Dive into the dynamic world of MLOps and master the art of bringing Machine Learning models to life with this MLOps training course

MLOps training:
basic course

Dive into the dynamic world of MLOps and master the art of bringing Machine Learning models to life with this MLOps training course

MLOps training:
basic course

At a glance

3 days

Individually schedulable

Completely
remote

Theory &
Practice

English

Learn the key principles of MLOps and its integration with DevOps and Machine Learning. Explore advanced tools, CI/CD methods, cloud platforms, data versioning, and ML pipeline management.

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Agenda (example)

We are happy to create a customized agenda with you so that DBT will be a breeze in the future.

Day 1

MLOps Foundations & Data Pipelines

  1. Understanding MLOps principles & lifecycle
  2. The role of DevOps in ML workflows
  3. Overview of ML frameworks & environments
  4. Key challenges in scaling and operationalizing ML models
  1. Types & characteristics of data pipelines
  2. Data quality & governance: Lineage, contracts, metadata management
  3. Data versioning strategies for reproducibility
  4. Hands-on: Implementing data versioning with DVC
  1. Use cases & benefits of feature stores
  2. Comparative deep dive: Feast & Hopsworks
  3. Hands-on: Using a feature store for ML workflows

Day 2

Model Deployment & Orchestration

  1. Frameworks for ML pipelines: Overview & best practices
  2. Deep dive: Apache Airflow for ML workflows
  3. Deep dive: Kubeflow for end-to-end ML lifecycle management
  4. Hands-on: Building a production-ready ML pipeline
  1. Saving & loading ML models for production
  2. Model versioning & management strategies
  3. Comparing model serving frameworks: TFServe, KServe & more
  4. Deployment strategies: Batch, real-time & event-driven ML pipelines
  5. Hands-on: Deploying ML models in a production environment
  1. Comparison of ML hosting platforms: Features, scalability, ease of use
  2. Hands-on: Deploying models using MLFlow, Kubeflow & Metaflow

Day 3

Monitoring, Compliance & Best Practices

  1. Continuous model & data monitoring for production ML systems
  2. The pillars of drift detection, fairness & performance tracking
  3. Implementing data validation using Great Expectations & DeeQu
  4. Hands-on: Monitoring ML models with Evidently & NannyML
  1. Leveraging Grafana for monitoring insights
  2. Best practices for interpreting ML monitoring results
  3. Automating alerting & performance tracking
  1. Discussion on your ecosystem & best practices
  2. Addressing your challenges & open questions
  3. Recommendations for next steps in scaling MLOps

With this MLOps training, you will experience a balanced mix of theory, live demonstrations and practical exercises.

Learn the essential principles and concepts of MLOps, including integration into the DevOps and Machine Learning domains.

get to know advanced MLOps tools and techniques, as well as methods for continuous integration and delivery (CI/CD).

Dive into the use of specialized cloud platforms, data versioning and feature stores, the creation and management of ML pipelines.

In this MLOps Course …

… you will learn the essential principles and concepts of MLOps, including integration into the DevOps and Machine Learning domains, the use of specialized cloud platforms, data versioning and feature stores, the creation and management of ML pipelines, as well as the deployment and monitoring of models. You will also get to know advanced MLOps tools and techniques, as well as methods for continuous integration and delivery (CI/CD).

Practical Applications That We Will Cover in the MLOps training:

  • 1
    Implementing and managing ML pipelines with Kubeflow and Apache Airflow.
  • 2
    Using TensorFlow, DVC, Feast, and dbt in practical exercises to create and deploy ML models.
  • 3
    Applying monitoring and metrics tools like Hydrosphere, Evidently.ai, and Grafana to track data and concept drift.
  • 4
    Hands-on activities for model deployment with FastAPI, Seldon Core, and TensorFlow Serving.
  • 5
    Executing CI/CD processes with tools such as Jenkins, Prefect, Airflow, Rundeck, Kedro, TFX, and Kubeflow.

After the MLOps training course, You Will Be Able To:

  • 1
    Understand and apply the importance of MLOps.
  • 2
    Effectively use machine learning concepts and environments.
  • 3
    Implement data versioning and feature stores.
  • 4
    Create and orchestrate ML pipelines.
  • 5
    Use machine learning frameworks such as Scikit-Learn, Keras, and TensorFlow.
  • 6
    Deploy and monitor models using advanced techniques and tools.
  • 7
    Integrate CI/CD tools and platforms into ML workflows.

This MLOps course is perfect for you if you…

  • You work or wish to work in the field of machine learning, data engineering, or DevOps.
  • You want to expand your knowledge in the deployment and management of ML models.
  • You want to gain practical experience with advanced MLOps tools and techniques.
  • You want to develop a deep understanding of the integration of machine learning into DevOps processes.

The MLOps training is NOT suitable for you if you …

  • You are not interested in the integration of DevOps and machine learning.
  • You do not have prior knowledge in the fields of machine learning, data engineering, DevOps, or general programming.
  • You are not willing to engage in practical exercises and projects.
  • You do not wish to acquire skills in using cloud platforms and advanced MLOps tools.

Hear from our satisfied training attendees

A1 Telekom Austria AG

„UTA coached my team along the development process of the migration plan of our on-premises data lake to the public cloud.

The outstanding level of expertise, both on a technical and organizational level, ensured a well-structured and realistic migration plan including timeline, milestones, and efforts.

The enablement of my team was at the center of a very smooth collaboration. Through UTA, we achieved our goal faster and reduced risks of the migration project significantly.

I highly recommend UTA’s services!“

Reinhard Burgmann
Head of Data Ecosystem

Vattenfall

„I recently attended Vattenfall IT’s online Kafka training day hosted by Ultra Tendency, and it was an enriching experience.

The trainer, Ahmed, did a fantastic job explaining the theory behind Kafka, and the emphasis on practical application was great. The hands-on programming exercises were particularly helpful, and I’ve never experienced training with so many interactive examples!

Overall, I highly recommend this training to anyone who wants to improve their Kafka knowledge interactively and gain valuable skills.”

Bernard Benning
BA Heat

VP Bank

„The MLOps training exceeded our expectations!

It offered a perfect blend of an overview, hands-on coding examples, and real-world use cases. The trainer answered all questions competently and adapted the content to fit our company’s infrastructure.

This training not only provided us with knowledge but also practical skills that we can apply immediately.“

Eisele Peer
Lead Architect & Head of IT Integration & Development

Hutchison Drei Austria GmbH

„The training Introduction to the Cloud with AWS and Azure impressed us! We particularly appreciated the excellent overview of the topics, the hands-on exercises, and the extensive practical activities that made the learned concepts directly applicable.

The content was well-structured, and the combination of theory and practical applications was ideal for our needs. The opportunity to clarify specific questions in the Q&A sessions was also extremely valuable. Overall, the training deepened our understanding of cloud computing and provided us with insights into the differences and strengths of AWS and Azure.

We now feel better prepared to make strategic decisions for our cloud strategy. Thank you for this excellent training!“

Eisele Peer
Lead Architect & Head of IT Integration & Development

Get to know your Training professionals

Marvin Taschenberger

Professional Software Architect, Ultra Tendency

Hudhaifa Ahmed

Senior Lead Big Data Developer & Berlin Territory Manager, Ultra Tendency

Matthias Baumann

Chief Technology Officer & Principal Big Data Solutions Architect Lead, Ultra Tendency

Required hardware & infrastructure for your MLOps Training

  • You will need a PC or Mac with a web browser and MS Teams.
  • During the training, we will provide you with a virtual machine with the required local dependencies, services and root access.
  • This VM has a running Kubernetes cluster on which you can test and execute the training instructions.
  • You can access the machine via a browser or SSH if you wish and the network restrictions allow it.