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  • Project Based Learning
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MLOps Fundamentals Training Course Overview

Join Multisoft Systems for MLOps Fundamentals Training to explore the critical aspects of machine learning operations. Gain expertise in deploying, monitoring, and managing ML models efficiently, boosting your skills and advancing your career in AI technology.

MLOps Fundamentals Training provided by Multisoft Systems offers a comprehensive introduction to the practices and tools necessary for the effective deployment, management, and continuous improvement of machine learning (ML) models. This course is designed to bridge the gap between data science and operations, enabling participants to implement machine learning models efficiently and sustainably within an organizational context. Throughout the training, participants will learn key MLOps principles, including version control for data and models, automation of the machine learning lifecycle, and rigorous monitoring of model performance in production environments. The curriculum covers the deployment of machine learning models using cloud platforms, ensuring models are scalable, reliable, and secure. Additionally, the course delves into the integration of ML models with existing IT infrastructure, emphasizing the importance of collaboration between data scientists and operations teams.

By mastering MLOps, professionals can ensure that ML projects are not only successful in experimental phases but also deliver sustained value in real-world applications. This training is vital for those looking to enhance their ability to manage sophisticated ML workflows and for organizations aiming to leverage AI technologies for strategic advantage. Participants will leave with a solid understanding of how to apply MLOps techniques to accelerate and optimize their machine learning initiatives.

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MLOps Fundamentals Training Course curriculum

Curriculum Designed by Experts

Join Multisoft Systems for MLOps Fundamentals Training to explore the critical aspects of machine learning operations. Gain expertise in deploying, monitoring, and managing ML models efficiently, boosting your skills and advancing your career in AI technology.

MLOps Fundamentals Training provided by Multisoft Systems offers a comprehensive introduction to the practices and tools necessary for the effective deployment, management, and continuous improvement of machine learning (ML) models. This course is designed to bridge the gap between data science and operations, enabling participants to implement machine learning models efficiently and sustainably within an organizational context. Throughout the training, participants will learn key MLOps principles, including version control for data and models, automation of the machine learning lifecycle, and rigorous monitoring of model performance in production environments. The curriculum covers the deployment of machine learning models using cloud platforms, ensuring models are scalable, reliable, and secure. Additionally, the course delves into the integration of ML models with existing IT infrastructure, emphasizing the importance of collaboration between data scientists and operations teams.

By mastering MLOps, professionals can ensure that ML projects are not only successful in experimental phases but also deliver sustained value in real-world applications. This training is vital for those looking to enhance their ability to manage sophisticated ML workflows and for organizations aiming to leverage AI technologies for strategic advantage. Participants will leave with a solid understanding of how to apply MLOps techniques to accelerate and optimize their machine learning initiatives.

  • Understand the core principles of MLOps and its importance in the ML lifecycle.
  • Learn to automate and streamline the deployment, monitoring, and maintenance of machine learning models.
  • Acquire skills in version control for both data and ML models to ensure reproducibility and traceability.
  • Gain proficiency in using cloud platforms for scalable and secure ML model deployment.
  • Explore strategies for integrating machine learning models with existing IT infrastructure.
  • Develop collaborative skills for working effectively between data science and operations teams.

Course Prerequisite

  • Basic understanding of machine learning concepts and algorithms.
  • Familiarity with programming languages, preferably Python.
  • Experience with data handling and analysis techniques.

Course Target Audience

  • Data Scientists
  • Machine Learning Engineers
  • DevOps Engineers
  • IT Operations Professionals
  • Software Developers interested in AI
  • Project Managers overseeing AI projects
  • Business Analysts involved in data-driven decision making

Course Content

  • Data Scientists’ Pain Points
  • Machine Learning Lifecycle
  • MLOps Architecture and TensorFlow Extended Components

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  • Introduction
  • Introduction to Containers
  • Containers and Container Images
  • Introduction to Kubernetes
  • Introduction to Google Kubernetes Engine
  • Compute Options Detail
  • Kubernetes Concepts
  • The Kubernetes Control Plane
  • Google Kubernetes Engine Concepts
  • Deployments
  • Ways to Create Deployments
  • Services and Scaling
  • Updating Deployments
  • Rolling Updates
  • Blue-Green Deployments
  • Canary Deployments
  • Managing Deployments
  • Jobs and CronJobs
  • Parallel Jobs
  • CronJobs

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  • Containers and Container Images
  • Introduction to Kubernetes
  • Introduction to Google Kubernetes Engine
  • Containers and Kubernetes in Google Cloud
  • Kubernetes Concepts
  • The Kubernetes Control Plane
  • Google Kubernetes Engine Concepts
  • Deployments
  • Updating Deployments
  • Jobs

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  • Concepts
  • When to use
  • Ecosystem
  • Getting Started with Google Cloud

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  • System and concepts overview
  • Create a reproducible dataset
  • Implement a tunable model
  • Build and push a training container
  • Train and tune the model
  • Serve and query the model

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  • System and concept overview
  • Describing a Kubeflow Pipeline with KF DSL
  • Pre-built components5m
  • Lightweight Python Components
  • Custom components
  • Compile, upload and Run

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  • Concept Overview
  • Cloud Build Builders
  • Cloud Build Configuration
  • Cloud Build Triggers

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MLOps Fundamentals Training (MCQ) Assessment

This assessment tests understanding of course content through MCQ and short answers, analytical thinking, problem-solving abilities, and effective communication of ideas. Some Multisoft Assessment Features :

  • User-friendly interface for easy navigation
  • Secure login and authentication measures to protect data
  • Automated scoring and grading to save time
  • Time limits and countdown timers to manage duration.
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MLOps Fundamentals Corporate Training

Employee training and development programs are essential to the success of businesses worldwide. With our best-in-class corporate trainings you can enhance employee productivity and increase efficiency of your organization. Created by global subject matter experts, we offer highest quality content that are tailored to match your company’s learning goals and budget.


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Learning Assessment

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Certification Training Achievements: Recognizing Professional Expertise

Multisoft Systems is the “one-top learning platform” for everyone. Get trained with certified industry experts and receive a globally-recognized training certificate. Some Multisoft Training Certificate Features :

  • Globally recognized certificate
  • Course ID & Course Name
  • Certificate with Date of Issuance
  • Name and Digital Signature of the Awardee
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MLOps Fundamentals Training FAQ's

  • MLOps, short for Machine Learning Operations, refers to the practices and tools used to streamline and automate the deployment, monitoring, and management of machine learning models in production environments.

  • Data scientists, machine learning engineers, DevOps engineers, IT professionals, software developers, project managers, and business analysts interested in enhancing their skills in deploying and managing machine learning models.

  • While basic familiarity with machine learning concepts is beneficial, this training is designed to accommodate learners with various levels of experience in the field.

  • The training covers a range of tools and technologies used in MLOps, including version control systems like Git, cloud platforms for model deployment, monitoring tools, and automation frameworks.

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