SlideShare una empresa de Scribd logo
1 de 65
Descargar para leer sin conexión
Advanced Model Inferencing leveraging Kubeflow Serving, KNative and Istio
Animesh Singh - IBM
Clive Cox - Seldon
Advanced Model Serving Leveraging
KNative, Istio and Kubeflow Serving
Agenda
●  Introduction to Machine Learning and its challenges
●  Introduction to Model Inferencing and its challenges
●  Production ML Serving
●  Monitoring ML Models
●  Summary and Roadmap
4	
Enterprise Machine Learning
*Source: Hidden Technical Debt in Machine Learning Systems
Perception
*Source: Hidden Technical Debt in Machine Learning Systems
In reality…ML Code is tiny part in this overall
platform
Data
cleansing
Data	analysis	&	
transformation
Data
validation
Data
splitting
Data
prep
Building
a model
Model
validation
Training
at scale
Model
creation
Deploying Serving
Monitoring &
Logging +
Explainability
Finetune &
improvements
Rollout
Training
optimization Model
Model
Data
Data
Data
ingestion
EdgeCloud
DataflowandWorkflowOrchestration
Marketplace	(AI	Hub)
Data	consistency	(versioning)
Feature	
Engineering
ML Workflow
8	
End to end ML on Kubernetes?	
●  Containers	
●  Packaging	
●  Kubernetes	service	endpoints	
●  Persistent	volumes	
●  Scaling	
●  Immutable	deployments	
●  GPUs,	Drivers	&	the	GPL	
●  Cloud	APIs	
●  DevOps	
●  ...	
First, can you become an expert in ...
9	
Introducing:Kubeflow
Distributed Model Training and HPO
(TFJob, PyTorch Job, Katib, …)
•  Addresses	One	of	the	key	goals	for	model	builder	
persona:		
	
Distributed	Model	Training	and	Hyper	parameter	
optimization	for	Tensorflow,	PyTorch	etc.	
	
•  Common	problems	in	HP	optimization	
•  Overfitting	
•  Wrong	metrics	
•  Too	few	hyperparameters	
	
•  Katib:	a	fully	open	source,	Kubernetes-native	
hyperparameter	tuning	service	
•  Inspired	by	Google	Vizier	
•  Framework	agnostic	
•  Extensible	algorithms	
•  Simple	integration	with	other	Kubeflow	
components	
	
•  Kubeflow	also	supports	distributed	MPI	based	training	
using	Horovod
Kubeflow Pipelines
§  Containerized implementations of ML Tasks
§  Pre-built components: Just provide params or code
snippets (e.g. training code)
§  Create	your	own	components	from	code	or	libraries	
§  Use	any	runtime,	framework,	data	types	
§  Attach	k8s	objects	-	volumes,	secrets
§  Specification of the sequence of steps
§  Specified via Python DSL
§  Inferred from data dependencies on input/output
§  Input Parameters
§  A “Run” = Pipeline invoked w/ specific parameters
§  Can be cloned with different parameters
§  Schedules	
§  Invoke a single run or create a recurring scheduled
pipeline
12	
IBM and Seldon
Major Contributors Source devstats.org
Community	is	growing!	
13
Develop
(Jupyter)
Deploy
(KFServing)
Train
(Job CRs)
Build
(Fairing)
Core CUJ
Kubeflow	1.0	Arriving	January	2020		
http://bit.ly/kf_roadmap
Production Model Serving
❑  Rollouts:
Is this rollout safe? How do I roll
back? Can I test a change
without swapping traffic?
❑  Protocol Standards:
How do I make a prediction?
GRPC? HTTP? Kafka?
❑  Cost:
Is the model over or under scaled?
Are resources being used efficiently?
❑  Monitoring:
Are the endpoints healthy? What is
the performance profile and request
trace?
Prepared
and
Analyzed
Data
Trained
Model
Deployed
Model
Prepared
Data
Untrained
Model
❑  Frameworks:
How do I serve on Tensorflow?
XGBoost? Scikit Learn? Pytorch?
Custom Code?
❑  Features:
How do I explain the predictions?
What about detecting outliers and
skew? Bias detection? Adversarial
Detection?
❑  How do I wire up custom pre and
post processing
Production Model Serving?
How hard could it be?
Experts fragmented across industry
●  Seldon	Core	was	pioneering	Graph	Inferencing.	
●  IBM	and	Bloomberg	were	exploring	serverless	ML	lambdas.	IBM	gave	a	talk	on	
the	ML	Serving	with	Knative	at	last	KubeCon	in	Seattle	
●  Google	had	built	a	common	Tensorflow	HTTP	API	for	models.	
●  Microsoft	Kubernetizing	their	Azure	ML	Stack
Putting the pieces together
●  Kubeflow	created	the	conditions	for	collaboration.	
●  A	promise	of	open	code	and	open	community.	
●  Shared	responsibilities	and	expertise	across	multiple	companies.	
●  Diverse	requirements	from	different	customer	segments
Introducing KFServing
KFServing
●  Founded by Google, Seldon, IBM, Bloomberg and Microsoft
●  Part of the Kubeflow project
●  Focus on 80% use cases - single model rollout and update
●  Kfserving 1.0 goals:
○  Serverless ML Inference
○  Canary rollouts
○  Model Explanations
○  Optional Pre/Post processing
KFServing Stack
•  Event	triggered	functions	on	Kubernetes
•  Scale	to	and	from	zero
•  Queue	based	autoscaling	for	GPUs	and	TPUs.	KNative	autoscaling	by	default	provides	inflight	requests	per	pod	
•  Traditional	CPU	autoscaling	if	desired.	Traditional	scaling	hard	for	disparate	devices	(GPU,	CPU,	TPU)	
Knative provides a set of building blocks that enable declarative, container-based, serverless workloads
on Kubernetes. Knative Serving provides primitives for serving platforms such as:
KNative
IBM is
2nd largest contributor
Connect: Traffic Control, Discovery,
Load Balancing, Resiliency
Observe: Metrics, Logging, Tracing
Secure: Encryption (TLS),
Authentication, and Authorization of
service-to-service communication
Control: Policy Enforcement
An open service mesh platform to connect, observe, secure, and control microservices.
Founded by Google, IBM and Lyft. IBM is the 2nd largest contributor	
Istio
Manages	the	hosting	aspects	of	your	models	
	
•  InferenceService	-	manages	the	lifecycle	of	
models	
	
•  Configuration	-	manages	history	of	model	
deployments.	Two	configurations	for	default	
and	canary.	
	
•  Revision	-	A	snapshot	of	your	model	version	
•  Config	and	image
	
•  Route	-	Endpoint	and	network	traffic	
management
Route Default
Configuration		
Revision	1
Revision	M	90
%
KFService	
Canary
Configuration		
Revision	1
Revision	N	10
%
KFServing: Default and
Canary Configurations
Model	Servers	
							-		TensorFlow	
							-		Nvidia	TRTIS	
							-		PyTorch	
							-		XGBoost	
							-		SKLearn	
							-		ONNX	
				
							Components:	
									-		Predictor,	Explainer,	
Transformer	
							Storages	
	-		AWS/S3	
	-		GCS	
	-		Azure	Blob	
	-		PVC	
Supported Frameworks, Components and
Storage Subsystems
The InferenceService architectureconsists of a static graph of components which coordinate requests for a
single model. Advanced features such as Ensembling, A/B testing, and Multi-Arm-Bandits should compose
InferenceServices together.
Inference Service Control Plane
KFServing Deployment View
-  Today’s	popular	model	servers,	such	as	TFServing,	ONNX,	Seldon,	
TRTIS,	all	communicate	using	similar	but	non-interoperable	HTTP/gRPC	
protocol	
-  KFServing	v1	data	plane	protocol	uses	TFServing	compatible	HTTP	API	
and	introduces	explain	verb	to	standardize	between	model	servers,	
punt	on	v2	for	gRPC	and	performance	optimization.	
KFServing Data Plane Unification
API Verb Path Payload
List Models GET /v1/models [model_names]
Readiness GET /v1/models/<model_name>
Predict POST /v1/models/<model_name>:predict Request: {instances:[]}
Response: {predictions:[]}
Explain POST /v1/models<model_name>:explain Request: {instances:[]}
Response: {predictions:[],
explanations:[]}
KFServing Data Plane v1 protocol
apiVersion: "serving.kubeflow.org/v1alpha1"
kind: "InferenceService"
metadata:
name: "sklearn-iris"
spec:
default:
sklearn:
modelUri: "gs://kfserving-samples/models/sklearn/iris"
apiVersion: "serving.kubeflow.org/v1alpha1"
kind: "InferenceService"
metadata:
name: "flowers-sample"
spec:
default:
tensorflow:
modelUri: "gs://kfserving-samples/models/tensorflow/flowers"
apiVersion: "serving.kubeflow.org/v1alpha1"
kind: "InferenceService"
metadata:
name: ”pytorch-iris"
spec:
default:
pytorch:
modelUri: "gs://kfserving-samples/models/pytorch/iris"
KFServing Examples
apiVersion: "serving.kubeflow.org/v1alpha1"
kind: "KFService"
metadata:
name: "my-model"
spec:
default:
# 90% of traffic is sent to this model
tensorflow:
modelUri: "gs://mybucket/mymodel-2"
canaryTrafficPercent: 10
canary:
# 10% of traffic is sent to this model
tensorflow:
modelUri: "gs://mybucket/mymodel-3"
apiVersion: "serving.kubeflow.org/v1alpha1"
kind: "KFService"
metadata:
name: "my-model"
spec:
default:
tensorflow:
modelUri: "gs://mybucket/mymodel-2"
# Defaults to zero, so can also be omitted or explicitly set to zero.
canaryTrafficPercent: 0
canary:
# Canary is created but no traffic is directly forwarded.
tensorflow:
modelUri: "gs://mybucket/mymodel-3"
Canary
Pinned
Canary/Pinned Examples
Demo
Model Serving is accomplished. Can the
predictions be trusted?
Prepared
and
Analyzed
Data
Trained
Model
Deployed
Model
Prepared
Data
Untrained
Model
Can the model explain
its predictions?
Are there concept drifts?
Is there an outlier? 
Is the model vulnerable
to adversarial attacks?
Production Machine Learning Serving
Production ML Architecture
InferenceService	
logger
Broker
Trigger
Outlier
Detection
Alerting
API	
Serving	
Model
Explainer
Adversarial
Detection
Concept
Drift
Machine Learning Explanations
Why Explain ML Models?
Regulation (GDPR):
[the data subject possesses the right to access] “meaningful information about
the logic involved, as well as the significance and the envisaged consequences
of such processing for the data subject."
Insight:
•  Is my model doing what I think it’s doing?
•  Investigate model behaviour, e.g. on outliers
ML Explanation Goals
•  Human interpretable
•  Not over-simplified
•  Trade-off between interpretability and fidelity
Local Black Box Explanations
Explain this:
Deny: p=0.95
Accept: p=0.05
Age:
23
Occupation:
Bar staff
Postcode:
IV3 5SN
Owns house:
No
Architecture
Model ŷx
e
Explainer ex
X
̂
Ŷ Expensive!
https://github.com/SeldonIO/alibi	
	
	
State	of	the	art	implementations:	
	
Seldon Alibi:Explain
Janis KlaiseGiovanni Vacanti Alexandru CocaArnaud Van Looveren
•  Anchors	
•  Counterfactuals		
•  Contrastive	explanations	
•  Trust	scores
Model
Persian cat:
p=0.90
Dishwasher:
p=0.003
Notebook:
p=0.002
Precision:
0.95
Explainer
X
̂
Ŷ
Anchors
KfServing Explanations
apiVersion: "serving.kubeflow.org/v1alpha2"
kind: "InferenceService"
metadata:
name: "income"
spec:
default:
predictor:
sklearn:
storageUri: "gs://seldon-models/sklearn/income/model"
explainer:
alibi:
type: AnchorTabular
storageUri: "gs://seldon-models/sklearn/income/explainer"	
apiVersion: "serving.kubeflow.org/v1alpha2"
kind: "InferenceService"
metadata:
name: "moviesentiment"
spec:
default:
predictor:
sklearn:
storageUri: "gs://seldon-models/sklearn/moviesentiment"
explainer:
alibi:
type: AnchorText
Explanation Demos
Income Prediction SKLearn Classifier and
Alibi:Explain AnchorTabular Explainer
https://github.com/kubeflow/kfserving/blob/master/docs/samples/
explanation/alibi/income/income_explanations.ipynb
Movie Review RoBERTa Classifier and
Alibi:Explain AnchorText Explainer
https://github.com/SeldonIO/seldon-models/blob/master/pytorch/
moviesentiment_roberta/inference/kfserving/
movie_review_explanations.ipynb
Income Model and Explainer
InferenceService	
API	
Serving	
Income Model
Income Explainer
46
AIX360 toolkit is an open-source library to help explain AI and
machine learning models and their predictions. This includes three
classes of algorithms: local post-hoc, global post-hoc, and directly
interpretable explainers for models that use image, text, and
structured/tabular data.
The AI Explainability360 Python package includes a comprehensive
set of explainers, both at global and local level.
Toolbox
Local post-hoc
Global post-hoc
Directly interpretable
http://aix360.mybluemix.net
AI	Explainability	360		
↳	(AIX360)	
https://github.com/IBM/AIX360	
	
Explanations: Resources
AIX360
Payload Logging
Payload Logging
Why:
●  Capture payloads for analysis and future retraining of the model
●  Perform offline processing of the requests and responses
KfServing Implementation (alpha):
●  Add to any InferenceService Endpoint: Predictor, Explainer, Transformer
●  Log Requests, Responses or Both from the Endpoint
●  Simple specify a URL to send the payloads
●  URL will receive CloudEvents
POST	/event	HTTP/1.0	
Host:	example.com	
Content-Type:	application/json	
ce-specversion:	1.0	
ce-type:	repo.newItem	
ce-source:	http://bigco.com/repo	
ce-id:	610b6dd4-c85d-417b-b58f-3771e532	
		
<payload>
Payload Logging
apiVersion: "serving.kubeflow.org/v1alpha2"
kind: "InferenceService"
metadata:
name: "sklearn-iris"
spec:
default:
predictor:
minReplicas: 1
logger:
url: http://message-dumper.default/
mode: all
sklearn:
storageUri: "gs://kfserving-samples/models/sklearn/iris"
resources:
requests:
cpu: 0.1
Payload Logging Architecture Examples
Model
InferenceService	
logger
Broker
Trigger
Outlier
Detector
Alerting
API	
Http kafka
Bridge Kafka Cluster
Serving
ML Inference Analysis
ML Inference Analysis
Don’t trust predictions on instances outside of training distribution!
•  Outlier Detection
•  Adversarial Detection
•  Concept Drift
Outlier Detection
Don’t trust predictions on instances outside of training distribution!
→ Outlier Detection
Detector types:
-  stateful online vs. pretrained offline
-  feature vs. instance level detectors
Data types:
-  tabular, images & time series
Outlier types:
-  global, contextual & collective outliers
Adversarial Detection
Don’t trust predictions on instances outside of training distribution!
→ Adversarial Detection
-  Outliers w.r.t. the model prediction
-  Detect small input changes with a big impact on predictions!
Concept Drift
Production data distribution != training distribution?
→ Concept Drift! Retrain!
Need to track the right distributions:
-  feature vs. instance level
-  continuous vs. discrete
-  online vs. offline training data
-  track streaming number of outliers
https://github.com/SeldonIO/alibi-detect	
	
	
State	of	the	art	implementations:	
	
	
Seldon Alibi:Detect
Janis KlaiseGiovanni Vacanti Alexandru CocaArnaud Van Looveren
•  Outlier	Detection	
•  Adversarial	Detection	
•  Concept	Drift	(roadmap)
Outlier Detection Demo
Outlier image and heatmap of VAE outlier score per RGB channel
KFServing	CIFAR10	Model	with	Alibi:Detect	VAE	Outlier	Detector
Outlier Detection on CIFAR10
CIFAR10 Model
InferenceService	
logger
Broker
Trigger
CIFAR10
Outlier Detector
Message
Dumper
API	
Serving
Adversarial Detection Demos
KFServing	MNIST	Model	with	
Alibi:Detect	VAE	Adversarial	Detector	
https://github.com/SeldonIO/alibi-detect/tree/master/integrations/
samples/kfserving/ad-mnist	
	
KFServing	Traffic	Signs	Model	with	
Alibi:Detect	VAE	Adversarial	Detector
Adversarial Detection on Traffic Signs
Traffic Signs Model
InferenceService	
logger
Broker
Trigger
Traffic Signs
Adversarial Detector
Message
Dumper
API	
Serving
•  
61
Adversarial	Robustness	360	
↳	(ART)	
ART is a library dedicated to adversarial machine learning. Its
purpose is to allow rapid crafting and analysis of attack, defense
and detection methods for machine learning models. Applicable
domains include finance, self driving vehicles etc.
The Adversarial Robustness Toolbox provides an implementation
for many state-of-the-art methods for attacking and defending
classifiers.
https://github.com/IBM/adversarial-robustness-toolbox
Toolbox: Attacks, defenses, and metrics
Evasion attacks
Defense methods
Detection methods
Robustness metrics
ART
https://art-demo.mybluemix.net/
Adversarial Attack, Detection and
Defense Mechanisms: Resources
Summary and Roadmap
Production ML Architecture
InferenceService	
logger
Broker
Trigger
Outlier
Detection
Alerting
API	
Serving	
Model
Explainer
Adversarial
Detection
Concept
Drift
Open Source Projects
●  ML Inference
○  KFServing
○  Seldon Core
https://github.com/kubeflow/kfserving
https://github.com/SeldonIO/seldon-core
●  Model Explanations
○  Seldon Alibi
○  IBM AI Explainability 360
https://github.com/seldonio/alibi
https://github.com/IBM/AIX360
●  Outlier and Adversarial Detection and Concept Drift
○  Seldon Alibi-detect
https://github.com/seldonio/alibi-detect
●  Adversarial Attack, Detection and Defense
○  IBM Adversarial Robustness 360
https://github.com/IBM/adversarial-robustness-toolbox
Related Tech Kubecon Talks

Más contenido relacionado

La actualidad más candente

Helm - the Better Way to Deploy on Kubernetes - Reinhard Nägele - Codemotion...
 Helm - the Better Way to Deploy on Kubernetes - Reinhard Nägele - Codemotion... Helm - the Better Way to Deploy on Kubernetes - Reinhard Nägele - Codemotion...
Helm - the Better Way to Deploy on Kubernetes - Reinhard Nägele - Codemotion...Codemotion
 
MySQL Monitoring using Prometheus & Grafana
MySQL Monitoring using Prometheus & GrafanaMySQL Monitoring using Prometheus & Grafana
MySQL Monitoring using Prometheus & GrafanaYoungHeon (Roy) Kim
 
Kubernetes Application Deployment with Helm - A beginner Guide!
Kubernetes Application Deployment with Helm - A beginner Guide!Kubernetes Application Deployment with Helm - A beginner Guide!
Kubernetes Application Deployment with Helm - A beginner Guide!Krishna-Kumar
 
Kubernetes: A Short Introduction (2019)
Kubernetes: A Short Introduction (2019)Kubernetes: A Short Introduction (2019)
Kubernetes: A Short Introduction (2019)Megan O'Keefe
 
GitOps with ArgoCD
GitOps with ArgoCDGitOps with ArgoCD
GitOps with ArgoCDCloudOps2005
 
Monitoring Kubernetes with Elasticsearch Services - Ted Jung, Consulting Arch...
Monitoring Kubernetes with Elasticsearch Services - Ted Jung, Consulting Arch...Monitoring Kubernetes with Elasticsearch Services - Ted Jung, Consulting Arch...
Monitoring Kubernetes with Elasticsearch Services - Ted Jung, Consulting Arch...Amazon Web Services Korea
 
How Apache Kafka® Works
How Apache Kafka® WorksHow Apache Kafka® Works
How Apache Kafka® Worksconfluent
 
Helm - Package manager in K8S
Helm - Package manager in K8SHelm - Package manager in K8S
Helm - Package manager in K8SPiotr Perzyna
 
Kubernetes - introduction
Kubernetes - introductionKubernetes - introduction
Kubernetes - introductionSparkbit
 
Centralized logging for (java) applications with the elastic stack made easy
Centralized logging for (java) applications with the elastic stack   made easyCentralized logging for (java) applications with the elastic stack   made easy
Centralized logging for (java) applications with the elastic stack made easyfelixbarny
 
Red Hat OpenShift Container Platform Overview
Red Hat OpenShift Container Platform OverviewRed Hat OpenShift Container Platform Overview
Red Hat OpenShift Container Platform OverviewJames Falkner
 
Apache Spark on K8S and HDFS Security with Ilan Flonenko
Apache Spark on K8S and HDFS Security with Ilan FlonenkoApache Spark on K8S and HDFS Security with Ilan Flonenko
Apache Spark on K8S and HDFS Security with Ilan FlonenkoDatabricks
 
Getting up to speed with MirrorMaker 2 | Mickael Maison, IBM and Ryanne Dolan...
Getting up to speed with MirrorMaker 2 | Mickael Maison, IBM and Ryanne Dolan...Getting up to speed with MirrorMaker 2 | Mickael Maison, IBM and Ryanne Dolan...
Getting up to speed with MirrorMaker 2 | Mickael Maison, IBM and Ryanne Dolan...HostedbyConfluent
 
Containers Anywhere with OpenShift by Red Hat
Containers Anywhere with OpenShift by Red HatContainers Anywhere with OpenShift by Red Hat
Containers Anywhere with OpenShift by Red HatAmazon Web Services
 

La actualidad más candente (20)

Helm - the Better Way to Deploy on Kubernetes - Reinhard Nägele - Codemotion...
 Helm - the Better Way to Deploy on Kubernetes - Reinhard Nägele - Codemotion... Helm - the Better Way to Deploy on Kubernetes - Reinhard Nägele - Codemotion...
Helm - the Better Way to Deploy on Kubernetes - Reinhard Nägele - Codemotion...
 
MySQL Monitoring using Prometheus & Grafana
MySQL Monitoring using Prometheus & GrafanaMySQL Monitoring using Prometheus & Grafana
MySQL Monitoring using Prometheus & Grafana
 
Kubernetes Application Deployment with Helm - A beginner Guide!
Kubernetes Application Deployment with Helm - A beginner Guide!Kubernetes Application Deployment with Helm - A beginner Guide!
Kubernetes Application Deployment with Helm - A beginner Guide!
 
Kubernetes: A Short Introduction (2019)
Kubernetes: A Short Introduction (2019)Kubernetes: A Short Introduction (2019)
Kubernetes: A Short Introduction (2019)
 
Argocd up and running
Argocd up and runningArgocd up and running
Argocd up and running
 
GitOps with ArgoCD
GitOps with ArgoCDGitOps with ArgoCD
GitOps with ArgoCD
 
Openshift argo cd_v1_2
Openshift argo cd_v1_2Openshift argo cd_v1_2
Openshift argo cd_v1_2
 
Monitoring Kubernetes with Elasticsearch Services - Ted Jung, Consulting Arch...
Monitoring Kubernetes with Elasticsearch Services - Ted Jung, Consulting Arch...Monitoring Kubernetes with Elasticsearch Services - Ted Jung, Consulting Arch...
Monitoring Kubernetes with Elasticsearch Services - Ted Jung, Consulting Arch...
 
How Apache Kafka® Works
How Apache Kafka® WorksHow Apache Kafka® Works
How Apache Kafka® Works
 
Helm - Package manager in K8S
Helm - Package manager in K8SHelm - Package manager in K8S
Helm - Package manager in K8S
 
Kubernetes - introduction
Kubernetes - introductionKubernetes - introduction
Kubernetes - introduction
 
Helm 3
Helm 3Helm 3
Helm 3
 
Grafana
GrafanaGrafana
Grafana
 
Centralized logging for (java) applications with the elastic stack made easy
Centralized logging for (java) applications with the elastic stack   made easyCentralized logging for (java) applications with the elastic stack   made easy
Centralized logging for (java) applications with the elastic stack made easy
 
Red Hat OpenShift Container Platform Overview
Red Hat OpenShift Container Platform OverviewRed Hat OpenShift Container Platform Overview
Red Hat OpenShift Container Platform Overview
 
Kubernetes CI/CD with Helm
Kubernetes CI/CD with HelmKubernetes CI/CD with Helm
Kubernetes CI/CD with Helm
 
Introduction to helm
Introduction to helmIntroduction to helm
Introduction to helm
 
Apache Spark on K8S and HDFS Security with Ilan Flonenko
Apache Spark on K8S and HDFS Security with Ilan FlonenkoApache Spark on K8S and HDFS Security with Ilan Flonenko
Apache Spark on K8S and HDFS Security with Ilan Flonenko
 
Getting up to speed with MirrorMaker 2 | Mickael Maison, IBM and Ryanne Dolan...
Getting up to speed with MirrorMaker 2 | Mickael Maison, IBM and Ryanne Dolan...Getting up to speed with MirrorMaker 2 | Mickael Maison, IBM and Ryanne Dolan...
Getting up to speed with MirrorMaker 2 | Mickael Maison, IBM and Ryanne Dolan...
 
Containers Anywhere with OpenShift by Red Hat
Containers Anywhere with OpenShift by Red HatContainers Anywhere with OpenShift by Red Hat
Containers Anywhere with OpenShift by Red Hat
 

Similar a Advanced Model Inferencing leveraging Kubeflow Serving, KNative and Istio

End to end Machine Learning using Kubeflow - Build, Train, Deploy and Manage
End to end Machine Learning using Kubeflow - Build, Train, Deploy and ManageEnd to end Machine Learning using Kubeflow - Build, Train, Deploy and Manage
End to end Machine Learning using Kubeflow - Build, Train, Deploy and ManageAnimesh Singh
 
Hybrid Cloud, Kubeflow and Tensorflow Extended [TFX]
Hybrid Cloud, Kubeflow and Tensorflow Extended [TFX]Hybrid Cloud, Kubeflow and Tensorflow Extended [TFX]
Hybrid Cloud, Kubeflow and Tensorflow Extended [TFX]Animesh Singh
 
Kostiantyn Bokhan, N-iX. CD4ML based on Azure and Kubeflow
Kostiantyn Bokhan, N-iX. CD4ML based on Azure and KubeflowKostiantyn Bokhan, N-iX. CD4ML based on Azure and Kubeflow
Kostiantyn Bokhan, N-iX. CD4ML based on Azure and KubeflowIT Arena
 
Why is dev ops for machine learning so different
Why is dev ops for machine learning so differentWhy is dev ops for machine learning so different
Why is dev ops for machine learning so differentRyan Dawson
 
Infrastructure Agnostic Machine Learning Workload Deployment
Infrastructure Agnostic Machine Learning Workload DeploymentInfrastructure Agnostic Machine Learning Workload Deployment
Infrastructure Agnostic Machine Learning Workload DeploymentDatabricks
 
Containerized architectures for deep learning
Containerized architectures for deep learningContainerized architectures for deep learning
Containerized architectures for deep learningAntje Barth
 
Machine Learning para devs com ML.NET
Machine Learning para devs com ML.NETMachine Learning para devs com ML.NET
Machine Learning para devs com ML.NETLetticia Nicoli
 
Scaling AI/ML with Containers and Kubernetes
Scaling AI/ML with Containers and Kubernetes Scaling AI/ML with Containers and Kubernetes
Scaling AI/ML with Containers and Kubernetes Tushar Katarki
 
Software Development Life Cycle
Software Development Life CycleSoftware Development Life Cycle
Software Development Life CycleAmber Carter
 
ODSC East 2020 Accelerate ML Lifecycle with Kubernetes and Containerized Da...
ODSC East 2020   Accelerate ML Lifecycle with Kubernetes and Containerized Da...ODSC East 2020   Accelerate ML Lifecycle with Kubernetes and Containerized Da...
ODSC East 2020 Accelerate ML Lifecycle with Kubernetes and Containerized Da...Abhinav Joshi
 
Running Apache Spark Jobs Using Kubernetes
Running Apache Spark Jobs Using KubernetesRunning Apache Spark Jobs Using Kubernetes
Running Apache Spark Jobs Using KubernetesDatabricks
 
Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...
Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...
Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...Akash Tandon
 
Pitfalls of machine learning in production
Pitfalls of machine learning in productionPitfalls of machine learning in production
Pitfalls of machine learning in productionAntoine Sauray
 
Ml ops deployment choices
Ml ops   deployment choicesMl ops   deployment choices
Ml ops deployment choicesAvinash Patil
 
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...DataWorks Summit
 
From Data Science to MLOps
From Data Science to MLOpsFrom Data Science to MLOps
From Data Science to MLOpsCarl W. Handlin
 
OS for AI: Elastic Microservices & the Next Gen of ML
OS for AI: Elastic Microservices & the Next Gen of MLOS for AI: Elastic Microservices & the Next Gen of ML
OS for AI: Elastic Microservices & the Next Gen of MLNordic APIs
 
Latest trends in information technology
Latest trends in information technologyLatest trends in information technology
Latest trends in information technologyEldos Kuriakose
 

Similar a Advanced Model Inferencing leveraging Kubeflow Serving, KNative and Istio (20)

End to end Machine Learning using Kubeflow - Build, Train, Deploy and Manage
End to end Machine Learning using Kubeflow - Build, Train, Deploy and ManageEnd to end Machine Learning using Kubeflow - Build, Train, Deploy and Manage
End to end Machine Learning using Kubeflow - Build, Train, Deploy and Manage
 
Hybrid Cloud, Kubeflow and Tensorflow Extended [TFX]
Hybrid Cloud, Kubeflow and Tensorflow Extended [TFX]Hybrid Cloud, Kubeflow and Tensorflow Extended [TFX]
Hybrid Cloud, Kubeflow and Tensorflow Extended [TFX]
 
Kostiantyn Bokhan, N-iX. CD4ML based on Azure and Kubeflow
Kostiantyn Bokhan, N-iX. CD4ML based on Azure and KubeflowKostiantyn Bokhan, N-iX. CD4ML based on Azure and Kubeflow
Kostiantyn Bokhan, N-iX. CD4ML based on Azure and Kubeflow
 
Why is dev ops for machine learning so different
Why is dev ops for machine learning so differentWhy is dev ops for machine learning so different
Why is dev ops for machine learning so different
 
Infrastructure Agnostic Machine Learning Workload Deployment
Infrastructure Agnostic Machine Learning Workload DeploymentInfrastructure Agnostic Machine Learning Workload Deployment
Infrastructure Agnostic Machine Learning Workload Deployment
 
Containerized architectures for deep learning
Containerized architectures for deep learningContainerized architectures for deep learning
Containerized architectures for deep learning
 
Machine Learning para devs com ML.NET
Machine Learning para devs com ML.NETMachine Learning para devs com ML.NET
Machine Learning para devs com ML.NET
 
Scaling AI/ML with Containers and Kubernetes
Scaling AI/ML with Containers and Kubernetes Scaling AI/ML with Containers and Kubernetes
Scaling AI/ML with Containers and Kubernetes
 
Software Development Life Cycle
Software Development Life CycleSoftware Development Life Cycle
Software Development Life Cycle
 
ODSC East 2020 Accelerate ML Lifecycle with Kubernetes and Containerized Da...
ODSC East 2020   Accelerate ML Lifecycle with Kubernetes and Containerized Da...ODSC East 2020   Accelerate ML Lifecycle with Kubernetes and Containerized Da...
ODSC East 2020 Accelerate ML Lifecycle with Kubernetes and Containerized Da...
 
Running Apache Spark Jobs Using Kubernetes
Running Apache Spark Jobs Using KubernetesRunning Apache Spark Jobs Using Kubernetes
Running Apache Spark Jobs Using Kubernetes
 
MLOps in action
MLOps in actionMLOps in action
MLOps in action
 
Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...
Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...
Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...
 
Pitfalls of machine learning in production
Pitfalls of machine learning in productionPitfalls of machine learning in production
Pitfalls of machine learning in production
 
Ml ops deployment choices
Ml ops   deployment choicesMl ops   deployment choices
Ml ops deployment choices
 
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
 
From Data Science to MLOps
From Data Science to MLOpsFrom Data Science to MLOps
From Data Science to MLOps
 
OS for AI: Elastic Microservices & the Next Gen of ML
OS for AI: Elastic Microservices & the Next Gen of MLOS for AI: Elastic Microservices & the Next Gen of ML
OS for AI: Elastic Microservices & the Next Gen of ML
 
Latest trends in information technology
Latest trends in information technologyLatest trends in information technology
Latest trends in information technology
 
Operator development made easy with helm
Operator development made easy with helmOperator development made easy with helm
Operator development made easy with helm
 

Más de Animesh Singh

Machine Learning Exchange (MLX)
Machine Learning Exchange (MLX)Machine Learning Exchange (MLX)
Machine Learning Exchange (MLX)Animesh Singh
 
KFServing Payload Logging for Trusted AI
KFServing Payload Logging for Trusted AIKFServing Payload Logging for Trusted AI
KFServing Payload Logging for Trusted AIAnimesh Singh
 
Kubeflow Distributed Training and HPO
Kubeflow Distributed Training and HPOKubeflow Distributed Training and HPO
Kubeflow Distributed Training and HPOAnimesh Singh
 
Defend against adversarial AI using Adversarial Robustness Toolbox
Defend against adversarial AI using Adversarial Robustness Toolbox Defend against adversarial AI using Adversarial Robustness Toolbox
Defend against adversarial AI using Adversarial Robustness Toolbox Animesh Singh
 
Trusted, Transparent and Fair AI using Open Source
Trusted, Transparent and Fair AI using Open SourceTrusted, Transparent and Fair AI using Open Source
Trusted, Transparent and Fair AI using Open SourceAnimesh Singh
 
AIF360 - Trusted and Fair AI
AIF360 - Trusted and Fair AIAIF360 - Trusted and Fair AI
AIF360 - Trusted and Fair AIAnimesh Singh
 
AI & Machine Learning Pipelines with Knative
AI & Machine Learning Pipelines with KnativeAI & Machine Learning Pipelines with Knative
AI & Machine Learning Pipelines with KnativeAnimesh Singh
 
Fabric for Deep Learning
Fabric for Deep LearningFabric for Deep Learning
Fabric for Deep LearningAnimesh Singh
 
Microservices, Kubernetes and Istio - A Great Fit!
Microservices, Kubernetes and Istio - A Great Fit!Microservices, Kubernetes and Istio - A Great Fit!
Microservices, Kubernetes and Istio - A Great Fit!Animesh Singh
 
How to build a Distributed Serverless Polyglot Microservices IoT Platform us...
How to build a Distributed Serverless Polyglot Microservices IoT Platform us...How to build a Distributed Serverless Polyglot Microservices IoT Platform us...
How to build a Distributed Serverless Polyglot Microservices IoT Platform us...Animesh Singh
 
How to build an event-driven, polyglot serverless microservices framework on ...
How to build an event-driven, polyglot serverless microservices framework on ...How to build an event-driven, polyglot serverless microservices framework on ...
How to build an event-driven, polyglot serverless microservices framework on ...Animesh Singh
 
As a Service: Cloud Foundry on OpenStack - Lessons Learnt
As a Service: Cloud Foundry on OpenStack - Lessons LearntAs a Service: Cloud Foundry on OpenStack - Lessons Learnt
As a Service: Cloud Foundry on OpenStack - Lessons LearntAnimesh Singh
 
Introducing Cloud Native, Event Driven, Serverless, Micrsoservices Framework ...
Introducing Cloud Native, Event Driven, Serverless, Micrsoservices Framework ...Introducing Cloud Native, Event Driven, Serverless, Micrsoservices Framework ...
Introducing Cloud Native, Event Driven, Serverless, Micrsoservices Framework ...Animesh Singh
 
Finding and-organizing Great Cloud Foundry User Groups
Finding and-organizing Great Cloud Foundry User GroupsFinding and-organizing Great Cloud Foundry User Groups
Finding and-organizing Great Cloud Foundry User GroupsAnimesh Singh
 
CAPS: What's best for deploying and managing OpenStack? Chef vs. Ansible vs. ...
CAPS: What's best for deploying and managing OpenStack? Chef vs. Ansible vs. ...CAPS: What's best for deploying and managing OpenStack? Chef vs. Ansible vs. ...
CAPS: What's best for deploying and managing OpenStack? Chef vs. Ansible vs. ...Animesh Singh
 
Building a PaaS Platform like Bluemix on OpenStack
Building a PaaS Platform like Bluemix on OpenStackBuilding a PaaS Platform like Bluemix on OpenStack
Building a PaaS Platform like Bluemix on OpenStackAnimesh Singh
 
Cloud foundry Docker Openstack - Leading Open Source Triumvirate
Cloud foundry Docker Openstack - Leading Open Source TriumvirateCloud foundry Docker Openstack - Leading Open Source Triumvirate
Cloud foundry Docker Openstack - Leading Open Source TriumvirateAnimesh Singh
 
Build Scalable Internet of Things Apps using Cloud Foundry, Bluemix & Cloudant
Build Scalable Internet of Things Apps using Cloud Foundry, Bluemix & CloudantBuild Scalable Internet of Things Apps using Cloud Foundry, Bluemix & Cloudant
Build Scalable Internet of Things Apps using Cloud Foundry, Bluemix & CloudantAnimesh Singh
 
Automated Lifecycle Management - CloudFoundry on OpenStack
Automated Lifecycle Management - CloudFoundry on OpenStackAutomated Lifecycle Management - CloudFoundry on OpenStack
Automated Lifecycle Management - CloudFoundry on OpenStackAnimesh Singh
 

Más de Animesh Singh (20)

Machine Learning Exchange (MLX)
Machine Learning Exchange (MLX)Machine Learning Exchange (MLX)
Machine Learning Exchange (MLX)
 
KFServing Payload Logging for Trusted AI
KFServing Payload Logging for Trusted AIKFServing Payload Logging for Trusted AI
KFServing Payload Logging for Trusted AI
 
KFServing and Feast
KFServing and FeastKFServing and Feast
KFServing and Feast
 
Kubeflow Distributed Training and HPO
Kubeflow Distributed Training and HPOKubeflow Distributed Training and HPO
Kubeflow Distributed Training and HPO
 
Defend against adversarial AI using Adversarial Robustness Toolbox
Defend against adversarial AI using Adversarial Robustness Toolbox Defend against adversarial AI using Adversarial Robustness Toolbox
Defend against adversarial AI using Adversarial Robustness Toolbox
 
Trusted, Transparent and Fair AI using Open Source
Trusted, Transparent and Fair AI using Open SourceTrusted, Transparent and Fair AI using Open Source
Trusted, Transparent and Fair AI using Open Source
 
AIF360 - Trusted and Fair AI
AIF360 - Trusted and Fair AIAIF360 - Trusted and Fair AI
AIF360 - Trusted and Fair AI
 
AI & Machine Learning Pipelines with Knative
AI & Machine Learning Pipelines with KnativeAI & Machine Learning Pipelines with Knative
AI & Machine Learning Pipelines with Knative
 
Fabric for Deep Learning
Fabric for Deep LearningFabric for Deep Learning
Fabric for Deep Learning
 
Microservices, Kubernetes and Istio - A Great Fit!
Microservices, Kubernetes and Istio - A Great Fit!Microservices, Kubernetes and Istio - A Great Fit!
Microservices, Kubernetes and Istio - A Great Fit!
 
How to build a Distributed Serverless Polyglot Microservices IoT Platform us...
How to build a Distributed Serverless Polyglot Microservices IoT Platform us...How to build a Distributed Serverless Polyglot Microservices IoT Platform us...
How to build a Distributed Serverless Polyglot Microservices IoT Platform us...
 
How to build an event-driven, polyglot serverless microservices framework on ...
How to build an event-driven, polyglot serverless microservices framework on ...How to build an event-driven, polyglot serverless microservices framework on ...
How to build an event-driven, polyglot serverless microservices framework on ...
 
As a Service: Cloud Foundry on OpenStack - Lessons Learnt
As a Service: Cloud Foundry on OpenStack - Lessons LearntAs a Service: Cloud Foundry on OpenStack - Lessons Learnt
As a Service: Cloud Foundry on OpenStack - Lessons Learnt
 
Introducing Cloud Native, Event Driven, Serverless, Micrsoservices Framework ...
Introducing Cloud Native, Event Driven, Serverless, Micrsoservices Framework ...Introducing Cloud Native, Event Driven, Serverless, Micrsoservices Framework ...
Introducing Cloud Native, Event Driven, Serverless, Micrsoservices Framework ...
 
Finding and-organizing Great Cloud Foundry User Groups
Finding and-organizing Great Cloud Foundry User GroupsFinding and-organizing Great Cloud Foundry User Groups
Finding and-organizing Great Cloud Foundry User Groups
 
CAPS: What's best for deploying and managing OpenStack? Chef vs. Ansible vs. ...
CAPS: What's best for deploying and managing OpenStack? Chef vs. Ansible vs. ...CAPS: What's best for deploying and managing OpenStack? Chef vs. Ansible vs. ...
CAPS: What's best for deploying and managing OpenStack? Chef vs. Ansible vs. ...
 
Building a PaaS Platform like Bluemix on OpenStack
Building a PaaS Platform like Bluemix on OpenStackBuilding a PaaS Platform like Bluemix on OpenStack
Building a PaaS Platform like Bluemix on OpenStack
 
Cloud foundry Docker Openstack - Leading Open Source Triumvirate
Cloud foundry Docker Openstack - Leading Open Source TriumvirateCloud foundry Docker Openstack - Leading Open Source Triumvirate
Cloud foundry Docker Openstack - Leading Open Source Triumvirate
 
Build Scalable Internet of Things Apps using Cloud Foundry, Bluemix & Cloudant
Build Scalable Internet of Things Apps using Cloud Foundry, Bluemix & CloudantBuild Scalable Internet of Things Apps using Cloud Foundry, Bluemix & Cloudant
Build Scalable Internet of Things Apps using Cloud Foundry, Bluemix & Cloudant
 
Automated Lifecycle Management - CloudFoundry on OpenStack
Automated Lifecycle Management - CloudFoundry on OpenStackAutomated Lifecycle Management - CloudFoundry on OpenStack
Automated Lifecycle Management - CloudFoundry on OpenStack
 

Último

TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxTINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxDwiAyuSitiHartinah
 
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Guido X Jansen
 
CI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionCI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionajayrajaganeshkayala
 
Master's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationMaster's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationGiorgio Carbone
 
Mapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxMapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxVenkatasubramani13
 
How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?sonikadigital1
 
AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)Data & Analytics Magazin
 
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityStrategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityAggregage
 
Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Vladislav Solodkiy
 
Virtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product IntroductionVirtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product Introductionsanjaymuralee1
 
MEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .pptMEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .pptaigil2
 
Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...PrithaVashisht1
 
The Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerThe Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerPavel Šabatka
 
5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best PracticesDataArchiva
 
YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.JasonViviers2
 
ChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics InfrastructureChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics Infrastructuresonikadigital1
 
SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024Becky Burwell
 

Último (17)

TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxTINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
 
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
 
CI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionCI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual intervention
 
Master's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationMaster's Thesis - Data Science - Presentation
Master's Thesis - Data Science - Presentation
 
Mapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxMapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptx
 
How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?
 
AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)
 
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityStrategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
 
Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023
 
Virtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product IntroductionVirtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product Introduction
 
MEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .pptMEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .ppt
 
Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...
 
The Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerThe Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayer
 
5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices
 
YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.
 
ChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics InfrastructureChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics Infrastructure
 
SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024
 

Advanced Model Inferencing leveraging Kubeflow Serving, KNative and Istio