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Machine Learning Guide

MLA 019 Cloud, DevOps & Architecture

Machine Learning Guide

OCDevel

Artificial, Introduction, Learning, Courses, Technology, Ml, Intelligence, Ai, Machine, Education

4.9 • 848 Ratings

🗓️ 13 January 2022

⏱️ 75 minutes

🔗️ Recording | iTunes | RSS

🧾️ Download transcript

Summary

The deployment of machine learning models for real-world use involves a sequence of cloud services and architectural choices, where machine learning expertise must be complemented by DevOps and architecture skills, often requiring collaboration with professionals. Key concepts discussed include infrastructure as code, cloud container orchestration, and the distinction between DevOps and architecture, as well as practical advice for machine learning engineers wanting to deploy products securely and efficiently.

Links

;## Translating Machine Learning Models to Production

  • After developing and training a machine learning model locally or using cloud tools like AWS SageMaker, it must be deployed to reach end users.
  • A typical deployment stack involves the trained model exposed via a SageMaker endpoint, a backend server (e.g., Python FastAPI on AWS ECS with Fargate), a managed database (such as AWS RDS Postgres), an application load balancer (ALB), and a public-facing frontend (e.g., React app hosted on S3 with CloudFront and Route 53).

Infrastructure as Code and Automation Tools

  • Infrastructure as code (IaC) manages deployment and maintenance of cloud resources using tools like Terraform, allowing environments to be version-controlled and reproducible.
  • Terraform is favored for its structured approach and cross-cloud compatibility, while other tools like Cloud Formation (AWS-specific) and Pulumi offer alternative paradigms.
  • Configuration management tools such as Ansible, Chef, and Puppet automate setup and software installation on compute instances but are increasingly replaced by containerization and Dockerfiles.
  • Continuous Integration and Continuous Deployment (CI/CD) pipelines (with tools like AWS CodePipeline or CircleCI) automate builds, testing, and code deployment to infrastructure.

Containers, Orchestration, and Cloud Choices

  • Containers, enabled by Docker, allow developers to encapsulate applications and dependencies, facilitating consistency across environments from local development to production.
  • Deployment options include AWS ECS/Fargate for managed orchestration, Kubernetes for large-scale or multi-cloud scenarios, and simpler services like AWS App Runner and Elastic Beanstalk for small-scale applications.
  • Kubernetes provides robust flexibility and cross-provider support but brings high complexity, making it best suited for organizations with substantial infrastructure needs and experienced staff.
  • Use of cloud services versus open-source alternatives on Kubernetes (e.g., RDS vs. Postgres containers) affects manageability, vendor lock-in, and required expertise.

DevOps and Architecture: Roles and Collaboration

  • DevOps unites development and operations through common processes and tooling to accelerate safe production deployments and improve coordination.
  • Architecture focuses on the holistic design of systems, establishing how different technical components fit together and serve overall business or product goals.
  • There is significant overlap, but architecture plans and outlines systems, while DevOps engineers implement, automate, and monitor deployment and operations.
  • Cross-functional collaboration is essential, as machine learning engineers, DevOps, and architects must communicate requirements, constraints, and changes, especially regarding production-readiness and security.

Security, Scale, and When to Seek Help

  • Security is a primary concern when moving to production, especially if handling sensitive data or personally identifiable information (PII); professional DevOps involvement is strongly advised in such cases.
  • Common cloud security pitfalls include publicly accessible networks, insecure S3 buckets, and improper handling of secrets and credentials.
  • For experimentation or small-scale safe projects, machine learning engineers can use tools like Terraform, Docker, and AWS managed services, but should employ cloud cost monitoring to avoid unexpected bills.

Cloud Providers and Service Considerations

  • AWS dominates the cloud market, followed by Azure (strong in enterprise/Microsoft-integrated environments) and Google Cloud Platform (GCP), which offers a strong user interface but has a record of sunsetting products.
  • Managed cloud machine learning services, such as AWS SageMaker and GCP Vertex AI, streamline model training, deployment, and monitoring.
  • Vendor-specific tools simplify management but limit portability, while Kubernetes and its ML pipelines (e.g., Kubeflow, Apache Airflow) provide open-source, cross-cloud options with greater complexity.

Recommended Learning Paths and Community Resources

  • Learning and prototyping with Terraform, Docker, and basic cloud services is encouraged to understand deployment pipelines, but professional security review is critical before handling production-sensitive data.
  • For those entering DevOps, structured learning with platforms like aCloudGuru or AWS’s own curricula can provide certification-ready paths.
  • Continual learning is necessary, as tooling and best practices evolve rapidly.

Reference Links

Expert coworkers at Dept

DevOps Tools

Visual Guides and Comparisons

Learning Resources

Transcript

Click on a timestamp to play from that location

0:00.0

Welcome back to Machine Learning Applied. In this episode, I'm going to be interviewing

0:04.0

co-workers from Dept. Matt is an expert in architecture, and Giroat is an expert in DevOps or

0:10.8

developer operations. These two skills combine in the deploying of a full-fledged product that can be

0:19.8

used by consumers, a web app, a mobile app. Most of what we've

0:24.7

talked about in this podcast series is machine learning, how to develop and train your model

0:30.2

inside of a Docker container, or even developing and training your models on the cloud by way of

0:36.2

AWS SageMakers's studio notebooks.

0:40.4

Now, our skill sets are on the machine learning side, but eventually you want to get that

0:45.6

model into the hands of a customer.

0:48.4

After you have trained your model, whether on Local Host or on SageMaker, you will deploy your model through SageMaker to a SageMaker

0:58.0

Rest endpoint or to a model registry that can be called as SageMaker batch transform jobs

1:04.9

or SageMaker serverless inference jobs. So now you have your deployed machine learning model ready to be used,

1:13.4

but who's going to use it? That's where all the stuff from this episode comes in. Now, I know I have

1:18.8

talked about a lot of the tooling and concepts from this episode in the past. And I promise,

1:24.8

I'm not going to turn this podcast series into a full stack

1:27.7

slash architecture slash DevOps podcast. And that's actually why I'm creating this episode. I wanted

1:33.8

to talk to my colleagues who are experts in the field. Stop bumbling my way through these episodes

1:39.3

and wasting your guys' time and just say, hey, Matt and Gerouat, let's nail a coffin. How do you do this as a

1:46.1

machine learning engineer? How do you productize and deploy your machine learning model as a full

1:52.3

product in the cloud? Should you do that? Now, to get you thinking along that track, I want to talk

1:59.7

about my journey with Nothi.

2:01.7

Nothi was inspired by the publication and accessibility of these Transformers, NLP models.

...

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