Found this news item interesting:
Amazon’s own ‘Machine Learning University’ now available to all developers
by Dr. Matt Wood | on | in Amazon Machine Learning, Artificial Intelligence | Permalink | Comments | Share
Today, I’m excited to share that, for the first time, the same machine learning courses used to train engineers at Amazon are now available to all developers through AWS.
We’ve been using machine learning across Amazon for more than 20 years. With thousands of engineers focused on machine learning across the company, there are very few Amazon retail pages, products, fulfillment technologies, stores which haven’t been improved through the use of machine learning in one way or another. Many AWS customers share this enthusiasm, and our mission has been to take machine learning from something which had previously been only available to the largest, most well-funded technology companies, and put it in the hands of every developer. Thanks to services such as Amazon SageMaker, Amazon Rekognition, Amazon Comprehend, Amazon Transcribe, Amazon Polly, Amazon Translate, and Amazon Lex, tens of thousands of developers are already on their way to building more intelligent applications through machine learning.
Regardless of where they are in their machine learning journey, one question I hear frequently from customers is: “how can we accelerate the growth of machine learning skills in our teams?” These courses, available as part of a new AWS Training and Certification Machine Learning offering, are now part of my answer.
There are more than 30 self-service, self-paced digital courses with more than 45 hours of courses, videos, and labs for four key groups: developers, data scientists, data platform engineers, and business professionals. Each course starts with the fundamentals, and builds on those through real-world examples and labs, allowing developers to explore machine learning through some fun problems we have had to solve at Amazon. These include predicting gift wrapping eligibility, optimizing delivery routes, or predicting entertainment award nominations using data from IMDb (an Amazon subsidiary). Coursework helps consolidate best practices, and demonstrates how to get started on a range of AWS machine learning services, including Amazon SageMaker, AWS DeepLens, Amazon Rekognition, Amazon Lex, Amazon Polly, and Amazon Comprehend.
New AWS Certification for Machine Learning
To help developers demonstrate their knowledge (and to help employers hire more efficiently), we are also announcing the new “AWS Certified Machine Learning – Specialty” certification. Customers can take the exam now (and at half price for a limited time). Customers at re:Invent can sit for the exam this week at our Training and Certification exam sessions.
The digital courses are now available at no charge at aws.training/machinelearning and you only pay for the services you use in labs and exams during your training.Deserves a close look (notwithstanding the inappropriate marketing hype use of the word "University")..
Recall my prior post "Data Science?"
One AWS track, "Data Scientist":
A clip from a couple of the courses:
Curious omission of "finite / discrete math."
I bring most of that stuff to the table (though my calculus is by now a 3rd of a century old). AWS claims that the courses are available "at no charge," but I'm not seeing where you can get a deeper topical look (e.g., "syllabus") without "enrolling."
e.g., Linear and Logistical Regression:
Overview from enrollment page:
How do you make predictions in machine learning? Naumaan Nayyar, AWS Applied Scientist, will lead you through the key points—specifically, linear models for regression, least squares error, maximum likelihood estimate, regularization, logistic regression, empirical loss minimization, and gradient-based optimization methods.As an experienced stats/regression modeling analyst (large pdf **), I'll just comment that that's a lot of material to cover to competency in 8.5 hours. "Lead you through the key points..."
** From my 2003 bank white paper: "This paper documents the Custom Risk Score (CRS 2003) development process undertaken by the Risk Management Acquisitions Group Model Development Team, a rigorous analytical and modeling effort leading to the derivation of a revised and improved FNBM CRS metric set with which to effectively evaluate and segment new account applicants for optimal profitability consistent with effective management of credit risk. The study was performed in a manner consistent with statistical and credit industry best practices, and was analytically comprised of a systematic application of Factor Analysis, Cluster Analysis, and Logistic Regression methods applied to a broad array of internal and external (bureau) credit history attributes pertaining to a suitable sample of FNBM accounts.The resulting tool consists of a stratified "suite" of scorecards more closely calibrated to empirically evident incoming applicant clusters, and thus represents a significant improvement over its predecessor CRS 2001, which, while effective and statistically valid in its own right, was based on a portfolio-wide single-score logistic regression underwriting model..."You are not gonna get functionally up to speed on all of that kind of stuff in 8.5 hours.
Is this AWS offering basically a ML/"Data Science" "boot camp?" Will an AWS "certification" be of significant independent value?
The lense is over here, bro'.
More on "data science" and boot camps:
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| "Are Data Science Boot Camps Worth It?" |
OTHER STUFF
On the topic of tech talent, this book (along with several related other titles) is in my queue:
Nice podcast with the author:
50 minutes, well worth your time. Transcript here.I would imagine this would not be White House anti-immigration advisor Stephen Miller's favorite book. He breaks out in a rash upon hearing the word "global."
apropos of all of the foregoing, an interesting snip from the intriguing 2018 Annual Letter promulgated by SocialCapital.com.
"Because of the lack of distribution of talent (most of the top AI experts and new PhD’s unfortunately join Big Tech due to their professional-athlete level compensation packages), deep learning has barely scratched the surface of its potential applications. Over the next few decades, as more engineers are trained in artificial intelligence and as developer tools and frameworks get easier to use, we should see artificial intelligence successfully applied to problems that were previously too difficult using traditional software methods, such as self-driving cars, robotics, and drug design and discovery. AI can truly transform how we work, how we live, and even how we think." -- Chamath Palihapitiya, CEO'eh? In that regard, we'll have to keep considering Judea Pearl's pioneering AI work:
Some other books pending blog review:
There are more. Always.
UPDATE: INTERESTING AMAZON ERRATUM
From LinkedIn news updates.
Amazon says it’s marketing software that will allow doctors and hospitals to harvest patient medical records to enhance treatment and pare down costs. The move is the internet giant’s latest foray into medical care and follows recent acquisitions in the healthcare and pharmaceutical industries. Amazon will also develop an app that's embedded in electronic medical records and provides doctors direct links to products to market to patients. • Here’s what people are saying.JUST IN
Another "holy shit!" book. Another queue-jumper.
Twenty five leading lights of AI/ML etc. Fascinating. Hugely instructive.
UPDATE: I am a bit disappointed by the lack of any reference in the book to "computational biology." to wit,
Computational BiologyThink about the implications.
Computers, at the end of the day, are machines for turning information into processed information. This is obviously a very powerful and flexible capability, and our use cases for computers have expanded far beyond the “information processing” jobs they were initially tasked with. Yet, they can’t do everything. There are many different kinds of problems we can’t count on computers and software to solve. Many of them have one thing in common, which is that they aren’t problems about information. Instead, they’re problems about the physical world ("atoms vs. bits", as people like to say). Whether we need to build matter up (make materials, design drugs, process fuels), break things down (clean up pollution, treat disease) or identify and interact with things in the physical world (sense lead in water, sequester contaminants), there are a lot of hard problems in the world that computers cannot solve, but that biology can.
For many of these problems, the highest-potential path to fixing them lies in the overlap of computers and biology: computational biology. Computational biology is an emerging discipline that generally refers to two overlapping fields: 1) the practice of taking everything we’ve learned about how to build computers and applying that knowledge to building cells as a programmable, flexible, platform with which we can do useful work, and 2) productizing and automating the tools, processes, and methods we use in the lab to manipulate biology and build living systems. Although we’ve gone through a few waves of “biotech bubbles” over the past twenty years, this time may no longer only be about wildly speculative drug development, but instead about something more concrete and foundational. We'll be able to establish biological systems as engineered, all-purpose platforms that we can put to work the same way we do with computers.
Additionally, within a few years, we’ll reach a convergence point where our recent advances will start to overlap, and eventually blend, into our existing computing frameworks and infrastructure. This will have a profound and disruptive effect on many fields such as drug design and discovery, drug delivery, precision diagnostics and healthcare, engineered materials, ecology, agriculture, and much more. We’ll be able to work with biology in ways that increasingly resemble the way we work with software: as a platform for building tools, applications, and infrastructure. This time, however, we’ll be able to do it using living systems instead of code. — Chamath Palihapitiya, CEO, Social Capital, Oct. 2018 [pdf].
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More to come...
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