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DevOps

Fundamentals of MLOps

Raghunandana Sanur
Staff Data Engineer & MLOps Engineer at Talabat
DevOps Pre-Requisite Course
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What you’ll learn

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Description

Step into the dynamic world of Machine Learning Operations (MLOps) with our tailored course specifically designed for DevOps engineers eager to expand their skill sets and embrace the intersection of machine learning and operational excellence. This course provides a robust introduction to MLOps, covering crucial concepts, methodologies, and tools that merge the spheres of data science and DevOps.

1. Introduction to MLOps:

  •  Understand the core principles of MLOps and its necessity in the modern tech landscape.
  •  Explore the evolving role of an MLOps engineer in a data-driven world.
  •  Differentiate MLOps from DevOps by examining the collaboration of DataOps, ModelOps, and DevOps.
  •  Navigate the MLOps lifecycle, focusing on CI/CD, continuous training, and monitoring strategies.
  •  Analyze high-level MLOps architecture and its components.

2. Data Collection and Preparation:

  •  Master the intricacies of data collection, ingestion, and the concept of data lakes.
  •  Gain hands-on experience in data cleaning and transformation using Pandas, Polars, and large scale tools like Apache Spark and Dask.
  •  Dive into the world of streaming data with Apache Kafka and Apache Flink.
  •  Discover the role of feature stores and learn to orchestrate data pipelines using Airflow and Perfect.

3. Model Development and Training:

  •  Acquire skills in model development and training, including hyperparameter tuning techniques.
  •  Understand computing landscapes, emphasizing the use of CPUs and GPUs for efficient model training.
  •  Get introduced to MLflow for experiment management and model lifecycle through detailed demos and labs.

4. Model Deployment and Serving:

  •  Investigate model deployment and serving with tools like BentoML, addressing model drift and version upgrades.
  •  Explore the use of monitoring tools such as Prometheus, Grafana, and Evidently to ensure continuous model performance.

5. Automating Insurance Claim Reviews with MLflow and BentoML:

  •  Apply your MLOps knowledge to a practical project by deploying an application for automating insurance claim reviews.
  •  Learn to set up MLflow servers and integrate BentoML for seamless model serving within a Python Flask application.

6. Data Security and Governance:

  •  Explore critical aspects of data privacy, security, and access management.
  •  Navigate compliance landscapes, focusing on GDPR, HIPAA, and PCI standards and their implications.

Learning Outcomes:

By the end of this course, DevOps engineers will have a solid foundation in MLOps, enriched with the skills needed to design, deploy, monitor, and manage machine learning models effectively. This course empowers participants to merge their DevOps expertise with machine learning practices, staying at the forefront of technological innovation.

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About the instructor

Raghunandana Krishnamurthy is a seasoned Staff Data Engineer and MLOps expert, skilled in navigating both GCP and AWS cloud platforms to accelerate model development and deployment. His experience spans modernizing legacy data systems, architecting hybrid infrastructures, and ensuring data quality for diverse applications. He used to hold  Associate AWS Solution Architect certification, Cloudera Hadoop Admin certification, Airflow certification, and Databricks Lakehouse certification 

A technical leader and passionate trainer, Raghunandana excels at building and maintaining big data platforms, championing DevOps best practices, and fostering team alignment. With hands-on expertise in tools like SageMaker, VertexAI, Prometheus, Grafana, and extensive DevOps tools focusing on Data Engineering and MLOps.

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Introduction to MLOp

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Lesson Content

Module Content

Course Introduction 03:31
GitHub Repo
Getting Started with Machine Learning Team 05:23
Introducing MLOps Engineer 04:59
DevOps and MLOps - A Comparison 07:00
MLOps LifeCycle 03:58
Continuous Integration (CI), Continuous Deployment (CD), Continuous Training (CT), Continuous Monitoring (CM) 08:09
Finding and Exploring Right Tools from DevOps for MLOps 07:01
MLOps Architecture 05:13
Quiz - Introduction to MLOps
How to Reach Out to KodeKloud and Engage with the Community

Data Collection and Preparation

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Lesson Content

Module Content

Data Collection and Preparation 04:04
Data Ingestion - ETL 03:30
Idea of Data Lake 04:39
Data Cleaning and Data Transformation 05:33
Data Collection and Preparation 07:31
Demo: Small to Medium Datasets Data Transformation : Pandas, Polars 19:23
Lab: Small to Medium Datasets data transformation : Pandas, Polars
Quiz - Data Collection and Preparation - Set 1
Large Datasets: Apache Spark (PySpark), Dask 03:53
Streaming Datasets: Apache Kafka Apache Flink 06:26
Demo: Stream Data using Apache Kafka 12:16
Lab: Streamdata using Apache Kafka
What is Feature Store? 11:13
Data Pipeline Orchestration - Airflow, Perfect 08:38
Demo: Data Pipeline Orchestration 13:17
Lab: Data Pipeline Orchestration
Quiz - Data Collection and Preparation - Set 2

Model Development and Training

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Module Content

Model Development 05:02
Model Training and Hyperparameter tuning 03:37
World of CPUs and GPUs 04:32
Introduction to MLflow 04:31
Demo: Setting up MLflow 02:58
Demo: Running and experiment and storing the result on MLflow 05:20
Demo: MLflow Model Artifact and Versioning 03:21
Lab: Hands on with MLflow
Quiz - Model Development and Training

Model Deployment and Serving

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Module Content

Model Serving 10:03
Model Drift and Online/Offline Serving 11:29
Model Deployment and Serving 04:39
Demo: Model Serving using BentoML 10:23
Demo: Upgrading Model Versions with BentoML Serving 04:04
Lab: Model Serving using BentoML
Monitoring Tools (Prometheus,Grafana,Evidently) 05:02
Quiz - Model Deployment and Serving

Automating Insurance Claim Reviews with MLflow and BentoML

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Module Content

Deploy App for Insurance Agents to Upload all Insurance Claims 07:11
Demo: Generate Dummy Data for the Project 02:27
Demo: Setup MLflow server and run the ML Experiment 04:06
Demo: Register the Model and Setup BentoML for Serving ML Models 03:16
Demo: Upgrade Python Flask App to Connect to BentoML for Online Serving 05:53
Lab: Deploy App for Insurance Agents to Upload all Insurance Claims

Data Security and Governance

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Data Privacy and Data Security 10:09
Data Access Management 07:27
Data Retention 07:06
Need of Compliance and GDPR 05:23
HIPAA Compliance 03:28
PCI Compliance 04:12
Compliance Consequences and Penalties 03:22
Compliance Summary 01:43
Quiz - Data Security and Governance

Sneak Peek into AWS SageMaker

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Overview of SageMaker 06:33
Core Components of SageMaker 05:59
MLOps with SageMaker 02:12
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