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MLOps for Beginners
Rating: 4.3 out of 5(370 ratings)
4,609 students

MLOps for Beginners

Understand how to provide an end-to-end ML development process to design, build and manage the AI model lifecycle
Created byKatonic MLOps Platform
Last updated 9/2022
English
English [Auto],

What you'll learn

  • Current State of AI
  • How MLOps alleviates challenges faced in AI implementation
  • AI Model Lifecycle
  • Introduction to ML Platforms

Course content

1 section • 11 lectures • 33m total length

  • Introduction1:43

    Explore how organizations struggle with ml operations and learn to industrialize through a machine learning operations platform, demystifying jargon and enabling banks and enterprises to scale.

  • Current State of AI4:26

    Explore how software powers great companies and why AI is becoming mainstream across industries. Identify leadership and ground level capability gaps and production challenges AI adoption faces, with ops solutions.

  • Challenges in AI implementation6:16

    Demystify ml production by showing that models are only 5% of the cost; implement ml operations with data pipelines, continuous training, and continuous monitoring, plus cross-disciplinary collaboration.

  • MLOps - A Solution0:57

    Unite data scientists, data engineers, and IT operations with the right tools and processes to enable continuous innovation, experiment, fail fast, and put successful models into production.

  • Intro to ML Platforms2:29

    Explore a flexible, Kubernetes-based ML platform that supports on-prem and cloud deployments, enabling end-to-end experimentation through production with freedom to choose environments, algorithms, and Python integrations.

  • Benefits for Organizations1:27

    See how organizations use MLOps to automate model workloads with few data scientists. Explore Kubernetes-based platforms with scalable, pay-as-you-go infrastructure for training GPUs and production models.

  • Demo Use Case1:12

    Explore a demo use case where a bank uses machine learning to predict which customers will churn and why, bridging dashboards to ml ops level two.

  • What is Feature Engineering?2:22

    Discover feature engineering in ML: transform organizational data not designed for ML into signals or features, train models with probabilistic outputs, and explore real estate examples within MLOps for Beginners.

  • AI Model Lifecycle7:31

    Define a clear business objective, convert data to features, run experiments with tracking, and deploy scalable pipelines with model versioning, APIs, dashboards, and drift monitoring.

  • Advanced AI Model Lifecycle4:33

    Organizations will run hundreds of models and thousands of pipelines, and feature stores enable feature reuse. The lecture explains decoupling feature engineering from training and inference to boost consistency.

  • Resources1:00

    Discover practical MLOps guidance through a practitioner’s guide, best practices, and pre-built accelerators on the Catholic platform, plus level two and three tutorials and a free Catholic dot II account.

Requirements

  • No programming experience needed. You will learn everything you need to know.

Description

AI is no longer exclusively for digitally native companies like Amazon, Netflix, or Uber. Unsurprisingly, Gartner predicts that more than 75% of organizations will shift from piloting AI technologies to operationalizing them by the end of 2024 — which is where the real challenges begin. Unfortunately, scaling AI in this sense isn’t easy. There is a chasm between ML and MLOps that can be tricky to scale. Getting one or two AI models into production is different from running an entire enterprise or product on AI. And as AI is scaled, problems can (and often do) scale, too.


Organizations that are serious about AI have to adopt a new discipline, “MLOps” or Machine Learning Operations. MLOps is the bridge. It is an engineering culture and practice that aims to unify ML system development and operations to facilitate data processing, machine learning pipeline, model training, experimentation, evaluation, registry, deployment, monitoring, serving, and scaling. Essentially, MLOps refers to a set of practices that helps in deploying and maintaining machine learning models in production efficiently and reliably. It is a collaborative team function often comprising of data scientists and DevOps engineers.


In this course, you will learn:

  • The building blocks of MLOps

  • The best practices and tools that facilitate rapid, safe, and efficient development and operationalization of AI

Who this course is for:

  • Aspiring MLOps Professionals and Enthusiasts
  • Individuals interested in data and AI industry

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