In Part 1 of this collection, how Azure and AWS take essentially completely different approaches to machine studying undertaking administration and information storage.
Azure ML makes use of a workspace-centric construction with user-level role-based entry management (RBAC), the place permissions are granted to people primarily based on their tasks. In distinction, AWS SageMaker adopts a job-centric structure that decouples person permissions from job execution, granting entry on the job stage by IAM roles. For information storage, Azure ML depends on datastores and information property inside workspaces to handle connections and credentials behind the scenes, whereas AWS SageMaker integrates immediately with S3 buckets, requiring express permission grants for SageMaker execution roles to entry information.
Discover out extra on this article:
Having established how these platforms deal with undertaking setup and information entry, in Half 2, we’ll study the compute assets 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 elementary constructing blocks of cloud computing. Compute assets sometimes symbolize the most important value element of an ML undertaking, as coaching fashions—particularly giant AI fashions—requires lengthy coaching occasions and infrequently specialised compute cases (e.g., GPU cases) with larger prices. Subsequently, Azure ML designs a devoted AzureML Compute Operator function (see particulars in Part 1) for managing compute assets.
Azure and AWS supply varied occasion sorts that differ within the variety of CPUs/GPUs, reminiscence, disk area and sort, every designed for particular functions. Each platforms use a pay-as-you-go pricing mannequin, charging just for energetic compute time.
Azure virtual machine series 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 instances are additionally grouped into households primarily based on their goal; as an example, the m5 household accommodates general-purpose cases for SageMaker ML improvement. The desk beneath compares compute cases provided by Azure and AWS primarily based on their goal, hourly pricing and typical use circumstances. (Please notice 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 assets into ML techniques.
Azure ML
Computes are persistent assets within the Azure ML Workspace, sometimes created as soon as by the AzureML Compute Operator and reused by the information science group. Since compute assets are cost-intensive, this construction permits them to be centrally managed by a task with cloud infrastructure experience, whereas information scientists and engineers can give attention to improvement work.
Azure presents 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 primarily based 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 primarily based in your use case.
”](https://contributor.insightmediagroup.io/wp-content/uploads/2026/02/image-2-1024x477.png "image from “[Explore and configure the Azure Machine Learning workspace DP-100](https://www.youtube.com/watch?v=_f5dlIvI5LQ)”")
To create an Azure ML managed compute goal we create an AmlCompute object utilizing the code beneath:
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_instancesandmax_instances(non-obligatory): set the vary of cases allowed to run concurrently.idle_time_before_scale_down(non-obligatory): 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 group collaboration.
# Use the persevered 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 assets are managed by a standalone AWS service – EC2 (Elastic Compute Cloud). When utilizing these compute assets 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 method provides builders extra flexibility over compute choice primarily based on process, 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 beneath, 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 primarily based on the job runtime.
estimator = Estimator(
image_uri=image_uri,
function=function,
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 aren’t any out there spot cases. We use the code snippet beneath to implement a spot occasion possibility for a coaching job.
use_spot_instances: set asTrueto make use of spot cases, in any other case default to on-demandmax_wait: the utmost period of time you’re prepared to attend for out there spot cases (ready time is just not charged)max_run: the utmost quantity of coaching time allowed for the jobcheckpoint_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,
function=function,
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 observe?
- Azure ML: Azure’s persistent compute method permits centralized administration and sharing throughout a number of builders, permitting information scientists to give attention to 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 additionally 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 accountable for the underlying infrastructure and {hardware} picks, atmosphere setup is essential in making certain constant and reproducible behaviors throughout improvement and manufacturing atmosphere, mitigating bundle conflicts and dependency points when executing the identical code in numerous runtime setup by completely different builders. Azure ML and SageMaker each assist utilizing their curated environments and establishing {custom} environments.
Azure ML
Just like Knowledge and Compute, Atmosphere is taken into account a sort of useful resource and asset within the Azure ML Workspace. Azure ML presents a complete checklist of curated environments for fashionable python frameworks (e.g. PyTorch, Tensorflow, scikit-learn) designed for CPU or GPU/CUDA goal.
The code snippet beneath helps to retrieve the checklist of all curated environments in Azure ML. They typically 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.checklist()
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 beneath.
# 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 ascertain the OS, frameworks and packages required for job execution. These are Constructed-in Algorithm, Convey Your Personal Script (Script mode) and Convey Your Personal Container (BYOC), starting from the most straightforward 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 supply an equal built-in algorithm method 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 ok and passing the coaching information to suit the mannequin. Then the coaching job will spin up a ml.m5.giant compute occasion and the educated mannequin shall be saved within the output location.
Convey Your Personal Script
The deliver your personal script method (also referred to as script mode or deliver your personal mannequin) permits builders to leverage SageMaker’s prebuilt containers for fashionable python frameworks for machine studying like scikit-learn, PyTorch and Tensorflow. It supplies the flexibleness of customizing the coaching job by your personal script with out the necessity of managing the job execution atmosphere, making it the preferred alternative when utilizing specialised algorithms not included in SageMaker’s built-in choices.
Within the instance beneath, we instantiate an estimator utilizing the scikit-learn framework by offering a {custom} coaching script train.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",
function=function,
instance_count=1,
instance_type="ml.m5.giant",
py_version="py3",
framework_version="1.2-1",
script_mode=True,
hyperparameters={"estimators": 20},
)
# Prepare the estimator
sk_estimator.match({"prepare": training_data})Convey Your Personal Container
That is the method with the very best stage 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 includes constructing a Docker picture that accommodates all required bundle 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 beneath, we specify the {custom} docker picture URI as a parameter to create the estimator and match the estimator with coaching information.
from sagemaker.estimator import Estimator
image_uri = ":"
byoc_estimator = Estimator(
image_uri=image_uri,
function=function,
instance_count=1,
instance_type="ml.m5.giant",
output_path=" What does it imply in observe?
- Azure ML: Supplies assist for working coaching jobs utilizing its intensive assortment of curated environments that cowl fashionable frameworks reminiscent of PyTorch, TensorFlow, and scikit-learn, in addition to providing the potential to construct and configure {custom} environments from Docker photographs for extra specialised use circumstances. Nevertheless, you will need to notice that Azure ML doesn’t at present supply the built-in algorithm method that encapsulates and packages fashionable machine studying algorithms immediately into the atmosphere in the identical means that SageMaker does.
- AWS SageMaker AI: SageMaker is understood for its three stage of customizations—Constructed-in Algorithm, Convey Your Personal Script, Convey Your Personal Container—which cowl a spectrum of builders necessities. Constructed-in Algorithm and Convey 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
Based mostly on the comparisons of Compute and Atmosphere above together with what we mentioned in AWS vs. Azure: A Deep Dive into Model Training — Part 1 (Challenge Setup and Knowledge Storage), we might have realized the 2 platforms undertake completely different design rules to construction their machine studying ecosystems.
Azure ML follows a extra modular structure the place Knowledge, Compute, and Atmosphere are handled as unbiased assets and property inside the Azure ML Workspace. Since they are often configured and managed individually, this method is extra beginner-friendly, particularly for customers with out intensive 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 while not having infrastructural experience to handle compute cases.
AWS SageMaker has a steeper studying curve, as a number of companies are tightly coupled and orchestrated collectively as a holistic system for ML job execution. Nevertheless, this job-centric method presents clear separation between mannequin coaching and mannequin deployment environments, in addition to the flexibility for distributed coaching at scale. By giving builders extra infrastructure management, SageMaker is properly suited to large-scale information science and AI groups with excessive MLOps maturity and the necessity of CI/CD pipelines.
Take-Residence Message
On this collection, we examine the 2 hottest cloud platforms Azure and AWS for scalable mannequin coaching, breaking down the comparability into the next dimensions:
- Challenge and Permission Administration
- Knowledge storage
- Compute
- Atmosphere
In Part 1, we mentioned high-level undertaking setup and permission administration, then talked about storing and accessing the information required for mannequin coaching.
In Half 2, we examined how Azure ML’s persistent, workspace-centric compute assets differ from AWS SageMaker’s on-demand, job-specific method. Moreover, we explored atmosphere customization choices, from Azure’s curated environments and {custom} environments to SageMaker’s three stage of customizations—Constructed-in Algorithm, Convey Your Personal Script, Convey Your Personal Container. This comparability reveals Azure ML’s modular, beginner-friendly structure vs. SageMaker’s built-in, job-centric design that provides larger scalability and infrastructure management for groups with MLOps necessities.
