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MLOps Zero to Hero

Learn Production-Grade MLOps using DVC, MLFlow, AWS, Docker, Kubernetes, KServe, SageMaker and Kubeflow.

  1. Topics
  2. IT & Software
  3. MLOps & AI Deployment

MLOps Zero to Hero

InstructorAbhishek Veeramalla
Duration12h 20m
Students21K
Rating4.6 (2,154)
Sponsored
Price
$14.99
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Coupon history

Comidoc has tracked 1 coupon for this course since 2025, last checked 10 months, 5 days ago.

Coupon codeDiscountAddedStatusLifetime
LAUNCHOFFER33% offDec 15, 202509:25 AM UTCExpired4d 22h
Comidoc Analysis

Production-Oriented MLOps via Kubernetes and AWS

Strengths

Infrastructure-First Deployment

The curriculum emphasizes the orchestration layer, covering containerization with Docker and model serving via Kubernetes and KServe.

Practical Tool Integration

One sampled review suggests that learners engage with industry-standard tools including DVC for versioning, MLflow for tracking, and SageMaker for cloud-native workflows.

Structured Conceptual Organization

One sampled review suggests that instructional segments use organized topic overviews to help learners understand how various components fit together before technical implementation.

Limitations

High Technical Barrier to Entry

The steep learning curve requires familiarity with Kubernetes and cloud services, which can be overwhelming for those without a DevOps background.

Limited Advanced Scope

The curriculum lacks depth in model monitoring, performance evaluation, and emerging topics like LLMOps.

Technical Friction Points

Issues include a non-functional S3 resource link and potential command-line discrepancies for macOS users.

Best suited to

  • DevOps engineers transitioning to MLOps
  • Engineers seeking Kubernetes-native model serving
  • Learners interested in AWS SageMaker workflows

Less suited to

  • Data scientists without DevOps or containerization experience
  • Learners seeking advanced LLMOps or observability topics
  • Windows users looking for seamless command-line compatibility

Comidoc Score

6.2/10

Worth considering
Beginner-friendly

Comidoc verdict

The learning path moves from containerization fundamentals to complex orchestration using Kubernetes, KServe, and AWS SageMaker.

Infrastructure-heavy deployment workflows provide a strong technical foundation for DevOps engineers, though the steep learning curve may alienate those without prior containerization or cloud experience. While tool coverage is broad, some learners noted a lack of depth in advanced areas like model monitoring or LLMOps.

This course is best suited for DevOps professionals looking to specialize in machine learning operations through hands-on, infrastructure-led training.

Score breakdown

Curriculum depth
7.3

Covers essential MLOps workflows including versioning and serving, though some signals suggest a need for more depth in monitoring and advanced use cases.

Applied learning
8.0

Includes practical implementations of DVC, MLflow, and Kubernetes-based serving workflows.

Clarity & experience
5.8

Instructional organization is a strength, but OS-specific command issues and steep difficulty levels can impact the experience.

Currency & reliability
4.3

While covering modern tools, some learners report missing highly relevant topics like LLMOps and functional resource links.

Audience fit
4.3

Aligns well with DevOps-to-MLOps transitions but presents a high barrier for those without existing infrastructure knowledge.

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