine Learning
A Sober Look at Machine LearningSven Krasser
1.1K views54 slides
Of Search Lights and Blind Spots: Machine Learning in Cybersecurity by
Of Search Lights and Blind Spots: Machine Learning in CybersecurityOf Search Lights and Blind Spots: Machine Learning in Cybersecurity
Of Search Lights and Blind Spots: Machine Learning in CybersecuritySven Krasser
561 views30 slides
Straight Talk on Machine Learning -- What the Marketing Department Doesn’t Wa... by
Straight Talk on Machine Learning -- What the Marketing Department Doesn’t Wa...Straight Talk on Machine Learning -- What the Marketing Department Doesn’t Wa...
Straight Talk on Machine Learning -- What the Marketing Department Doesn’t Wa...Sven Krasser
1.2K views41 slides
Fundamentals of Machine Learning: Perspectives from a Data Scientist (ISC Wes... by
Fundamentals of Machine Learning: Perspectives from a Data Scientist (ISC Wes...Fundamentals of Machine Learning: Perspectives from a Data Scientist (ISC Wes...
Fundamentals of Machine Learning: Perspectives from a Data Scientist (ISC Wes...Sven Krasser
1K views51 slides
IoT-Shield: A Novel DDoS Detection Approach for IoT-Based Devices by
IoT-Shield: A Novel DDoS Detection Approach for IoT-Based DevicesIoT-Shield: A Novel DDoS Detection Approach for IoT-Based Devices
IoT-Shield: A Novel DDoS Detection Approach for IoT-Based Devicessaeid ghasemshirazi
59 views11 slides

More Related Content

What's hot

Threat Hunting for Command and Control Activity by
Threat Hunting for Command and Control ActivityThreat Hunting for Command and Control Activity
Threat Hunting for Command and Control ActivitySqrrl
670 views31 slides
Elastic Stack Roadmap by
Elastic Stack RoadmapElastic Stack Roadmap
Elastic Stack RoadmapImma Valls Bernaus
96 views72 slides
Episode IV: A New Scope by
Episode IV: A New ScopeEpisode IV: A New Scope
Episode IV: A New ScopeThreatConnect
290 views20 slides
The Art and Science of Alert Triage by
The Art and Science of Alert TriageThe Art and Science of Alert Triage
The Art and Science of Alert TriageSqrrl
1.2K views21 slides
Threat Hunting Platforms (Collaboration with SANS Institute) by
Threat Hunting Platforms (Collaboration with SANS Institute)Threat Hunting Platforms (Collaboration with SANS Institute)
Threat Hunting Platforms (Collaboration with SANS Institute)Sqrrl
1.4K views13 slides
Au cœur de la roadmap de la Suite Elastic by
Au cœur de la roadmap de la Suite ElasticAu cœur de la roadmap de la Suite Elastic
Au cœur de la roadmap de la Suite ElasticElasticsearch
611 views72 slides

What's hot(20)

Threat Hunting for Command and Control Activity by Sqrrl
Threat Hunting for Command and Control ActivityThreat Hunting for Command and Control Activity
Threat Hunting for Command and Control Activity
Sqrrl670 views
The Art and Science of Alert Triage by Sqrrl
The Art and Science of Alert TriageThe Art and Science of Alert Triage
The Art and Science of Alert Triage
Sqrrl1.2K views
Threat Hunting Platforms (Collaboration with SANS Institute) by Sqrrl
Threat Hunting Platforms (Collaboration with SANS Institute)Threat Hunting Platforms (Collaboration with SANS Institute)
Threat Hunting Platforms (Collaboration with SANS Institute)
Sqrrl1.4K views
Au cœur de la roadmap de la Suite Elastic by Elasticsearch
Au cœur de la roadmap de la Suite ElasticAu cœur de la roadmap de la Suite Elastic
Au cœur de la roadmap de la Suite Elastic
Elasticsearch611 views
Modernizing Your SOC: A CISO-led Training by Sqrrl
Modernizing Your SOC: A CISO-led TrainingModernizing Your SOC: A CISO-led Training
Modernizing Your SOC: A CISO-led Training
Sqrrl347 views
Machine Learning for Incident Detection: Getting Started by Sqrrl
Machine Learning for Incident Detection: Getting StartedMachine Learning for Incident Detection: Getting Started
Machine Learning for Incident Detection: Getting Started
Sqrrl972 views
Managing Indicator Deprecation in ThreatConnect by ThreatConnect
Managing Indicator Deprecation in ThreatConnectManaging Indicator Deprecation in ThreatConnect
Managing Indicator Deprecation in ThreatConnect
ThreatConnect414 views
Grace Hopper Open Source Day Findings | Thorn & Cloudera Cares by Cloudera, Inc.
Grace Hopper Open Source Day Findings | Thorn & Cloudera CaresGrace Hopper Open Source Day Findings | Thorn & Cloudera Cares
Grace Hopper Open Source Day Findings | Thorn & Cloudera Cares
Cloudera, Inc.2.1K views
The Security Industry is Suffering from Fragmentation, What Can Your Organiza... by ThreatConnect
The Security Industry is Suffering from Fragmentation, What Can Your Organiza...The Security Industry is Suffering from Fragmentation, What Can Your Organiza...
The Security Industry is Suffering from Fragmentation, What Can Your Organiza...
ThreatConnect301 views
Building a Real-Time Gaming Analytics Service with Apache Druid by Imply
Building a Real-Time Gaming Analytics Service with Apache DruidBuilding a Real-Time Gaming Analytics Service with Apache Druid
Building a Real-Time Gaming Analytics Service with Apache Druid
Imply 250 views
Art into Science 2017 - Investigation Theory: A Cognitive Approach by chrissanders88
Art into Science 2017 - Investigation Theory: A Cognitive ApproachArt into Science 2017 - Investigation Theory: A Cognitive Approach
Art into Science 2017 - Investigation Theory: A Cognitive Approach
chrissanders881.1K views
Abstract Tools for Effective Threat Hunting by chrissanders88
Abstract Tools for Effective Threat HuntingAbstract Tools for Effective Threat Hunting
Abstract Tools for Effective Threat Hunting
chrissanders881.7K views
University of Oxford: building a next generation SIEM by Elasticsearch
University of Oxford: building a next generation SIEMUniversity of Oxford: building a next generation SIEM
University of Oxford: building a next generation SIEM
Elasticsearch10.9K views
Troubleshooting your elasticsearch cluster like a support engineer by Imma Valls Bernaus
Troubleshooting your elasticsearch cluster like a support engineerTroubleshooting your elasticsearch cluster like a support engineer
Troubleshooting your elasticsearch cluster like a support engineer
SOC2016 - The Investigation Labyrinth by chrissanders88
SOC2016 - The Investigation LabyrinthSOC2016 - The Investigation Labyrinth
SOC2016 - The Investigation Labyrinth
chrissanders882.2K views
Threat Hunting with Elastic at SpectorOps: Welcome to HELK by Elasticsearch
Threat Hunting with Elastic at SpectorOps: Welcome to HELKThreat Hunting with Elastic at SpectorOps: Welcome to HELK
Threat Hunting with Elastic at SpectorOps: Welcome to HELK
Elasticsearch20.3K views
User and Entity Behavior Analytics using the Sqrrl Behavior Graph by Sqrrl
User and Entity Behavior Analytics using the Sqrrl Behavior GraphUser and Entity Behavior Analytics using the Sqrrl Behavior Graph
User and Entity Behavior Analytics using the Sqrrl Behavior Graph
Sqrrl1.3K views

Similar to Practical Machine Learning in Information Security

Webinar: ROI on Big Data - RDBMS, NoSQL or Both? A Simple Guide for Knowing H... by
Webinar: ROI on Big Data - RDBMS, NoSQL or Both? A Simple Guide for Knowing H...Webinar: ROI on Big Data - RDBMS, NoSQL or Both? A Simple Guide for Knowing H...
Webinar: ROI on Big Data - RDBMS, NoSQL or Both? A Simple Guide for Knowing H...DataStax
5.4K views40 slides
Horses for Courses: Database Roundtable by
Horses for Courses: Database RoundtableHorses for Courses: Database Roundtable
Horses for Courses: Database RoundtableEric Kavanagh
258 views34 slides
How To Tell if Your Business Needs NoSQL by
How To Tell if Your Business Needs NoSQLHow To Tell if Your Business Needs NoSQL
How To Tell if Your Business Needs NoSQLDataStax
3.7K views41 slides
TidalScale Overview by
TidalScale OverviewTidalScale Overview
TidalScale OverviewPete Jarvis
1K views21 slides
Tapping the cloud for real time data analytics by
 Tapping the cloud for real time data analytics Tapping the cloud for real time data analytics
Tapping the cloud for real time data analyticsAmazon Web Services
1.1K views29 slides
The Last Frontier- Virtualization, Hybrid Management and the Cloud by
The Last Frontier-  Virtualization, Hybrid Management and the CloudThe Last Frontier-  Virtualization, Hybrid Management and the Cloud
The Last Frontier- Virtualization, Hybrid Management and the CloudKellyn Pot'Vin-Gorman
294 views46 slides

Similar to Practical Machine Learning in Information Security(20)

Webinar: ROI on Big Data - RDBMS, NoSQL or Both? A Simple Guide for Knowing H... by DataStax
Webinar: ROI on Big Data - RDBMS, NoSQL or Both? A Simple Guide for Knowing H...Webinar: ROI on Big Data - RDBMS, NoSQL or Both? A Simple Guide for Knowing H...
Webinar: ROI on Big Data - RDBMS, NoSQL or Both? A Simple Guide for Knowing H...
DataStax5.4K views
Horses for Courses: Database Roundtable by Eric Kavanagh
Horses for Courses: Database RoundtableHorses for Courses: Database Roundtable
Horses for Courses: Database Roundtable
Eric Kavanagh258 views
How To Tell if Your Business Needs NoSQL by DataStax
How To Tell if Your Business Needs NoSQLHow To Tell if Your Business Needs NoSQL
How To Tell if Your Business Needs NoSQL
DataStax3.7K views
TidalScale Overview by Pete Jarvis
TidalScale OverviewTidalScale Overview
TidalScale Overview
Pete Jarvis1K views
Tapping the cloud for real time data analytics by Amazon Web Services
 Tapping the cloud for real time data analytics Tapping the cloud for real time data analytics
Tapping the cloud for real time data analytics
Amazon Web Services1.1K views
The Last Frontier- Virtualization, Hybrid Management and the Cloud by Kellyn Pot'Vin-Gorman
The Last Frontier-  Virtualization, Hybrid Management and the CloudThe Last Frontier-  Virtualization, Hybrid Management and the Cloud
The Last Frontier- Virtualization, Hybrid Management and the Cloud
Smack Stack and Beyond—Building Fast Data Pipelines with Jorg Schad by Spark Summit
Smack Stack and Beyond—Building Fast Data Pipelines with Jorg SchadSmack Stack and Beyond—Building Fast Data Pipelines with Jorg Schad
Smack Stack and Beyond—Building Fast Data Pipelines with Jorg Schad
Spark Summit2.1K views
Webinar - Big Data: Let's SMACK - Jorg Schad by Codemotion
Webinar - Big Data: Let's SMACK - Jorg SchadWebinar - Big Data: Let's SMACK - Jorg Schad
Webinar - Big Data: Let's SMACK - Jorg Schad
Codemotion1.4K views
Big Data on Cloud Native Platform by Sunil Govindan
Big Data on Cloud Native PlatformBig Data on Cloud Native Platform
Big Data on Cloud Native Platform
Sunil Govindan127 views
Big Data on Cloud Native Platform by Sunil Govindan
Big Data on Cloud Native PlatformBig Data on Cloud Native Platform
Big Data on Cloud Native Platform
Sunil Govindan23 views
CrowdStrike CrowdCast: Is Ransomware Morphing Beyond The Ability Of Standard ... by CrowdStrike
CrowdStrike CrowdCast: Is Ransomware Morphing Beyond The Ability Of Standard ...CrowdStrike CrowdCast: Is Ransomware Morphing Beyond The Ability Of Standard ...
CrowdStrike CrowdCast: Is Ransomware Morphing Beyond The Ability Of Standard ...
CrowdStrike1.1K views
(ENT211) Migrating the US Government to the Cloud | AWS re:Invent 2014 by Amazon Web Services
(ENT211) Migrating the US Government to the Cloud | AWS re:Invent 2014(ENT211) Migrating the US Government to the Cloud | AWS re:Invent 2014
(ENT211) Migrating the US Government to the Cloud | AWS re:Invent 2014
Amazon Web Services1.3K views
Data lake-itweekend-sharif university-vahid amiry by datastack
Data lake-itweekend-sharif university-vahid amiryData lake-itweekend-sharif university-vahid amiry
Data lake-itweekend-sharif university-vahid amiry
datastack524 views
Big Data Session 1.pptx by ElsonPaul2
Big Data Session 1.pptxBig Data Session 1.pptx
Big Data Session 1.pptx
ElsonPaul23 views
First in Class: Optimizing the Data Lake for Tighter Integration by Inside Analysis
First in Class: Optimizing the Data Lake for Tighter IntegrationFirst in Class: Optimizing the Data Lake for Tighter Integration
First in Class: Optimizing the Data Lake for Tighter Integration
Inside Analysis793 views
Webinar | From Zero to 1 Million with Google Cloud Platform and DataStax by DataStax
Webinar | From Zero to 1 Million with Google Cloud Platform and DataStaxWebinar | From Zero to 1 Million with Google Cloud Platform and DataStax
Webinar | From Zero to 1 Million with Google Cloud Platform and DataStax
DataStax2.6K views
RISELab:Enabling Intelligent Real-Time Decisions by Jen Aman
RISELab:Enabling Intelligent Real-Time DecisionsRISELab:Enabling Intelligent Real-Time Decisions
RISELab:Enabling Intelligent Real-Time Decisions
Jen Aman595 views
RISELab: Enabling Intelligent Real-Time Decisions keynote by Ion Stoica by Spark Summit
RISELab: Enabling Intelligent Real-Time Decisions keynote by Ion StoicaRISELab: Enabling Intelligent Real-Time Decisions keynote by Ion Stoica
RISELab: Enabling Intelligent Real-Time Decisions keynote by Ion Stoica
Spark Summit2.3K views
Using real time big data analytics for competitive advantage by Amazon Web Services
 Using real time big data analytics for competitive advantage Using real time big data analytics for competitive advantage
Using real time big data analytics for competitive advantage
Amazon Web Services2.3K views
Estimating the Total Costs of Your Cloud Analytics Platform by DATAVERSITY
Estimating the Total Costs of Your Cloud Analytics PlatformEstimating the Total Costs of Your Cloud Analytics Platform
Estimating the Total Costs of Your Cloud Analytics Platform
DATAVERSITY335 views

Recently uploaded

The Power of Heat Decarbonisation Plans in the Built Environment by
The Power of Heat Decarbonisation Plans in the Built EnvironmentThe Power of Heat Decarbonisation Plans in the Built Environment
The Power of Heat Decarbonisation Plans in the Built EnvironmentIES VE
85 views20 slides
MVP and prioritization.pdf by
MVP and prioritization.pdfMVP and prioritization.pdf
MVP and prioritization.pdfrahuldharwal141
40 views8 slides
Business Analyst Series 2023 - Week 4 Session 8 by
Business Analyst Series 2023 -  Week 4 Session 8Business Analyst Series 2023 -  Week 4 Session 8
Business Analyst Series 2023 - Week 4 Session 8DianaGray10
180 views13 slides
Webinar : Desperately Seeking Transformation - Part 2: Insights from leading... by
Webinar : Desperately Seeking Transformation - Part 2:  Insights from leading...Webinar : Desperately Seeking Transformation - Part 2:  Insights from leading...
Webinar : Desperately Seeking Transformation - Part 2: Insights from leading...The Digital Insurer
98 views52 slides
Measuring User on the web with the core web vitals - by @theafolayan.pptx by
Measuring User on the web with the core web vitals - by @theafolayan.pptxMeasuring User on the web with the core web vitals - by @theafolayan.pptx
Measuring User on the web with the core web vitals - by @theafolayan.pptxOluwaseun Raphael Afolayan
14 views13 slides
"Node.js Development in 2024: trends and tools", Nikita Galkin by
"Node.js Development in 2024: trends and tools", Nikita Galkin "Node.js Development in 2024: trends and tools", Nikita Galkin
"Node.js Development in 2024: trends and tools", Nikita Galkin Fwdays
37 views38 slides

Recently uploaded(20)

The Power of Heat Decarbonisation Plans in the Built Environment by IES VE
The Power of Heat Decarbonisation Plans in the Built EnvironmentThe Power of Heat Decarbonisation Plans in the Built Environment
The Power of Heat Decarbonisation Plans in the Built Environment
IES VE85 views
Business Analyst Series 2023 - Week 4 Session 8 by DianaGray10
Business Analyst Series 2023 -  Week 4 Session 8Business Analyst Series 2023 -  Week 4 Session 8
Business Analyst Series 2023 - Week 4 Session 8
DianaGray10180 views
Webinar : Desperately Seeking Transformation - Part 2: Insights from leading... by The Digital Insurer
Webinar : Desperately Seeking Transformation - Part 2:  Insights from leading...Webinar : Desperately Seeking Transformation - Part 2:  Insights from leading...
Webinar : Desperately Seeking Transformation - Part 2: Insights from leading...
"Node.js Development in 2024: trends and tools", Nikita Galkin by Fwdays
"Node.js Development in 2024: trends and tools", Nikita Galkin "Node.js Development in 2024: trends and tools", Nikita Galkin
"Node.js Development in 2024: trends and tools", Nikita Galkin
Fwdays37 views
The Coming AI Tsunami.pptx by johnhandby
The Coming AI Tsunami.pptxThe Coming AI Tsunami.pptx
The Coming AI Tsunami.pptx
johnhandby14 views
"Package management in monorepos", Zoltan Kochan by Fwdays
"Package management in monorepos", Zoltan Kochan"Package management in monorepos", Zoltan Kochan
"Package management in monorepos", Zoltan Kochan
Fwdays37 views
"Running students' code in isolation. The hard way", Yurii Holiuk by Fwdays
"Running students' code in isolation. The hard way", Yurii Holiuk "Running students' code in isolation. The hard way", Yurii Holiuk
"Running students' code in isolation. The hard way", Yurii Holiuk
Fwdays38 views
Future of AR - Facebook Presentation by Rob McCarty
Future of AR - Facebook PresentationFuture of AR - Facebook Presentation
Future of AR - Facebook Presentation
Rob McCarty66 views
What is Authentication Active Directory_.pptx by HeenaMehta35
What is Authentication Active Directory_.pptxWhat is Authentication Active Directory_.pptx
What is Authentication Active Directory_.pptx
HeenaMehta3515 views
Adopting Karpenter for Cost and Simplicity at Grafana Labs.pdf by MichaelOLeary82
Adopting Karpenter for Cost and Simplicity at Grafana Labs.pdfAdopting Karpenter for Cost and Simplicity at Grafana Labs.pdf
Adopting Karpenter for Cost and Simplicity at Grafana Labs.pdf
MichaelOLeary8213 views
Zero to Cloud Hero: Crafting a Private Cloud from Scratch with XCP-ng, Xen Or... by ShapeBlue
Zero to Cloud Hero: Crafting a Private Cloud from Scratch with XCP-ng, Xen Or...Zero to Cloud Hero: Crafting a Private Cloud from Scratch with XCP-ng, Xen Or...
Zero to Cloud Hero: Crafting a Private Cloud from Scratch with XCP-ng, Xen Or...
ShapeBlue209 views
Mobile Core Solutions & Successful Cases.pdf by IPLOOK Networks
Mobile Core Solutions & Successful Cases.pdfMobile Core Solutions & Successful Cases.pdf
Mobile Core Solutions & Successful Cases.pdf
IPLOOK Networks16 views
Enabling DPU Hardware Accelerators in XCP-ng Cloud Platform Environment - And... by ShapeBlue
Enabling DPU Hardware Accelerators in XCP-ng Cloud Platform Environment - And...Enabling DPU Hardware Accelerators in XCP-ng Cloud Platform Environment - And...
Enabling DPU Hardware Accelerators in XCP-ng Cloud Platform Environment - And...
ShapeBlue120 views
Cocktail of Environments. How to Mix Test and Development Environments and St... by Aleksandr Tarasov
Cocktail of Environments. How to Mix Test and Development Environments and St...Cocktail of Environments. How to Mix Test and Development Environments and St...
Cocktail of Environments. How to Mix Test and Development Environments and St...
Deep Tech and the Amplified Organisation: Core Concepts by Holonomics
Deep Tech and the Amplified Organisation: Core ConceptsDeep Tech and the Amplified Organisation: Core Concepts
Deep Tech and the Amplified Organisation: Core Concepts
Holonomics17 views
AI + Memoori = AIM by Memoori
AI + Memoori = AIMAI + Memoori = AIM
AI + Memoori = AIM
Memoori15 views

Practical Machine Learning in Information Security