Famous Machine Learning Platforms
Microsoft Azure Machine learning
The Azure Machine Learning services authorize data scientists and developers with productive experiences of a wide range for training, deploying, and building models of machine learning rapidly. Accelerate market time and foster collaboration of team with industry-leading DevOps and MLOps for machine learning (ML). It is innovated on a trusted platform, secure, and designed for responsible ML.
|
Features |
Security Features |
Capabilities |
|
BoosT
productivity with ML for all skill |
Get
end to end security |
Collaborative
notebook |
|
Automated
machine learning |
Build
on trusted with Azure |
Drag
and drop ML |
|
Operationalize
at scale with MLOps |
Built
in mechanisms for identity
authentication |
Auto-scaling
compute |
|
Build responsible ML solutions |
Manage
governance with audit trials, policies, etc |
RStudio
support |
|
Innovate
on a flexible and open platform |
Accelerate the end-to-end machine learning
lifecycle |
Data
Labeling |
Tensor Flow
TensorFlow is an open-source machine learning platform. It has flexible ecosystem tools, comprehensive,
community and libraries resources, which allows the researcher to push the state of
art in ML and producers simply deploy and build ML motorized applications. It
offers a workflows collection for developing and training models utilizing
JavaScript or Python, and for precisely deploying in the cloud, in the browser, on prem
or on the device, doesn’t matter which language you utilize.
|
Features |
Security Features |
Capabilities |
|
Responsive
Construct |
TensorFlow
Models Programs |
Library
Management |
|
Statistical
Distribution Availability |
Running
Untrusted Models |
Scalability |
|
Layered
Components |
Accepting
the mistrustful input |
Library
Management |
|
Event
Logger |
Reporting
a vulnerability |
Pipelining |
IBM Waston Studio
International
Business Machines (IBM) Waston Studio is an enterprise solution for data
engineers and data scientists. It provides a data suite science tools for
instance, Spark, Zeppelin notebooks, Jupyter, and RStudio, which are integrated
by IBM technologies proprietary. Its interface offers a collaborative space of
Projects for individuals and teams to minimize the value time. Projects can
have data assets, collaborators, and notebooks. It is a social environment for
solving challenges of data with the finest tools and most updated expertise.
|
Features |
Security Features |
Capabilities |
|
Prepare,
refine and explore data |
Administer
can set up SAML2.0 |
Accelerate
deployment time |
|
Bring
your open-source notebooks |
SSH
private key |
Boost
customer segmentation depth |
|
Automatically
build model pipelines with AutoAI |
SSL
certificate and private key |
Minimize
errors |
|
Run
and Train models |
Tokens |
Predict
Outcomes and prescribe actions |
|
Combine
predictive and prescriptive models |
Authentication
and Authorization |
Optimize
AI and cloud economics |
Google Cloud ML Engine
The purpose of Google
cloud Engine is a platform that is hosted for running ML predictions at scale
and training jobs. The service treats independently these processes of
prediction and training. It is probable to utilize Google Cloud ML Engine, for
training a complex model with leveraging the TPU and GPU infrastructure.
|
Features |
Security Features |
Capabilities |
|
Prepare
and store databases with BigQuery and Cloud Storage |
Secure
by design infrastructure |
For
every skill level |
|
Build
best in class ML models |
Security
products |
MLOps,
simplified |
|
Validate the model with AI Explanations and What-If Tool |
Meet
your policy requirement |
Best
of Google’s AI |
|
Deploy
models at scale to get prediction in a cloud with Prediction |
Transparency
and Privacy |
Faster
time to production |
|
Manage
models with Pipelines and applying MLops |
Web
App and API Protection (WAAP) |
Robust
governance with interpretable models |
Amazon Web Services
As per the AWE website,
Amazon Machine Learning is a service managed for constructing models of ML and
producing predictions. Amazon ML involves an automatic tool of data
transformation, simplifying the tool of ML even more for the users. Additionally,
Amazon also provides other tools of ML for instance Amazon SageMaker that is
fully managed platform which forms it simple for data scientists and developers
for using models of ML.
|
Features |
Security Features |
Capabilities |
|
Powerful
data and relationship management |
Data
Protection |
Mobile
Friendly access |
|
Flexible
Schema management |
Identity
and access management |
Server-less
Cloud functions |
|
Fully
managed infrastructure |
Infrastructure
protection |
Different
databases |
|
Searching
across objects and relationships |
Threat
detection and continuous monitoring |
Storage |
|
Built-in
data encryption |
Compliance
and data privacy |
Pre-build
services and cloud-based solutions |
Read More
Features | Amazon Cloud Directory | Amazon Web Services (AWS). (n.d.). Amazon Web Services, Inc. Retrieved February 14, 2021, from https://aws.amazon.com/cloud-directory/features/
TensorFlow Features | Why TensorFlow Is So Popular—DataFlair. (n.d.). Retrieved February 14, 2021, from https://data-flair.training/blogs/tensorflow-features/
TensorFlow Security—Javatpoint. (n.d.). Www.Javatpoint.Com. Retrieved February 14, 2021, from https://www.javatpoint.com/tensorflow-security
Platform. (n.d.). Google Cloud. Retrieved February 14, 2021, from https://cloud.google.com/ai-platform
Privacy and security & Cloud compliance. (n.d.). Google Cloud. Retrieved February 14, 2021, from https://cloud.google.com/security
IBM Watson Studio | IBM. (n.d.). Retrieved February 14, 2021, from https://www.ibm.com/cloud/watson-studio
Security in Watson Studio Local. (2014, October 24). www.ibm.com/support/knowledgecenter/en/sshgwl_1.2.3/local/security.html
Azure Machine Learning—ML as a Service | Microsoft Azure. (n.d.). Retrieved February 13, 2021, from https://azure.microsoft.com/en-us/services/machine-learning/
Comments
Post a Comment