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
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
Companies using Amazon SageMaker
Zola | SoFi | Shelf | Transferwise
Amazon SageMaker Vs. Amazon Machine Learning: Similarities & Differences
Similarities
Differences
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.
