I am thrilled to release fast.ai’s newest free course, Computational Linear Algebra, including an online textbook and a series of videos, and covering applications (using Python) such as how to identify the foreground in a surveillance video, how to categorize documents, the algorithm powering Google’s search, how to reconstruct an image from a CT scan, and more.
Jeremy and I developed this material for a numerical linear algebra course we taught in the University of San Francisco’s Masters of Analytics program, and it is the first ever numerical linear algebra course, to our knowledge, to be completely centered around practical applications and to use cutting edge algorithms and tools, including PyTorch, Numba, and randomized SVD. It also covers foundational numerical linear algebra concepts such as floating point arithmetic, machine epsilon, singular value decomposition, eigen decomposition, and QR decomposition.
What is numerical linear algebra?
“What exactly is numerical linear algebra?” you may be wondering. It is all about getting computers to do matrix math with speed and with acceptable accuracy, and way more awesome than the somewhat dull name suggests. Who cares about how computers do matrix math? All of you, probably. Data science is largely about manipulating matrices since almost all data can be represented as a matrix: time-series, structured data, anything that fits in a spreadsheet or SQL database, images, and language (often represented as word embeddings).
A typical first linear algebra course focuses on how to solve matrix problems by hand, for instance, spending time using Gaussian Elimination with pencil and paper to solve a small system of equations manually. However, it turns out that the methods and concerns for solving larger matrix problems via a computer are often drastically different:
Speed: when you have big matrices, matrix computations can get very slow. There are different ways of addressing this:
Different algorithms: there may be a less intuitive way to solve the problem that still gets the right answer, and does it faster.
Vectorizing or parallelizing your code.
Locality: traditional runtime computations focus on Big O, the number of operations computed. However, for modern computing, moving data around in memory can be very time-consuming, and you need ways to minimize how much memory movement you do.
Accuracy: Computers represent numbers (which are continuous and infinite) in a discrete and finite way, which means that they have limited accuracy. Rounding errors can add up, particularly if you are iterating! Furthermore, some math problems are not very stable, meaning that if you vary the input a little, you get a vastly different output. This isn’t a problem with rounding or with the computer, but it can still have a big impact on your results.
Memory use: for matrices that have lots of zero entries, or that have a particular structure, there are more efficient ways to store these in memory.
Scalability: in many cases you are interested in working with more data than you have space in memory for.
This course also includes some very modern approaches that we don’t know of any other numerical linear algebra courses covering (and we have done a lot of researching of numerical linear algebra courses and syllabi), such as:
This approach is very different from how most math courses operate: typically, math courses first introduce all the separate components you will be using, and then you gradually build them into more complex structures. The problems with this are that students often lose motivation, don’t have a sense of the “big picture”, and don’t know which pieces they’ll even end up needing. We have been inspired by Harvard professor David Perkin’s baseball analogy. We don’t require kids to memorize all the rules of baseball and understand all the technical details before we let them have fun and play the game. Rather, they start playing with a just general sense of it, and then gradually learn more rules/details as time goes on. All that to say, don’t worry if you don’t understand everything at first! You’re not supposed to. We will start using some “black boxes” or matrix decompositions that haven’t yet been explained, and then we’ll dig into the lower level details later.
I love math (I even have a math PhD!), but when I’m trying to solve a practical problem, code is more useful than theory. Also, one of the tests of whether you truly understand something is if you can code it, so this course if much more code-centered than a typical numerical linear algebra course.
The primary resource for this course is the free online textbook of Jupyter Notebooks, available on Github. They are full of explanations, code samples, pictures, interesting links, and exercises for you to try. Anyone can view the notebooks online by clicking on the links in the readme Table of Contents. However, to really learn the material, you need to interactively run the code, which requires installing Anaconda on your computer (or an equivalent set up of the Python scientific libraries) and you will need to be able to clone or download the git repo.
Accompanying the notebooks is a playlist of lecture videos, available on YouTube. If you are ever confused by a lecture or it goes too quickly, check out the beginning of the next video, where I review concepts from the previous lecture, often explaining things from a new perspective or with different illustrations.
The algorithm behind Google’s PageRank, used to rank the relative importance of different web pages
Can’t I just use sci-kit learn?
Many (although certainly not all) of the algorithms covered in this course are already implemented in scientific Python libraries such as Numpy, Scipy, and Scikit Learn, so you may be wondering why it’s necessary to learn what’s going on underneath the hood. Knowing how these algorithms are implemented will allow you to better combine and utilize them, and will make it possible for you to customize them if needed. In at least one case, we show how to get a sizable speedup over sci-kit learn’s implementation of a method. Several of the topics we cover are areas of active research, and there is recent research that has not yet been added to existing libraries.
Q: My daughter loves math and art. She’s currently an 8th grader. My husband and I are not STEAM (Science, Technology, Engineering, Art, Math) people. I’d love to expose her to possible career options but am limited by my ignorance and perhaps my location. Do you have any suggestions for an intelligent, young person who is about to start her high school journey?
A: First, I am so glad you are encouraging your daughter’s interests! I have several recommendations and resources. This is a fantastic time in history to be a kid with an internet connection interested in math and art.
1. She should learn to code. In STEM, code is the language of creativity, and without knowing how to code, you are reliant on tools created by others. A good place to start is with blockly games, which teaches programming concepts (such as loops, variables, and logic) though a variety of mazes and puzzles. Blockly library was developed by the Google for Education team.
A note for parents of younger children: you might want to check out scratch (language for children developed by MIT Media Lab), snap (drag-and-drop programming language), or snap circuits (electronics kits).
3. A ton of exciting advances are happening in the maker space– people creating clothing that lights up, machines that 3d print pancakes, robots to move your Klein bottle collection around– and there are lots of resources available for all ages. Maker spaces are being added in libraries across the country, and can include anything from 3D Printers, littleBits, LEGO Robotics, Arduinos, Snap Circuits, design software, woodworking tools, jewelry making tools, paper crafting equipment, microscopes and other science gadgets, sewing machines, and more, and many offer workshops or classes. You can also see if there is a regional Maker Faire in your area.
One of the students from our fast.ai course bought several tons of legos on ebay and constructed a machine to automatically sort the legos (old bulk lego is sold more cheaply, but the resale value for sorted Lego is much higher and can be quite lucrative for certain pieces). I want children to know that adults do things like create interactive colorful light-up clothing for the keynote speech at a professional conference, or construct machines to sort Legos in their free time. Both of these examples are by experts, but you do not need to be an expert to work with hardware or program an arduino.
4. Encourage her to start a blog about what she is learning, creating, and exploring. I recently wrote a post (inspired by a question from a college student) encouraging everyone to blog, and I think the advice certainly holds for high schoolers. Many schools relegate writing to the humanities and social sciences, and don’t give students the practice of writing about math and technology. Being able to write and communicate technical ideas clearly is a super important and useful skill in today’s world (art can help with this too!). As I said previously, a blog is like a resume, only better. This holds true for high school students as well, and could be useful in landing internships. Check out this post for tips on how to get started.
You can checkout the zines by Amy W (an MIT computer science grad who hacks knitting machines) or Julia Evans (an infrastructure engineer at credit processing startup Stripe) for great examples of how cartoons and sketches can illuminate technical concepts. They are also two women I deeply admire!
6. Miscellaneous Groups and Resources. Although these are location specific, note that groups exist in a wide variety of places, not just in major tech hubs like San Francisco or New York City:
Iridescent Technovation: Through Technovation, teams of teenage girls around the world (from 78 different countries!) build mobile apps to solve problems in their communities, create business plans, and launch their solutions.
Black Girls Code: Introduces Black girls to coding and game design. They’ve reached over 3,000 students in cities such as Atlanta, Miama, LA, Dallas, Memphis, and others, and have plans to expand.
Blue 1647 offers a variety of programs including teaching youth to create web and mobile apps, Latina Girls Code, MineCraft Development bootcamps, programs for individuals with intellectual disabilities, and more. It has locations in Chicago, St. Louis, Compton, Indiana, Haiti, and LA.
7. There is a lovely essay called A Mathematician’s Lament written by Paul Lockhart, a former Brown University math professor who quit to teach K-12. He describes a nightmare world in which children are not allowed to sing songs or play instruments until they have spent over a decade studying music notation, transcribing sheet music by hand in different keys, and memorizing their circle of fifths. That sounds horrifying! Yet it is how math is taught in most schools– the focus is on dry notation, formal rules, memorization, and disconnected components, with the fun and creative parts saved until long after most students have dropped out.
I hope you can encourage your child to keep a sense of creativity, beauty, pattern, and play when approaching math. I know it can be difficult for children to maintain their curiosity and passion for subjects when adults or peers don’t understand their interests.
My daughter is still a toddler, so I haven’t gotten to experience this firsthand yet and I would love to hear from those of you who have! Also, a huge thanks to everyone who gave me suggestions for this article on Twitter.
This week’s Ask-A-Data-Scientist column has a question from a college freshman at my alma mater, Swarthmore. Please email your data science related quandaries to firstname.lastname@example.org. Note that questions are edited for clarity and brevity. Previous posts include:
Q: I’m currently a freshman at Swarthmore College and I’m really interested in machine learning and deep learning. I wanted to take Artificial Intelligence this semester; unfortunately, no freshmen got into the class as it has been difficult for the CS department to keep up with the huge spike in interest.
I’m currently taking Andrew Ng’s Coursera Course on Machine Learning and will finish it in ~2-3 weeks. Next, I was planning on taking your fast.ai MOOC, which I saw on hacker news.
I know you may be too busy, but can I ask you questions I have about ML and my proposed plan? How can I continue to learn machine learning after Ng’s Coursera course and fast.ai? It seems like the only two options are 1.) research and 2.) graduate level courses at UPenn (which seem to be quite difficult to get into from Swarthmore (especially as a first-year student)). Any advice would be appreciated.
A: In general, I am happy to answer questions, although it may take me some time (my inbox, oh my inbox). For technical questions, it’s best to first ask on our fast.ai forums. There are tons of interesting discussions on our forums, even if you are not taking our course. For career-related or general questions, I often answer them in my ask-a-data-scientist column.
Even without Swarthmore or UPenn’s AI classes, you will never run out of things to do with deep learning or ways to learn more. Our MOOC takes 70 hours of study to complete, and if you get interested in any of the Kaggle competitions we have you start, you could spend much longer. We will be releasing Part 2 in a few months, which will be a similar time commitment, only with even more side avenues for further study, recommended papers to read, and ways to extend the work.
Take the official classes when/if you are able, but you don’t need the credentials or resources from official classes (to anyone out there not in university or at a university that doesn’t offer an AI class, don’t worry: you don’t need them!). One of our students, who was an econ major with no graduate degree, was just accepted to the prestigious Google Brain residency program! Another student developed a new fraud detection technique based on material from our course and has received a bonus at his job. Several others have received internship and job offers, or switched teams in their current workplaces to more exciting machine learning projects.
Credentials can sometimes be useful to get your foot in the door, particularly if you are an underrepresented minority in tech (and thus facing greater scrutiny).
However, there are lots of even more effective ways to get your name and work out there:
Write a popular blog post (more on this below).
Create an interesting app and put it online.
Write helpful answers to others’ questions on the learn machine learning subreddit or on the fast.ai forums. Altruism is important to me, but that’s not why I recommend helping others. Explaining something you’ve learned to someone else is a key part of solidifying your own understanding.
Do your own experiments, and share the results via a blogpost or github. One of our students, Slav Ivanov, asked about using different optimizers for style transfer. Jeremy suggested he try it out, and Slav wrote an excellent blog post on what he found. This post was very popular on reddit and made Slav’s work more widely known.
Contribute to open source. Here, one of our students shares about his positive experience contributing to TensorFlow. With 3 lines of code, he reduced the binary size of TensorFlow on Android to less than 10MB!
In general, I recommend that you start a side project of something that interests you (that uses deep learning) so you will have that to work on.
Why you (yes, you) should blog
The top advice I would give my younger self would be to start blogging sooner. Here are some reasons to blog:
It’s like a resume, only better. I know of a few people who have had blog posts lead to job offers!
Helps you learn. Organizing knowledge always helps me synthesize my own ideas. One of the tests of whether you understand something is whether you can explain it to someone else. A blog post is a great way to do that.
I’ve gotten invitations to conferences and invitations to speak from my blog posts. I was invited to the TensorFlow Dev Summit (which was awesome!) for writing a blog post about how I don’t like TensorFlow.
Meet new people. I’ve met several people who have responded to blog posts I wrote.
Saves time. Any time you answer a question multiple times through email, you should turn it into a blog post, which makes it easier for you to share the next time someone asks.
To inspire you, here are some sample blog posts from students in part 2 of our course:
I enjoyed all of the above blog posts and also, I don’t think any of them are too intimidating. They’re meant to be accessible.
Tips for getting started blogging
Jeremy had been suggesting for years that I should start blogging, and I’d respond “I don’t have anything to say.” This wasn’t true. What I meant was that I didn’t feel confident, and I felt like the things I could write had already been written about by people with more expertise or better writing skills than me.
It turns out that is fine! Your posts don’t have to be earth-shattering or even novel to be read and shared. My writing skills were rather weak when I started (part of the reason I chose to study math and CS in college was because those courses requried the least amount of writing and also no labs), but my skills are improving with time.
Here are some more tips to help you start your first post:
Make a list of links to other blog posts, articles, or studies that you like, and write brief summaries or highlight what you particularly like about them. Part of my first blog post came from my making just such a list, because I couldn’t believe more people hadn’t read the posts and articles that I thought were awesome.
Summarize what you learned at a conference you attended, or in a class you are taking.
Any email you’ve written twice should be a blog post. Now, if I’m asked a question that I think someone else would also be interested in, I try to write it up.
Don’t be a perfectionist. I spent 9 months on my first blog post, it went viral, and I have repeatedly hit new lows in readership ever since then. One of my personal goals for 2017 is to post my writing quicker and not to obsess so much before I post, because it just builds up pressure and I end up writing less.
You are best positioned to help people one step behind you. The material is still fresh in your mind. Many experts have forgotten what it was like to be a beginner (or an intermediate) and have forgotten why the topic is hard to understand when you first hear it. The context of your particular background, your particular style, and your knowledge level will give a different twist to what you’re writing about.
If you are a woman in NYC, Chicago, or San Francisco, I recommend joining your local chapter of Write/Speak/Code, a group that encourages women software developers to write blog posts, speak at conferences, and contribute to open source.
Get angry. The catalyst that finally got me to start writing was when someone famous said something that made me angry. So angry that I had to explain all the ways his thinking was wrong.
If you’re wondering about the actual logistics, Medium makes it super simple to get started. Another option is to use Jekyll and Github pages. I can personally recommend both, as I have 2 blogs and use one for each.
You are on the right path by taking MOOCs, and by adding in a side project, involvement in online communities, and blogging you will have even more opportunities to learn and meet others!
This week’s Ask-A-Data-Scientist column answers two short questions from students. Please email your data science related quandaries to email@example.com. Note that questions are edited for clarity and brevity. Previous posts include:
Q1: I have a BS and MS in aerospace engineering and have been accepted to a data science bootcamp for this summer. I have been spending 15 hours/week on MIT’s 6.041 edx.org probability course, which is the hardest math course I’ve ever taken. I feel like my time could be better spent elsewhere. What about teaching myself the concepts as needed on the job? Or maybe you could recommend certain areas of probability to focus on? I’d like to tackle a personal project (either dealing with fitness tracker data or bitcoin) and maybe put probability on the backburner for a bit.
A: It sounds like you already know the answer to this one: yes! your time could be better spent elsewhere.
Let your coding projects motivate what you do, and learn math on an as needed basis. There are 3 reasons this is a good approach:
For most people, the best motivation will be letting the problems you’re working on motivate your learning.
The real test of whether you understand something is whether you can use it and build with it. So the projects you’re working on are needed to cement your understanding.
By learning on an as-needed basis, you study what you actually need, and don’t waste time on topics that may end up being irrelevant.
The only exceptions: if you want to be a math professor or work at a think tank (for most of my math phd, my goal was to become a math professor, so I see the appeal, but I was also totally unaware at the time of the breadth of awesome and exciting jobs that use math). And sometimes you need to brush up on math for white-boarding interviews.
Q2: I am currently pursuing a Master’s degree in Data Science. I am not that advanced in programming and new to most of the concepts of machine learning & statistics. Data science is such a vast field so most of my friends advise me to concentrate on a specific branch. Right now I am trying everything and becoming a jack in all and ace at none. How can I approach this to find a specialty?
A: There is nothing wrong with being a jack of all trades in data science; in some ways, that is what it means to be a data scientist. As long as you are spending the vast majority of your time writing code for practical projects, you are on the right track.
My top priorities of things to focus on for aspiring data scientists:
Focus on Python (including Numpy, Pandas, and Jupyter notebooks).
Try to focus on 1 main project. Extend something that you did in class. It can be difficult if you are mostly doing scattered problem sets in a variety of areas. For self-learners, one of the risks is jumping around too much and starting scattered tutorials across a range of sites, but never going deep enough with any one thing. Pick 1 Kaggle competition, personal project, or extension of a school project and stick with it. I can think of a few times I continued extended a class project for months after the class ended, because I was so absorbed in it. This is a great way to learn.
Start with decision tree ensembles (random forests and gradient boosting machines) on structured data sets. I have deeply conflicted feelings on this topic. While it’s possible to do these in Python using sklearn, I think R still handles structured datasets and categorical variables better. However, if you are only going to master one language, I think Python is the clear choice, and most people can’t focus on learning 2 new languages at the same time.
Then move on to deep learning using the Python library Keras. To quote Andrew Ng, deep learning is “the new electricity” and a very exciting, high impact area to be working in.
In terms of tips, there are a few things you can skip since they aren’t widely used in practice, such as support vector machines/kernel methods, Bayesian methods, and theoretical math (unless it’s explicitly necessary for a practical project you are working on).
Note that this answer is geared towards data scientists and not data engineers. Data engineers put algorithms into production and have a different set of skills, such as Spark and HDFS.
Here’s the latest installment of my Ask-A-Data-Scientist advice column. Please email your data science related quandaries to firstname.lastname@example.org. Note that questions are edited for clarity and brevity. Other posts include:
In the last week I received two questions with diametrically opposed premises: one was excited that machine learning is now automated, the other was concerned that machine learning takes too many years of study. Here are the questions:
Q1: I heard that Google Cloud announced that entrepreneurs can easily and quickly build on top of ML/NLP APIs. Is this statement true: “The future of ML and data post Google Cloud - the future is here, NLP and speech advancements have been figured out by Google and are accessible by API. The secret sauce has been commoditized so you can build your secret sauce on top of it. The time to secret sauce is getting shorter to shorter”?
Q2: Is it true that in order to work in machine learning, you need a PhD in the field? Is it true that before you can even begin studying machine learning, you must start by studying math, take “boring university level full length courses in calculus, linear algebra, and probability/statistics, and then learn C/C++ and parallel and distributed programming (CUDA, MPI, OpenMP, etc). According to this top rated comment on a Hacker News post, even after doing all that, we must then implement Machine Learning algorithms from scratch first in plain C, next in MPI or CUDA, and then in Numpy, before implementing them in Theano or TensorFlow.
A: It’s totally understandable that many people are having trouble navigating the hype, and the “AI is an exclusive tool for geniuses” warnings. AI is a hard topic for journalists to cover, and sadly many misrepresentations are spread. See for instance this great post for a recent case study of how DeepCoder was misconstrued in the media.
The answer to both of these questions is: NO. On the surface, they sound like opposite extremes. However, they have a common thread–many of those working in machine learning have an interest in either:
Convincing you to buy their general purpose machine learning API (none of which have been good for anything other than getting acqui-hired).
Convincing you that what they’re doing is so complicated, hard, and exclusive, that us mere mortals have no chance of understanding their sorcery. (This is such a common theme that recently a reddit parody of it was voted to the top of the machine learning page: A super harsh guide to machine learning)
Yes, advancements in machine learning are coming rapidly, but for now, you need to be able to code to effectively use the technology. We’ve found from our free online course Practical Deep Learning for Coders that it takes about 70 hours of study to become an effective deep learning practitioner.
Why “Machine Learning As A Service” (MLaaS) is such a disappointment in practice
A general purpose machine learning API seems like a great idea, but the technology is simply not there yet. Existing APIs are too overly specified to be widely useful, or attempt to be very general and have unacceptably poor performance. I agree with Bradford Cross, former founder of Flightcaster and Prismatic and partner at Data Collective VC, who recently wrote about the failure of many AI companies to try to build products that customers need and would pay for: “It’s the attitude that those working in and around AI are now responsible for shepherding all human progress just because we’re working on something that matters. This haze of hubris blinds people to the fact that they are stuck in an echo chamber where everyone is talking about the tech trend rather than the customer needs and the economics of the businesses.” (emphasis mine)
Cross continues, “Machine Learning as a Service is an idea we’ve been seeing for nearly 10 years and it’s been failing the whole time. The bottom line on why it doesn’t work: the people that know what they’re doing just use open source, and the people that don’t will not get anything to work, ever, even with APIs. Many very smart friends have fallen into this tarpit. Those who’ve been gobbled up by bigcos as a way to beef up ML teams include Alchemy API by IBM, Saffron by Intel, and Metamind by Salesforce. Nevertheless, the allure of easy money from sticking an ML model up behind an API function doesn’t fail to continue attracting lost souls. Amazon, Google, and Microsoft are all trying to sell an MLaaS layer as a component of their cloud strategy. I’ve yet to see startups or big companies use these APIs in the wild, and I see a lot of AI usage in the wild so its doubtful that its due to the small sample size of my observations.”
Is Google Cloud the answer?
Google is very poorly positioned to help democratize the field of deep learning. It’s not because of bad intentions– it’s just that they have way too many servers, way too much cash, and way too much data to appreciate the challenges the majority of the world faces in how to make the most of limited GPUs, on a limited budget (those AWS bills add up quickly!), and with limited size data sets. Google Brain is so deeply technical as to be out of touch with the average coder.
For instance, TensorFlow is a low level language, but Google seemed unaware of this when they released it and in how they marketed it. The designers of TensorFlow could have used a more standard Object-Oriented approach (like the excellent PyTorch), but instead they kept with the fine Google tradition of inventing new conventions just for Google.
The Hacker News plan: “Implement algorithms in plain C, then CUDA, and finally plain Numpy/MATLAB”
Why do Hacker News contributors regularly give such awful advice on machine learning? While the theory behind machine learning draws on a lot of advanced math, that is very different from the practical knowledge needed to use machine learning in practice. As a math PhD, knowing the math has been less helpful than you might expect in building practical, working models.
The line of thinking espoused in that Hacker News comment is harmful for a number of reasons:
It’s totally wrong
Good education motivates the study of underlying concepts. To borrow an analogy from Paul Lockhart’s Mathmatician’s Lament, kids would quit music if you made them study music theory for years before they were ever allowed to sing or touch an instrument
Good education doesn’t overly complicate the material. If you truly understand something, you can explain it in an accessible way. After weeks of work, in Practical Deep Learning for Coders, Jeremy Howard implemented different modern optimization techniques (often considered a complex topic) in Excel to make it clearer how they work.
As I wrote a few months ago, it is “far better to take a domain expert within your organization and teach them deep learning, than it is to take a deep learning expert and throw them into your organization. Deep learning PhD graduates are very unlikely to have the wide range of relevent experiences that you value in your most effective employees, and are much more likely to be interested in solving fun engineering problems, instead of keeping a razor-sharp focus on the most commercially important problems.
“In our experiences across many industries and many years of applying machine learning to a range of problems, we’ve consistently seen organizations under-appreciate and under invest in their existing in-house talent. In the days of the big data fad, this meant companies spent their money on external consultants. And in these days of the false ‘deep learning exclusivity’ meme, it means searching for those unicorn deep learning experts, often including paying vastly inflated sums for failing deep learning startups.”
Cutting through the hype (when you’re not an ML researcher)
Is there existing training data? If not, how do they plan on getting it?
Do they have an evaluation procedure built into their application development process?
Does their proposed application rely on unprecedentedly high performance on specific AI components?
Do the proposed solutions rely on attested, reliable phenomena?
If using pre-packaged AI components, do they have a clear plan on how they will go from using those components to having meaningful application output?
As an NLP researcher, Simonson is excited about the current advances in AI, but points out that the whole field is harmed when people exploit the gap in knowledge between practiotioners and the public.
Can this work unsupervised (= without labelling the examples)?
Can the system predict out of vocabulary names? (i.e. Imagine if I said “My friend Rudinyard was mean to me” - many AI systems would never be able to answer “Who was mean to me?” as Rudinyard is out of its vocabulary)
How much does the accuracy fall as the input story gets longer?
How stable is the model’s performance over time?
Merity also provides the reminder that models are often evaluated on highly processed, contrived, or limited datasets that don’t accurately reflect the real data you are working with.
What does this mean for you?
If you are an aspiring machine learning practitioner: Good news! You don’t need a PhD, you don’t need to code algorithms from scratch in CUDA or MPI. If you have a year of coding experience, we recommend that you try Practical Deep Learning for Coders, or consider my additional advice about how to become a data scientist.
You work in tech and want to build a business that uses ML: Good news! You don’t need to hire one of those highly elusive, highly expensive AI PhDs away from OpenAI. Give your coders the resources and time they need to get up to speed. Focus on a specific domain (together with experts from that domain) and build a product that people in that domain need and could use.