AWS vs. Azure: A Deep Dive into Mannequin Coaching – Half 2

Having established how these platforms deal with venture setup and knowledge entry, in Half 2, we’ll study the compute sources and runtime environments that energy the mannequin coaching jobs.

Compute

Compute is the digital machine the place your mannequin and code run. Together with community and storage, it is among the basic constructing blocks of cloud computing. Compute sources usually characterize the most important value element of an ML venture, as coaching fashions—particularly giant AI fashions—requires lengthy coaching instances and sometimes specialised compute cases (e.g., GPU cases) with greater prices. Subsequently, Azure ML designs a devoted AzureML Compute Operator position (see particulars in Half 1) for managing compute sources.

Azure and AWS provide numerous occasion sorts that differ within the variety of CPUs/GPUs, reminiscence, disk house and sort, every designed for particular functions. Each platforms use a pay-as-you-go pricing mannequin, charging just for lively compute time.

Azure digital machine collection are named in alphabetic order; as an example, D household VMs are designed for general-purpose workloads and meet the necessities for many improvement and manufacturing environments. AWS compute cases are additionally grouped into households based mostly on their function; as an example, the m5 household accommodates general-purpose cases for SageMaker ML improvement. The desk under compares compute cases provided by Azure and AWS based mostly on their function, hourly pricing and typical use instances. (Please word that the pricing construction varies by area and plan, so I like to recommend trying out their official web sites.)

Now that we’ve in contrast compute pricing in AWS and Azure, let’s discover how the 2 platforms differ in integrating compute sources into ML techniques.

Azure ML

Computes are persistent sources within the Azure ML Workspace, usually created as soon as by the AzureML Compute Operator and reused by the information science crew. Since compute sources are cost-intensive, this construction permits them to be centrally managed by a task with cloud infrastructure experience, whereas knowledge scientists and engineers can concentrate on improvement work.

Azure provides a spectrum of compute goal choices designated for ML improvement and deployment, relying on the dimensions of the workload. A compute occasion is a single-node machine appropriate for interactive improvement and testing within the Jupyter pocket book atmosphere. A compute cluster is one other kind of compute goal that spins up multi-node cluster machines. It may be scaled for parallel processing based mostly on workload demand and helps auto-scaling by configuring the parameter min_instances and max_instances. Moreover, there are severless compute, Kubernetes clusters, and containers which are match for various functions. Here’s a helpful visible abstract that helps you make the choice based mostly in your use case.

To create an Azure ML managed compute goal we create an AmlCompute object utilizing the code under:

• kind: use"amlcompute" for compute cluster. Alternatively, use "computeinstance" for single-node interactive improvement and “kubernetes" for AKS clusters.
• title: specify the compute goal title.
• measurement: specify the occasion measurement.
• min_instances and max_instances (non-compulsory): set the vary of cases allowed to run concurrently.
• idle_time_before_scale_down (non-compulsory): routinely shut down the compute cluster when idle to keep away from incurring pointless prices.

# Create a compute cluster
cpu_cluster = AmlCompute(
    title="cpu-cluster",
    kind="amlcompute",
    measurement="Standard_DS3_v2",
    min_instances=0,
    max_instances=4,
    idle_time_before_scale_down=120
)

# Create or replace the compute
ml_client.compute.begin_create_or_update(cpu_cluster)

As soon as the compute useful resource is created, anybody within the shared Workspace can use it by merely referencing its title in an ML job, making it simply accessible for crew collaboration.

# Use the endured compute "cpu-cluster" within the job
job = command(
    code='./src',
    command='python code.py',
    compute='cpu-cluster',
    display_name='train-custom-env',
    experiment_name='coaching'
)

AWS SageMaker AI

Compute sources are managed by a standalone AWS service – EC2 (Elastic Compute Cloud). When utilizing these compute sources in SageMaker, it require builders to explicitly configure the occasion kind for every job, then compute cases are created on-demand and terminated when the job finishes. This strategy provides builders extra flexibility over compute choice based mostly on job, however requires extra infrastructure data to pick and handle the suitable compute useful resource. For instance, out there occasion sorts differ by job kind. ml.t3.medium and ml.t3.giant are generally used for powering SageMaker notebooks in interactive improvement environments, however they don’t seem to be out there for coaching jobs, which require extra highly effective occasion sorts from the m5, c5, p3, or g4dn households.

As proven within the code snippet under, AWS SageMaker specifies the compute occasion and the variety of cases working concurrently as job parameters. A compute occasion with the ml.m5.xlarge kind is created throughout job execution and charged based mostly on the job runtime.

estimator = Estimator(
    image_uri=image_uri,
    position=position,  
    instance_type="ml.m5.xlarge", 
    instance_count=1
)

SageMaker jobs spin up on-demand cases by default. They’re charged by seconds and supplies assured capability for working time-sensitive jobs. For jobs that may tolerate interruptions and better latency, spot occasion is a extra cost-saving possibility that makes use of unused compute cases. The draw back is the extra ready interval when there are not any out there spot cases. We use the code snippet under to implement a spot occasion possibility for a coaching job.

• use_spot_instances: set as True to make use of spot cases, in any other case default to on-demand
• max_wait: the utmost period of time you’re keen to attend for out there spot cases (ready time shouldn’t be charged)
  max_run: the utmost quantity of coaching time allowed for the job
• checkpoint_s3_uri: the S3 bucket URI path to save lots of mannequin checkpoints, in order that coaching can safely restart after ready

estimator = Estimator(
    image_uri=image_uri,
    position=position,  
    instance_type="ml.m5.xlarge", 
    instance_count=1,
    use_spot_instances=True, 
    max_run=3600,
    max_wait=7200,  
    checkpoint_s3_uri=""  
)

What does this imply in follow?

• Azure ML: Azure’s persistent compute strategy permits centralized administration and sharing throughout a number of builders, permitting knowledge scientists to concentrate on mannequin improvement relatively than infrastructure administration.
• AWS SageMaker AI: SageMaker requires builders to explicitly outline compute occasion kind for every job, offering extra flexibility but in addition demanding deeper infrastructure data of occasion sorts, prices and availability constraints.

Reference

Atmosphere

Atmosphere defines the place the code or job is run, together with software program, working system, program packages, docker picture and atmosphere variables. Whereas compute is answerable for the underlying infrastructure and {hardware} picks, atmosphere setup is essential in guaranteeing constant and reproducible behaviors throughout improvement and manufacturing atmosphere, mitigating package deal conflicts and dependency points when executing the identical code in several runtime setup by totally different builders. Azure ML and SageMaker each help utilizing their curated environments and organising {custom} environments.

Azure ML

Just like Information and Compute, Atmosphere is taken into account a sort of useful resource and asset within the Azure ML Workspace. Azure ML provides a complete listing of curated environments for standard python frameworks (e.g. PyTorch, Tensorflow, scikit-learn) designed for CPU or GPU/CUDA goal.

The code snippet under helps to retrieve the listing of all curated environments in Azure ML. They often comply with a naming conference that features the framework title, model, working system, Python model, and compute goal (CPU/GPU), e.g.AzureML-sklearn-1.0-ubuntu20.04-py38-cpu signifies scikit-learn model 1.0, working on Ubuntu 20.04 with Python 3.8 for CPU compute.

envs = ml_client.environments.listing()
for env in envs:
    print(env.title)
    
    
# >>> Auzre ML Curated Environments
"""
AzureML-AI-Studio-Growth
AzureML-ACPT-pytorch-1.13-py38-cuda11.7-gpu
AzureML-ACPT-pytorch-1.12-py38-cuda11.6-gpu
AzureML-ACPT-pytorch-1.12-py39-cuda11.6-gpu
AzureML-ACPT-pytorch-1.11-py38-cuda11.5-gpu
AzureML-ACPT-pytorch-1.11-py38-cuda11.3-gpu
AzureML-responsibleai-0.21-ubuntu20.04-py38-cpu
AzureML-responsibleai-0.20-ubuntu20.04-py38-cpu
AzureML-tensorflow-2.5-ubuntu20.04-py38-cuda11-gpu
AzureML-tensorflow-2.6-ubuntu20.04-py38-cuda11-gpu
AzureML-tensorflow-2.7-ubuntu20.04-py38-cuda11-gpu
AzureML-sklearn-1.0-ubuntu20.04-py38-cpu
AzureML-pytorch-1.10-ubuntu18.04-py38-cuda11-gpu
AzureML-pytorch-1.9-ubuntu18.04-py37-cuda11-gpu
AzureML-pytorch-1.8-ubuntu18.04-py37-cuda11-gpu
AzureML-sklearn-0.24-ubuntu18.04-py37-cpu
AzureML-lightgbm-3.2-ubuntu18.04-py37-cpu
AzureML-pytorch-1.7-ubuntu18.04-py37-cuda11-gpu
AzureML-tensorflow-2.4-ubuntu18.04-py37-cuda11-gpu
AzureML-Triton
AzureML-Designer-Rating
AzureML-VowpalWabbit-8.8.0
AzureML-PyTorch-1.3-CPU
"""

To run the coaching job in a curated atmosphere, we create an atmosphere object by referencing its title and model, then passing it as a job parameter.

# Get an curated Atmosphere
atmosphere = ml_client.environments.get("AzureML-sklearn-1.0-ubuntu20.04-py38-cpu", model=44)

# Use the curated atmosphere in Job
job = command(
    code=".",
    command="python prepare.py",
    atmosphere=atmosphere,
    compute="cpu-cluster"
)

ml_client.jobs.create_or_update(job)

Alternatively, create a {custom} atmosphere from a Docker picture registered in Docker Hob utilizing the code snippet under.

# Get an curated Atmosphere
atmosphere = ml_client.environments.get("AzureML-sklearn-1.0-ubuntu20.04-py38-cpu", model=44)

# Use the curated atmosphere in Job
job = command(
    code=".",
    command="python prepare.py",
    atmosphere=atmosphere,
    compute="cpu-cluster"
)

ml_client.jobs.create_or_update(job)

AWS SageMaker AI

SageMaker’s atmosphere configuration is tightly coupled with job definitions, providing three ranges of customization to determine the OS, frameworks and packages required for job execution. These are Constructed-in Algorithm, Carry Your Personal Script (Script mode) and Carry Your Personal Container (BYOC), starting from the simplest but inflexible choice to probably the most complicated but customizable possibility.

Constructed-in Algorithms

That is the choice with the least quantity of effort for builders to coach and deploy machine studying fashions at scale in AWS SageMaker and Azure at present doesn’t provide an equal built-in algorithm strategy utilizing Python SDK as of February 2026.

SageMaker encapsulates the machine studying algorithm, in addition to its python library and framework dependencies inside an estimator object. For instance, right here we instantiate a KMeans estimator by specifying the algorithm-specific hyperparameter okay and passing the coaching knowledge to suit the mannequin. Then the coaching job will spin up a ml.m5.giant compute occasion and the skilled mannequin can be saved within the output location.

Carry Your Personal Script

The deliver your personal script strategy (also referred to as script mode or deliver your personal mannequin) permits builders to leverage SageMaker’s prebuilt containers for standard python frameworks for machine studying like scikit-learn, PyTorch and Tensorflow. It supplies the pliability of customizing the coaching job by means of your personal script with out the necessity of managing the job execution atmosphere, making it the most well-liked alternative when utilizing specialised algorithms not included in SageMaker’s built-in choices.

Within the instance under, we instantiate an estimator utilizing the scikit-learn framework by offering a {custom} coaching script prepare.py, the mannequin’s hyperparameters, together with the framework model and python model.

from sagemaker.sklearn import SKLearn

sk_estimator = SKLearn(
    entry_point="prepare.py",
    position=position,
    instance_count=1,
    instance_type="ml.m5.giant",
    py_version="py3",
    framework_version="1.2-1",
    script_mode=True,
    hyperparameters={"estimators": 20},
)

# Practice the estimator
sk_estimator.match({"prepare": training_data})

Carry Your Personal Container

That is the strategy with the best degree of customization, which permits builders to deliver a {custom} atmosphere utilizing a Docker picture. It fits situations that depend on unsupported python frameworks, specialised packages, or different programming languages (e.g. R, Java and so on). The workflow entails constructing a Docker picture that accommodates all required package deal dependencies and mannequin coaching scripts, then push it to Elastic Container Registry (ECR), which is AWS’s container registry service equal to Docker Hub.

Within the code under, we specify the {custom} docker picture URI as a parameter to create the estimator and match the estimator with coaching knowledge.

from sagemaker.estimator import Estimator

image_uri = ":"

byoc_estimator = Estimator(
    image_uri=image_uri,
    position=position,
    instance_count=1,
    instance_type="ml.m5.giant",
    output_path="",
    sagemaker_session=sess,
)

byoc_estimator.match(training_data)

What does it imply in follow?

• Azure ML: Supplies help for working coaching jobs utilizing its in depth assortment of curated environments that cowl standard frameworks resembling PyTorch, TensorFlow, and scikit-learn, in addition to providing the aptitude to construct and configure {custom} environments from Docker photos for extra specialised use instances. Nonetheless, you will need to word that Azure ML doesn’t at present provide the built-in algorithm strategy that encapsulates and packages standard machine studying algorithms straight into the atmosphere in the identical method that SageMaker does.
• AWS SageMaker AI: SageMaker is thought for its three degree of customizations—Constructed-in Algorithm, Carry Your Personal Script, Carry Your Personal Container—which cowl a spectrum of builders necessities. Constructed-in Algorithm and Carry Your Personal Script use AWS’s managed environments and combine tightly with ML algorithms or frameworks. They provide simplicity however are much less appropriate for extremely specialised mannequin coaching processes.

In Abstract

Primarily based on the comparisons of Compute and Atmosphere above together with what we mentioned in AWS vs. Azure: A Deep Dive into Mannequin Coaching — Half 1 (Challenge Setup and Information Storage), we might have realized the 2 platforms undertake totally different design rules to construction their machine studying ecosystems.

Azure ML follows a extra modular structure the place Information, Compute, and Atmosphere are handled as impartial sources and property inside the Azure ML Workspace. Since they are often configured and managed individually, this strategy is extra beginner-friendly, particularly for customers with out in depth cloud computing or permission administration data. As an example, a knowledge scientist can create a coaching job by attaching an current compute within the Workspace without having infrastructural experience to handle compute cases.

AWS SageMaker has a steeper studying curve, as a number of providers are tightly coupled and orchestrated collectively as a holistic system for ML job execution. Nonetheless, this job-centric strategy provides clear separation between mannequin coaching and mannequin deployment environments, in addition to the power for distributed coaching at scale. By giving builders extra infrastructure management, SageMaker is effectively suited to large-scale knowledge science and AI groups with excessive MLOps maturity and the necessity of CI/CD pipelines.

Take-Residence Message

On this collection, we evaluate the 2 hottest cloud platforms Azure and AWS for scalable mannequin coaching, breaking down the comparability into the next dimensions:

• Challenge and Permission Administration
• Information storage
• Compute
• Atmosphere

In Half 1, we mentioned high-level venture setup and permission administration, then talked about storing and accessing the information required for mannequin coaching.