Hone Your Skills with Our MLOps (Machine Learning Operations) Fundamentals Course

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MLOps (Machine Learning Operations) Fundamentals Course Overview

The MLOps (Machine Learning Operations) Fundamentals Certification validates professionals' ability to leverage machine learning technologies and operational strategies in an industrial context. It's aimed at ensuring these tech experts can design, implement, and manage machine learning solutions efficiently to aid business processes. This certification focuses on the principles of designing scalable machine learning models, adhering to regulatory compliance, and utilizing automation for streamlining workflows. Professionals with this certificate are apt in monitoring and managing ML models, ensuring their optimal performance. Industries employ these specialists to remain competitive, make data-driven decisions, and create innovative solutions with the potential to disrupt the market.

This is a Rare Course and it can be take up to 3 weeks to arrange the training.

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The 1-on-1 Advantage

Get 1-on-1 session with our expert trainers at a date & time of your convenience.
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Flexible Dates

Start your session at a date of your choice-weekend & evening slots included, and reschedule if necessary.
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4-Hour Sessions

Training never been so convenient- attend training sessions 4-hour long for easy learning.
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Destination Training

Attend trainings at some of the most loved cities such as Dubai, London, Delhi(India), Goa, Singapore, New York and Sydney.

You will learn:

Module 1: Why and When do we need MLOps
  • Discuss Data Scientists' pain points.
  • Identify ML Engineering characteristics and challenges
  • Define how Google Cloud can help with MLOps.
  • Recognize how MLOps differs from manual ML management.
  • Compare and contrast DevOps vs MLOps.
  • Define what is a Docker container.
  • Create Docker containers.
  • Identify the architecture of Kubernetes: pods, namespaces.
  • Create Docker containers using Google Container Builder
  • Store container images in Google Container Registry.
  • Create a Kubernetes Engine cluster.
  • Manage Kubernetes deployments
  • Identify the benefits and opportunities of AI Pipelines
  • Define Access Controls within AI Pipelines.
  • Recognize pipeline components.
  • List pipeline workflows.
  • Set up AI Platform Pipelines
  • Create a machine learning pipeline.
  • Run a machine learning pipeline.
  • Connect to AI Platform Pipelines using the Kubeflow Pipelines SDK
  • Configure a Google Kubernetes Engine cluster for AI Platform Pipelines.
  • Identify the main concepts of MLOps on AI Platform.
  • Create a reproducible dataset.
  • Implement a tunable model.
  • Build and push a training container.
  • Train and tune a model.
  • Serve and query a model.
  • Recognize how Kubeflow Pipelines fits in MLOps.
  • Describe a Kubeflow Pipeline with KF DSL.
  • Use the various Kubeflow components.
  • Compile, upload, and run a pipeline build in Kubeflow Pipelines.
  • Create Cloud Build Builders
  • Configure pipelines with Cloud Build.
  • Create triggers for training models using Cloud Build Triggers.
  • Adopt the best CI/CD practices in the context of ML systems.
  • Summarize the course.
Live Online Training (Duration : 8 Hours) Fee On Request
We Offer :
  • 1-on-1 Public - Select your own start date. Other students can be merged.
  • 1-on-1 Private - Select your own start date. You will be the only student in the class.

4 Hours
8 Hours
Week Days
Weekend

Start Time : At any time

12 AM
12 PM

1-On-1 Training is Guaranteed to Run (GTR)
Group Training
Date On Request
MLOps (Machine Learning Operations) Fundamentals
Course Prerequisites
• Basic understanding of Machine Learning and Data Science concepts
• Familiarity with Cloud platforms like AWS, Google Cloud, Microsoft Azure
• Coding skills in Python, R or equivalent languages
• Knowledge of DevOps principles and tools
• Complex problem-solving ability
• Basic understanding of SQL database

MLOps (Machine Learning Operations) Fundamentals Certification Training Overview

MLOps (Machine Learning Operations) Fundamentals certification training is designed to impart the essential skills needed to manage, scale, and deploy machine learning models effectively. The course generally covers topics like DevOps for Machine Learning, automated machine learning, model versioning, validation, delivery, monitor for drift, and retraining models. It introduces best practices for machine learning pipelines, creates a robust Machine Learning Operations strategy, and provides an understanding of MLOps' role in project development.

Why Should You Learn MLOps (Machine Learning Operations) Fundamentals?

Learning MLOps Fundamentals course in stats provides profound knowledge in handling machine learning models efficiently, streamlining workflows, ensuring consistent performance, and avoiding bottlenecks. It equips learners with essential skills to manage machine learning life cycles, enhance productivity, and boost business decisions, which are crucial in today's data-driven world.

Target Audience for MLOps (Machine Learning Operations) Fundamentals Certification Training

- Data scientists aiming to enhance ML model management skills
- IT professionals interested in machine learning operations
- Software engineers focusing on improving AI application deployment
- Tech leaders aiming to optimize AI/ML resources
- AI developers looking to understand ML operations in depth
- Any individuals with interest in the application of ML models in various industries

Why Choose Koenig for MLOps (Machine Learning Operations) Fundamentals Certification Training?

• Certified Professionals: Koenig Solutions has certified instructors with practical knowledge to ensure comprehensive learning.
• Career Enhancement: Gain MLOps Fundamentals certifications to stand out in the job market and boost your career prospects.
• Customized Training: Koenig offers tailored training programs to meet individual learning requirements.
• Affordable Pricing: Koenig provides cost-effective training ensuring value for money.
• Flexible Dates: Arrange classes according to your availability.
• Online Training: Offers instructor-led online training for flexible and convenient learning.
• Course Variety: Offers a wide range of accredited IT and management courses.
• Reputed Institute: Koenig is a top training institute recognized globally.
• Destination Training: Provides a unique learning experience through the destination training model.

MLOps (Machine Learning Operations) Fundamentals Skills Measured

After completing MLOps (Machine Learning Operations) Fundamentals certification training, an individual can acquire skills in managing the entire machine learning lifecycle, optimizing models for specific workloads, deploying machine learning models in production, integrating AI in software development process, understanding data versioning, facilitating collaboration in ML teams, understanding tools for experiment tracking and model versioning, automating model retraining and deployment and addressing real-world scenarios through case studies and projects in MLOps.

Top Companies Hiring MLOps (Machine Learning Operations) Fundamentals Certified Professionals

Major tech giants such as Google, Amazon, Microsoft, Facebook, IBM, Airbnb, and Uber are actively hiring MLOps Fundamentals certified professionals. These companies require MLOps experts who can help in managing and automating their machine learning system's lifecycle. Startups in the AI and machine learning space are also keen on hiring these professionals.

Learning Objectives - What you will Learn in this MLOps (Machine Learning Operations) Fundamentals Course?

The learning objectives of the MLOps (Machine Learning Operations) Fundamentals course include: understanding the key concepts and significance of MLOps in automating and managing the lifecycle of machine learning models; gaining proficiency in utilizing platforms and tools for ML model development, deployment, and monitoring; learning industry practices for continuous integration, testing, and delivery in ML systems. Additionally, students will develop skills to identify and mitigate potential challenges and issues in ML project execution. Hands-on experience in implementing MLOps pipelines and industry case studies will help students grasp real-world applications. Finally, they will learn about the importance of collaboration and communication between data scientists, ML engineers, and operation teams.
Student Name Feedback
Muthu Raja
United States
A2. overall good

FAQ's

1-on-1 Public - Select your start date. Other students can be merged.
1-on-1 Private - Select your start date. You will be the only student in the class.
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