Live online course
ML Inference, Serving, and Monitoring
If you are already familiar with using general tools such as Docker and Kubernetes to organize and deploy your machine learning applications, then Module 3 of our MLOps learning track is specifically focused on serving machine learning models.
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    Duration: 6 live sessions start coming soon
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    Price: early birds €650 (full price €800)
    * Get reimbursed by your employer
ML engineers, data scientists, data engineers, DevOps, and software engineers
By enrolling, you will:
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    learn how to use Seldon-Core for ML serving in various use cases
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    configure logging, monitoring, and metrics for your ML service
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    get experience with aNN libraries to deploy vector-based ML apps
6 live session
Immediate expert input
Real-world skills

You already know how to apply general tools like Docker and Kubernetes to structure and deploy your ML apps. Module 3 of our MLOps program is dedicated to serving ML models.


We’ll explore the capabilities of dedicated ML serving frameworks, learn how to deploy models as real-time services, build complex inference graphs, run A/B tests, and collect and process metrics.

Course syllabus
Session 1

Serving ML in production

ML inference and optimizations
ML serving vs regular APIs - what is the difference?
ML serving via Docker and Kubernetes
ML serving frameworks
Session 2


Architecture and use-cases
Standalone and k8s deployment
Seldon Custom Resource Definition (CRD)
Custom server and prebuilt servers: Sklearn, XGboost, MLFlow
Session 3

Advanced Seldon-Core

Seldon-Core Python components: model, transformer, aggregator, router
Seldon-core inference graphs
How to run AB-tests with Seldon-Core
Seldon-core feedback loops
Seldon-Deploy features
Session 4

Monitoring for ML

Prometheus for k8s services monitoring
Jaeger for tracing and APM
OpenTracing and OpenTelemetry
Grafana for visualizations
Online and offline metrics for ML services
How to monitor Seldon-Core services
Session 5

Efficient real-time aNN (Approximate Nearest Neighbours) search

aNN vs kNN
aNN algorithms and libraries
How to evaluate and optimize aNN: benchmarks and insights
How to deploy and manage aNN: platforms and custom solutions
Session 6

Demo day

Present team projects
Retrospective and follow-up questions
Lead ML Engineer

Meet the top expert

Dmytro Voitekh is a seasoned ML engineer with 8 years of experience in machine learning, MLOps, and full-stack engineering. He has a strong background in helping both early-stage startups and established tech companies to leverage ML features. His domains of expertise include machine learning and MLOps, and he has worked as an ML consultant, ML architect/lead, and CTO of a startup.


Dmytro has worked with a number of companies, including Proxet and GIPHY, and his extensive experience in the field makes him a valuable asset for any company that wants to incorporate machine learning into their business processes. He has numerous talks, workshops, and practical courses dedicated to ML, which showcases his expertise in the field.

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