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As the range of advancements in machine learning services continues to increase at a torrid pace, it has effectively created multiple avenues for on-field applications, primarily for brands and companies that already have the necessary infrastructure and resources at their disposal to deploy ML as a decision-making fulcrum in their operational structures.

Google translations have increased accuracy to 85% from 55% using machine learning.

While Amazon Machine Learning is an integral part of the company’s vast array of tech services, AWS SageMaker fundamental advancements in ML.

SageMaker and Amazon Machine Learning are available to the masses as completely Amazon-hosted ML platforms with similar structures yet a varied range of specific applications, suited for different user bases and overall usage.

Both platforms have somewhat resolved the intrinsic challenges of building, training, and then deploying ML models to augment key insights, decision-making processes, and managing complex repetitive workflows.

SageMaker and Amazon Machine Learning do differ, however, in their natural use cases, required resources, and above all, how an organization can aim to use ML to augment operations.

Machine Learning: How does it work?

Artificial Intelligence and ML are smack dab at the center of the 4th industrial revolution that’s unfolding right before eyes, and ML platforms like SageMaker, Azure Machine Learning, Google Cloud AI, IBM Watson and DataRobot are shaping the future as we know it.

Machine Learning is the next step forward for AI thanks to its inherent ability to deduce complex data based on even more complex logical streams and generate results as close to 100% accuracy as possible.

ML essentially asks the computing resource to learn from experience, create its own deduction model (based on algorithms) to simplify a complex data set and work towards achieving the best possible set of predictive analysis as results.

The key to understanding this stands on what we provide to the computer (to compute) and what we expect as results (i.e.the input and output).

In a conventional computing process, the input includes data and commands. The output is the result based on the input, as per the integrated logical processes. But in machine learning, the data and the results are instead provided as inputs, with the computer tasked with finding out the best set of logical processes/algorithms to explain and/or arrive at said results.

Key Takeaways

  • Azure Machine Learning can predict nearly 65% of stock market fluctuations.
  • Amazon has successfully reduced ‘click-to-ship’ time by 225% by integrating ML with its operational structure.
  • Google’s Deep Learning is 89% accurate at detecting breast cancer.

Want to better understand what Machine Learning is and why it is arguably the most powerful IT deliverable yet? Talk to our dedicated team of experts, who are helping lead the way forward with the development of ML integrations for our clients.

What is Amazon Machine Learning?

Amazon Machine Learning functions to the same effect as SageMaker, but on much more scaled-down use cases. Amazon ML is based on the same infrastructure support as SageMaker, although its platform offers a limited array of tools, integrations, and algorithms in comparison. What it does provide is easier accessibility and usability without the steep learning curve generally associated with ML.

Key Features



Database support is based on Amazon S3 along with extended support for Redshift and Amazon RDS. Data inputs can be further adapted for CSVs as well. All data is read, processed and visualized on the platform via Amazon ML APIs. The platform will auto-complete data processing as per the best model.


Training instances are variable across 3 algorithm models – regression, multiclass, and binary. The platform will auto-create training instances based on data provided to arrive at predictions instantly.


Deployment is highly scalable, consistent, and affordable for any business to easily run fundamental predictive analysis models in their operational structures.

Companies using Amazon Machine Learning

Kingsmen Software | Powersports Auction | Cymatic Security | Apli

What is Amazon Sagemaker?

Amazon SageMaker is a machine learning platform backed by AWS cloud infrastructure, a dedicated data repository, 10 dedicated built-in algorithms, and a highly scalable data structure to help professionals ‘create, train, and deploy’ machine learning models.

SageMaker’s one-click training and deployment features are already well-lauded across the industry.

Key Features



SageMaker is based on Jupyter Notebook, and the process starts with a notebook instance creation. Data storage and accessibility are inherent within Jupyter, which helps with server management, fundamental resource allocation, data preparation, and ML model definition to initiate the ML process.

SageMaker further supports external tools (e.g. – Python) and software (e.g. – Tensor Flow) on its platform which already includes 10 built-in algorithms including XG-Boost, PCA, and others.



SageMaker is arguably the only Auto ML platform that provides transparency into its operations, (i.e. users can observe how SageMaker arrives at analyses using varied algorithms in training instances based on the provided framework and tools). They can tweak certain automated choices or even data sets to re-train across varied ML models for a more comprehensive ML model.

This is a substantial advantage of SageMaker — helping run multiple training instances based on the marginally altered datasets — when compared to most other ML platforms that also provide Auto ML functions, but do so without the same level of transparency.



Deployment on Amazon SageMaker is backed by managed hosting, auto-petabyte scaling, and accuracy tuning as well as built-in automatic A/B testing of ML models.

Companies using Amazon SageMaker

Zola | SoFi | Shelf | Transferwise

Amazon SageMaker Vs. Amazon Machine Learning: Similarities & Differences




SageMaker can be programmed to deploy ML models to specific API endpoints, a natural characteristic for Amazon Machine Learning that leans heavily on APIs right from its build phase.

Hyperparameter Tuning

Hyperparameter tuning is the task of auto-selecting the most suitable data processing formats as per data sets provided, algorithms to arrive at the analyses, and the deployment mode(s) as per user choices. Hyperparameters optimization or tuning is provided on both platforms.


Where SageMaker logs metrics on CloudWatch, Amazon Machine Learning logs on CloudTrail. The latter is comprehensively customizable to send all data and events to CloudWatch.



Ease of Use

Amazon ML is limited in its scope but is easier to use considering its tools and integrations are auto-selected on the platform to build around the provided data set and train with pre-set algorithms to arrive at an overall model.

SageMaker also automates selection across all ML phases, but it requires users to take a hands-on approach with coding and data science-based alterations in the event that users wish to conclusively utilize the capabilities of this platform.


Expertise Required

With Amazon ML, a well-perceived study of the available guide and walkthrough can be sufficient given that you already have some experience with Cloud-hosted APIs to run ML models and even deploy them using API integrations exported beyond the platform.

SageMaker requires both coding skills and substantial comprehension of data science methodologies beyond just a broad understanding of the algorithms available on the platform.


Scale of Usage

At Shamrock, we can initiate and deploy both Amazon ML and SageMaker models in time to help meet client requirements. However, the platform of choice will depend on the data set we are dealing with and the scale of end-user patterns to them.

Predictive Analysis Made Easier

Understanding user intent, financial patterns, cybersecurity threats, etc., is now not just possible with ML platforms, it’s trending towards standardization. Both Amazon ML and SageMaker can be deployed anywhere along with end-to-end AWS cloud infrastructure backup and at acceptable costs.

That said, using either of these platforms will demand certain expertise and experience, so be ready!

Shamrock Consulting Group has long been at the forefront of technological shifts and digital transformation strategies, and our expert team is constantly fine-tuning our collective knowledge base and deliverables to be more in tune with the latest industry trends and benchmarks to help you stay ahead of the competition.

We can help you use ML for predictive analysis to increase your business intelligence and better achieve your goals via updated sales forecasts, targeted marketing campaigns, empowered logistics, and more.

Our years of expertise using AWS services will help us optimize both Amazon Machine Learning and Amazon SageMaker as per your business’ scale or niche and assist you in optimizing your business prospects.

Ben Ferguson

Ben Ferguson

Ben Ferguson is the Vice President and Senior Network Architect for Shamrock Consulting Group, an industry leader in digital transformation solutions. Since his departure from Biochemical research in 2004, Ben has built core competencies around cloud direct connects and cloud cost reduction, enterprise wide area network architecture, high density data center deployments, cybersecurity and Voice over IP telephony. Ben has designed hundreds of complex networks for some of the largest companies in the world and he’s helped Shamrock become a top partner of the 3 largest public cloud platforms for AWS, Azure and GCP consulting. When he takes the occasional break from designing networks, he enjoys surfing, golf, working out, trying new restaurants and spending time with his wife, Linsey, his son, Weston and his dog, Hamilton.

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