Machine Learning for All: The New AWS ML Stack


New innovations from Amazon mean the power of machine learning has never been simpler to deploy

Machine Learning (ML), along with artificial intelligence, has revolutionized the way today’s ever-increasing flow of relevant business and technical data is analyzed, understood, and used. However, the advantages of ML have never come easily or without substantial expense. If one doesn’t have the resources of a Fortune 100 organization, it can be prohibitive to bring the power of ML to a business. Amazon Web Services (AWS), the industry leader in cloud computing infrastructure and services, has found a way to begin changing all that.

The AWS ML Stack

With the release of the ML Stack, AWS brings the power of top-level machine learning capabilities to burgeoning SaaS and PaaS companies with unicorn aspirations but start-up budgets. Where in the past, taking full advantage of most ML capabilities required the services of data scientists and framework experts, these new ML Stack applications help level the playing field for up-and-coming platforms and applications.

Amazon Kendra democratizes enterprise search

Amazon Kendra brings powerful natural-language searches to websites and applications. Users can find the specific information they need as Kendra parses through the voluminous amounts of data spread across a company.

Users can use complex natural-language search inquiries instead of the standard single-keyword searches they have felt limited to in the past. Kendra can make all of a company’s data searchable by successfully accessing information from the multiplicity of databases, websites, and file systems it can access. With embedded ML, Kendra continuously improves search results as it becomes more familiar with the data it has at its disposal.

Amazon CodeGuru helps software developers optimize code

Designed to give coders the ability to make their code as quick and resource-aware as possible, Amazon CodeGuru is an ML service for application-performance measurement and automated code review. CodeGuru finds the most resource-taxing lines of code – the most expensive ones to run — and provides recommendations to optimize and fix the offending lines. CodeGuru helps find problem code quicker and easier, helping bring better software online sooner.

Employing ML lessons gleaned from hundreds of internal Amazon efforts and over 10,000 GitHub open-source projects, CodeGuru is already up to speed, and it’s only getting better with every line of code it helps optimize. As AWS says, “It’s like having a distinguished engineer on call, 24×7.”

Amazon Fraud Detector — seasoned working security for

Tens of billions of dollars are lost to online fraud each year. The threats are only getting more sophisticated as time goes on, so an effective defense requires ML capabilities to keep up. Like an experienced police officer, the application learns from each encounter with an incident of attempted fraud. So, Amazon Fraud Detector is always adding new information to its fraud-prevention rules to keep pace with always-inventive bad actors.

Just how experienced is this cop? This officer comes with over 20 years of fraud-detection experience from Also, teaching Amazon Fraud Detector is easy; you can create a fraud-detection model with no prior ML experience because the application does the hard detail work for you.

Fewer awkward doctor moments with Amazon Transcribe Medical

If there is one thing no patient likes, it’s feeling ignored by their physician during an office visit. In today’s highly regulated and cost-conscious managed-care medical environment, most doctors are put in the less-than-ideal position of having to type out their own notes while attending patients.

Amazon Transcribe Medical uses ML to quickly create accurate transcriptions of medical consultations between doctors and patients. Precise medical and pharmacological terms from dictated notes, live doctor/patient consultations, and even telemedicine sessions are converted to text.

Using natural-language processing and extremely accurate medical-industry-specific automatic speech recognition, data from these interactions is extracted for use in an electronic health record (ERH) system. It all frees up a doctor’s attention to focus on their patient and lets patients spend less time looking at the top of their doc’s head.

Amazon Augmented Artificial Intelligence facilitates the human review of ML predictions

Applications for machine learning often require humans to review predictions in low-confidence situations to make sure the results are correct. AWS offers one instance: “For example, extracting information from scanned mortgage application forms can require human review in some cases due to low-quality scans or poor handwriting.”

As you might imagine, building the infrastructure to make this kind of human review possible is incredibly complex. Traditionally, the difficulty has put these capabilities outside the reach of anything short of a top-tier enterprise, discouraging competition and limiting opportunities. Amazon Augmented AI (Amazon A2I) now makes this possible for more organizations than ever before.

AWS is bringing ML within reach; CloudHesive will put it in your hands

With the latest ML advances from Amazon and the implementation expertise of CloudHesive, your organization is much closer to the competitive advantages of AI and machine learning. Learn more by getting in touch with CloudHesive at 800-860-2040 or through our online contact form.

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