Kinh Nghiệm Hướng dẫn How did the Northern Renaissance differ from the Italian Renaissance quizlet? 2022
Lã Hiền Minh đang tìm kiếm từ khóa How did the Northern Renaissance differ from the Italian Renaissance quizlet? được Update vào lúc : 2022-12-17 22:46:08 . Với phương châm chia sẻ Bí kíp về trong nội dung bài viết một cách Chi Tiết Mới Nhất. Nếu sau khi tham khảo nội dung bài viết vẫn ko hiểu thì hoàn toàn có thể lại Comment ở cuối bài để Admin lý giải và hướng dẫn lại nha.The number of American households that were unbanked last year dropped to its lowest level since 2009, a dip due in part to people opening accounts to receive financial assistance during the pandemic, a new report says.
Nội dung chính Show- What does it mean to be unbanked?Who are the underbanked?Why are people unbanked or underbanked?Are some groups more likely to be unbanked? Will the number of unbanked rise if the U.S. has a recession? The decisionThe backgroundWhat’s nextFintech focusPenny Lee, Chief Executive Officer, Financial Technology AssociationAlex Marsh, Global Head of Policy, KlarnaTodd Denbo, Commercial Leader of Money & CEO of Intuit Financing, Inc., IntuitMahesh Kedia VP, GTM Strategy, New Market Entry and Revenue Operations, MarqetaKatherine Carroll, Global Head of Policy and Regulation, StripeTeddy Flo, Chief Legal Officer, Zest AIAndrew Gray, Partner, Morgan LewisJohn Pitts, Global Head of Policy PlaidMillions of modelsOpen or closedEngineering talent crunchHow did the Northern Renaissance different from the Italian Renaissance?How did the Northern Renaissance differ from the Italian Renaissance The Northern Renaissance was more secular?
Roughly 4.5% of U.S. households – or 5.9 million – didn't have a checking or savings account with a bank or credit union in 2022, a record low, according to the Federal Deposit Insurance Corporation's most recent survey of unbanked and underbanked households.
Roughly 45% of households that received a stimulus payment, jobless benefits or other government assistance after the start of the pandemic in March, 2022 said those funds helped compel them to open an account, according to the biennial report which has been conducted since 2009.
"Safe and affordable bank accounts provide a way to bring more Americans into the banking system and will continue to play an important role in advancing economic inclusion for all Americans,'' FDIC acting chairman Martin J. Gruenberg said in a statement.
A lack of banking options delayed some households from getting federal payments aimed helping the country weather the economic fallout from the COVID-19 health crisis.
Battle against predatory lending:Mississippi social justice firm fights payday 'predatory lending' in low-income communities
Checks arrived late for some of the unbanked:For 'unbanked' Americans, pandemic stimulus checks arrived slowly and with higher fees. But that could change.
The FDIC initiated an educational chiến dịch to get more Americans to open an account to enable the direct deposit of those funds. And banks such as Capital One and Ally Financial ended overdraft and other fees that have been a key barrier to some Americans accessing the banking system.
What does it mean to be unbanked?
A household is deemed unbanked when no one in the home has an account with a bank or credit union. That share of households has dropped by nearly half since 2009. And since 2011, when 8% of U.S. households were unbanked, the highest since the start of the survey, and the record low reached in 2022, roughly half of the drop was due to a shift in the financial circumstances of American households the FDIC says.
Who are the underbanked?
Financial technology is breaking down barriers to financial services and delivering value to consumers, small businesses, and the economy. Financial technology or “fintech” innovations use technology to transform traditional financial services, making them more accessible, lower-cost, and easier to use.
Fintech puts American consumers the center of their finances and helps them manage their money responsibly. From payment apps to budgeting and investing tools and alternative credit options, fintech makes it easier for consumers to pay for their purchases and build better financial habits.
Nearly half of fintech users say their finances are better due to fintech and save more than $50 a month on interest and fees. Fintech also arms small businesses with the financial tools for success, including low-cost banking services, digital accounting services, and expanded access to capital.
The Financial Technology Association represents the innovators shaping the future of finance, whether it’s streamlining online payments, expanding access to affordable credit, giving small businesses and creators the tools for success, or empowering everyday investors to build wealth. We advocate for modernized financial policies and regulations that allow fintech innovation to drive competition in the economy and expand consumer choice.
Join FTA’s inaugural Fintech Summit in partnership with Protocol on November 16 as we discuss these themes. Spots are still available for this hybrid sự kiện, and you can RSVP here to save your seat. Join us as we discuss how to shape the future of finance.
Alex Marsh, Global Head of Policy, Klarna
In its broadest sense, Open Banking has created a secure and connected ecosystem that has led to an explosion of new and innovative solutions that benefit the customer, rapidly revolutionizing not just the banking industry but the way all companies do business. Target benefits are delivered through speed, transparency, and security, and their impact can be seen across a diverse range of use cases.
Sharing financial data across providers can enable a customer (individual or business) to have real-time access to multiple bank accounts across multiple institutions all in one platform, saving time and helping consumers get a more accurate picture of their own finances before taking on debt, providing a more reliable indication than most lending guidelines currently do.
Open Banking can also widen the net of prospective lenders by providing an immediate and accurate understanding of a customer’s financial history, allowing more lenders to better understand the specific risk profile and hence drive a more competitive loan product for the end customer.
Companies can also create carefully refined marketing profiles and therefore, finely tune their services to the specific need. Open Banking platforms like Klarna Kosma also provide a unique opportunity for businesses to overlay additional tools that add real value for users and deepen their customer relationships.
The increased transparency brought about by Open Banking brings a vast array of additional benefits, such as helping fraud detection companies better monitor customer accounts and identify problems much earlier. The list of new value-add solutions continues to grow.
Todd Denbo, Commercial Leader of Money & CEO of Intuit Financing, Inc., Intuit
The speed of business has never been faster than it is today. For small business owners, time is a premium as they are wearing multiple hats every day. Macroeconomic challenges like inflation and supply chain issues are making successful money and cash flow management even more challenging. In fact, according to a recent Intuit QuickBooks survey, 99% of small businesses are concerned about inflation.
This presents a tremendous opportunity that innovation in fintech can solve by speeding up money movement, increasing access to capital, and making it easier to manage business operations in a central place. Fintech offers innovative products and services where outdated practices and processes offer limited options.
For example, fintech is enabling increased access to capital for business owners from diverse and varying backgrounds by leveraging alternative data to evaluate creditworthiness and risk models. This can positively impact all types of business owners, but especially those underserved by traditional financial service models.
When we look across the Intuit QuickBooks platform and the overall fintech ecosystem, we see a variety of innovations fueled by AI and data science that are helping small businesses succeed. By efficiently embedding and connecting financial services like banking, payments, and lending to help small businesses, we can reinvent how SMBs get paid and enable greater access to the vital funds they need critical points in their journey.
Overall, we see fintech as empowering people who have been left behind by antiquated financial systems, giving them real-time insights, tips, and tools they need to turn their financial dreams into a reality.
Mahesh Kedia VP, GTM Strategy, New Market Entry and Revenue Operations, Marqeta
Innovations in payments and financial technologies have helped transform daily life for millions of people. Despite these technological advances, 22% of American adults fall in the unbanked or underbanked category (source: Federal Reserve). People who are unbanked often rely on more expensive alternative financial products (AFPs) such as payday loans, money orders, and other expensive credit facilities that typically charge higher fees and interest rates, making it more likely that people have to dip into their savings to stay afloat. Now that more of the under/unbanked population has access to web-enabled smartphones, there are many advances in fintech that can help them access banking services. A few examples include:
Mobile wallets - The unbanked may not have traditional bank accounts but can have verified mobile wallet accounts for shopping and bill payments. Their mobile wallet identity can be used to open a virtual bank account for secure and convenient online banking.
Minimal to no-fee banking services - Fintech companies typically have much lower acquisition and operating costs than traditional financial institutions. They are then able to pass on these savings in the form of no-fee or no-minimum-balance products to their customers.
Help building credit - Some fintech companies provide a credit line to the under/unbanked against a portion of their personal savings, allowing them to build a credit history over time.This enables immigrants and other populations that may be underbanked to move up the credit lifecycle to get additional forms of credit such as auto, home and education loans, etc.
By providing access to banking services such as fee-không lấy phí savings and checking accounts, remittances, credit services, and mobile payments, fintech companies can help the under/unbanked population to achieve greater financial stability and wellbeing.
Katherine Carroll, Global Head of Policy and Regulation, Stripe
Entrepreneurs from every background, in every part of the world, should be empowered to start and scale global businesses.
Most businesses still face daunting challenges with very basic matters. Incorporation. Tax. Payments. These are still very manually intensive processes, and they are barriers to entrepreneurship in the form of paperwork, PDFs, faxes, and forms. Stripe is working to solve these rather mundane and boring challenges, almost always with an application programming interface that simplifies complex processes into a few clicks.
Whether it’s making it easy for businesses to accept payments from around the world, helping anyone, anywhere incorporate correctly in a matter of hours, or tailoring loans to businesses’ needs, Stripe services are making it possible for businesses of all sizes to use the tools that formerly were reserved for big companies in big cities. Of the companies that incorporated using Stripe, 92% are outside of Silicon Valley; 28% of founders identify as a minority; 43% are first-time entrepreneurs. Stripe powers nearly half a million businesses in rural America. Collectively, they outpace urban business revenue by 30%.
The internet economy is just beginning to make a real difference for businesses of all sizes in all kinds of places. We are excited about this future.
Teddy Flo, Chief Legal Officer, Zest AI
What I believe is most important — and what we have honed in on Zest AI — is the fact that you can’t change anything for the better if equitable access to capital isn't available for everyone. The way we make decisions on credit should be fair and inclusive and done in a way that takes into account a greater picture of a person. Lenders can better serve their borrowers with more data and better math. Zest AI has successfully built a compliant, consistent, and equitable AI-automated underwriting technology that lenders can utilize to help make their credit decisions. Through Zest AI, lenders can score underbanked borrowers that traditional scoring systems would deem as “unscorable.” We’ve proven that lenders can dig into their lower credit tier borrowers and lend to them without changing their risk tolerance.
Andrew Gray, Partner, Morgan Lewis
While artificial intelligence (AI) systems have been a tool historically used by sophisticated investors to maximize their returns, newer and more advanced AI systems will be the key innovation to democratize access to financial systems in the future. Despite privacy, ethics, and bias issues that remain to be resolved with AI systems, the good news is that as larger datasets become progressively easier to interconnect, AI and related natural language processing (NLP) technology innovations are increasingly able to equalize access. The even better news is that this democratization is taking multiple forms.
AI can be used to provide risk assessments necessary to bank those under-served or denied access. AI systems can also retrieve troves of data not used in traditional credit reports, including personal cash flow, payment applications usage, on-time utility payments, and other data buried within large datasets, to create fair and more accurate risk assessments essential to obtain credit and other financial services. By expanding credit availability to historically underserved communities, AI enables them to gain credit and build wealth.
Additionally, personalized portfolio management will become available to more people with the implementation and advancement of AI. Sophisticated financial advice and routine oversight, typically reserved for traditional investors, will allow individuals, including marginalized and low-income people, to maximize the value of their financial portfolios. Moreover, when coupled with NLP technologies, even greater democratization can result as inexperienced investors can interact with AI systems in plain English, while providing an easier interface to financial markets than existing execution tools.
John Pitts, Global Head of Policy Plaid
Open finance technology enables millions of people to use the apps and services that they rely on to manage their financial lives – from overdraft protection, to money management, investing for retirement, or building credit. More than 8 in 10 Americans are now using digital finance tools powered by open finance. This is because consumers see something they like or want – a new choice, more options, or lower costs.
What is open finance? At its core, it is about putting consumers in control of their own data and allowing them to use it to get a better giảm giá.
When people can easily switch to another company and bring their financial history with them, that presents real competition to legacy services and forces everyone to improve, with positive results for consumers. For example, we see the impact this is having on large players being forced to drop overdraft fees or to compete to deliver products consumers want.
We see the benefits of open finance first hand Plaid, as we support thousands of companies, from the biggest fintechs, to startups, to large and small banks. All are building products that depend on one thing - consumers' ability to securely share their data to use different services.
Open finance has supported more inclusive, competitive financial systems for consumers and small businesses in the U.S. and across the globe – and there is room to do much more. As an example, the National Consumer Law Consumer recently put out a new report that looked consumers providing access to their bank account data so their rent payments could inform their mortgage underwriting and help build credit. This is part of the promise of open finance.
At Plaid, we believe a consumer should have a right to their own data, and agency over that data, no matter where it sits. The CFPB's recent kick off of its 1033 rulemaking was particularly encouraging as is the agency’s commitment to strong consumer data rights and emphasis on promoting competition. This will be essential to securing benefits of open finance for consumers for many years to come.
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AWS is gearing up for re:Invent, its annual cloud computing conference where announcements this year are expected to focus on its end-to-end data strategy and delivering new industry-specific services.
It will be the second re:Invent with CEO Adam Selipsky as leader of the industry’s largest cloud provider after his return last year to AWS from data visualization company Tableau Software.
“What we're really trying to do is to look that end-to-end journey of data and to build really compelling, powerful capabilities and services each stop in that data journey and then…knit all that together with strong concepts like governance,” Selipsky told Protocol in a recent interview in Boston.
AWS now has more than 200 services, and Selispky said it’s not done building.
“I don't know when we ever will be,” he said. “We continue to both release new services because customers need them and they ask us for them and, the same time, we've put tremendous effort into adding new capabilities inside of the existing services that we've already built. Both prongs of that are important.”
But cost-cutting is a reality for many customers given the worldwide economic turmoil, and AWS has seen an increase in customers looking to control their cloud spending.
“Some customers are doing some belt-tightening,” Selipsky said. “What we see a lot of is folks just being really focused on optimizing their resources, making sure that they're shutting down resources which they're not consuming. By the way, they should be doing that all the time. The motivation's just a little bit higher in the current economic situation.”
This interview has been edited and condensed for clarity. Read Protocol’s other story based on our interview with Selipsky here.
Besides the sheer growth of AWS, what do you think has changed the most while you were Tableau? Were you surprised by anything?
The number of customers who are now deeply deployed on AWS, deployed in the cloud, in a way that's fundamental to their business and fundamental to their success surprised me. You can see it on paper and say, “Oh, the business has grown bigger, and that must mean there are more customers,” but the cloud and our relationship with these enterprises is now very much a C-suite agenda.
There was a time years ago where there were not that many enterprise CEOs who were well-versed in the cloud. Then you reached the stage where they knew they had to have a cloud strategy, and they were…asking their teams, their CIOs, “okay, do we have a cloud strategy?” Now, it's actually something that they're, in many cases, steeped in and involved in, and driving personally.
That’s just indicative of how much so many organizations are using the cloud now in every facet of their business — to run their core IT enterprise applications, of course, to do all sorts of new analytics, many of which involve machine learning now that there were never possible before, and then many, many end-customer applications as well.
It's not just about deploying technology. The conversation that I most end up having with CEOs is about organizational transformation. It is about how they can put data the center of their decision-making in a way that most organizations have never actually done in their history. And it's about using the cloud to innovate more quickly and to drive speed into their organizations. Those are cultural characteristics, not technology characteristics, and those have organizational implications about how they organize and what teams they need to have. It turns out that while the technology is sophisticated, deploying the technology is arguably the lesser challenge compared with how do you mold and shape the organization to best take advantage of all the benefits that the cloud is providing.
How has your experience Tableau affected AWS and how you think about putting your stamp on AWS?
I, personally, have just spent almost five years deeply immersed in the world of data and analytics and business intelligence, and hopefully I learned something during that time about those topics. I'm able to bring back a real insider's view, if you will, about where that world is heading — data, analytics, databases, machine learning, and how all those things come together, and how you really need to view what's happening with data as an end-to-end story. It's not about having a point solution for a database or an analytic service, it's really about understanding the flow of data from when it comes into your organization all the way through the other end, where people are collaborating and sharing and making decisions based on that data. AWS has tremendous resources devoted in all these areas.
Can you talk about the intersection of data and machine learning and how you see that playing out in the next couple of years?
What we're seeing is three areas really coming together: You've got databases, analytics capabilities, and machine learning, and it's sort of like a Venn diagram with a partial overlap of those three circles. There are areas of each which are arguably still independent from each other, but there's a very large and a very powerful intersection of the three — to the point where we've actually organized inside of AWS around that and have a single leader for all of those areas to really help bring those together.
There's so much data in the world, and the amount of it continues to explode. We were saying that five years ago, and it's even more true today. The rate of growth is only accelerating. It's a huge opportunity and a huge problem. A lot of people are drowning in their data and don't know how to use it to make decisions. Other organizations have figured out how to use these very powerful technologies to really gain insights rapidly from their data.
What we're really trying to do is to look that end-to-end journey of data and to build really compelling, powerful capabilities and services each stop in that data journey and then…knit all that together with strong concepts like governance. By putting good governance in place about who has access to what data and where you want to be careful within those guardrails that you set up, you can then set people không lấy phí to be creative and to explore all the data that's available to them.
AWS has more than 200 services now. Have you hit the peak for that or can you sustain that growth?
We're not done building yet, and I don't know when we ever will be. We continue to both release new services because customers need them and they ask us for them and, the same time, we've put tremendous effort into adding new capabilities inside of the existing services that we've already built. Both prongs of that are important.
We don't just build a service and move on. Inside of each of our services – you can pick any example – we're just adding new capabilities all the time. One of our focuses now is to make sure that we're really helping customers to connect and integrate between our different services. So those kinds of capabilities — both building new services, deepening our feature set within existing services, and integrating across our services – are all really important areas that we'll continue to invest in.
Do customers still want those fundamental building blocks and to piece them together themselves, or do they just want AWS to take care of all that?
There's no one-size-fits-all solution to what customers want. We absolutely have customers who very much want to have their hands “on the wheel,” if you will, and to be working with our services the the deepest layer, the most primitive level — so EC2 for compute, S3 for storage, one or more of our database services — and they want to be interacting with those services directly.
It is interesting, and I will say somewhat surprising to me, how much basic capabilities, such as price performance of compute, are still absolutely vital to our customers. If you'd asked me 15 years ago, “hey in 2022, how much of the cutting edge of innovation do you think would be around raw performance or price performance of a unit of compute,” I wouldn't have necessarily guessed that was still as important as it is. But it's absolutely vital. Part of that is because of the size of datasets and because of the machine learning capabilities which are now being created. They require vast amounts of compute, but nobody will be able to do that compute unless we keep dramatically improving the price performance.
We (also) absolutely have more and more customers who want to interact with AWS a higher level of abstraction…more the application layer or broader solutions, and we're putting a lot of energy, a lot of resources, into a number of higher-level solutions. One of the biggest of those … is Amazon Connect, which is our contact center solution. In minutes or hours or days, you can be up and running with a contact center in the cloud. At the beginning of the pandemic, Barclays … sent all their agents home. In something like 10 days, they got 6,000 agents up and running on Amazon Connect so they could continue servicing their end customers with customer service. We've built a lot of sophisticated capabilities that are machine learning-based inside of Connect. We can do call transcription, so that supervisors can help with training agents and services that extract meaning and themes out of those calls. We don't talk about the primitive capabilities that power that, we just talk about the capabilities to transcribe calls and to extract meaning from the calls. It's really important that we provide solutions for customers all levels of the stack.
Given the economic challenges that customers are facing, how is AWS ensuring that enterprises are getting better returns on their cloud investments?
Now's the time to lean into the cloud more than ever, precisely because of the uncertainty. We saw it during the pandemic in early 2022, and we're seeing it again now, which is, the benefits of the cloud only magnify in times of uncertainty.
For example, the one thing which many companies do in challenging economic times is to cut capital expense. For most companies, the cloud represents operating expense, not capital expense. You're not buying servers, you're basically paying per unit of time or unit of storage. That provides tremendous flexibility for many companies who just don't have the CapEx in their budgets to still be able to get important, innovation-driving projects done.
Another huge benefit of the cloud is the flexibility that it provides — the elasticity, the ability to dramatically raise or dramatically shrink the amount of resources that are consumed. In the first six months of the pandemic, Zoom's demand went up about 300%, and they were able to seamlessly and gracefully fulfill that demand because they're using AWS. You can only imagine if a company was in their own data centers, how hard that would have been to grow that quickly. The ability to dramatically grow or dramatically shrink your IT spend essentially is a unique feature of the cloud.
These kinds of challenging times are exactly when you want to prepare yourself to be the innovators … to reinvigorate and reinvest and drive growth forward again. We've seen so many customers who have prepared themselves, are using AWS, and then when a challenge hits, are actually able to accelerate because they've got competitors who are not as prepared, or there's a new opportunity that they spot. We see a lot of customers actually leaning into their cloud journeys during these uncertain economic times.
During Amazon’s Oct. 27 earnings call, it was noted there was an uptick in AWS customers wanting to cut costs, and Amazon’s CFO said customers were looking to save versus their committed spend. Do you still push multi-year contracts, and when there's times like this, do customers have the ability to renegotiate?
We're an $82-billion-a-year company last quarter, growing 27% year over year, so we have, of course, every use case and customers in every situation that you could imagine. Many are rapidly accelerating their journey to the cloud. Some customers are doing some belt-tightening. What we see a lot of is folks just being really focused on optimizing their resources, making sure that they're shutting down resources which they're not consuming. By the way, they should be doing that all the time. The motivation's just a little bit higher in the current economic situation. You do see some discretionary projects which are being not canceled, but pushed out.
But every customer is welcome to purely “pay by the drink” and to use our services completely on demand. Every customer is không lấy phí to make that choice. But of course, many of our larger customers want to make longer-term commitments, want to have a deeper relationship with us, want the economics that come with that commitment. We're signing more long-term commitments than ever these days.
AWS’ margins took a hit this past quarter, but do you think its margins in general are kind of fat?
We provide incredible value for our customers, which is what they care about. There have been analyst reports done showing that…for typical enterprise workloads that move over, customers save an average of 30% running those workloads in AWS compared to running them by themselves.
(Australian airline) Qantas, for example, is using AWS to do advanced analytics on flight paths — fuel-efficient flight paths, given wind conditions and what their flight paths should be — and they actually project they're going to save $40 million a year, in addition to…lowering their carbon footprint through better fuel efficiency. That kind of analysis would not be feasible, you wouldn't even be able to do that for most companies, on their own premises. So some of these workloads just become better, become very powerful cost-savings mechanisms, really only possible with advanced analytics that you can run in the cloud.
In other cases, just the fact that we have things like our Graviton processors and … run such large capabilities across multiple customers, our use of resources is so much more efficient than others. We are of significant enough scale that we, of course, have good purchasing economics of things like bandwidth and energy and so forth. So, in general, there's significant cost savings by running on AWS, and that's what our customers are focused on.
The margins of our business are going to … fluctuate up and down quarter to quarter. It will depend on what capital projects we've spent on that quarter. Obviously, energy prices are high the moment, and so there are some quarters that are puts, other quarters there are takes.
The important thing for our customers is the value we provide them compared to what they're used to. And those benefits have been dramatic for years, as evidenced by the customers' adoption of AWS and the fact that we're still growing the rate we are given the size business that we are. That adoption speaks louder than any other voice.
Do you anticipate a higher percentage of customer workloads moving back on premises than you maybe would have three years ago?
Absolutely not. We're a big enough business, if you asked me have you ever seen X, I could probably find one of anything, but the absolute dominant trend is customers dramatically accelerating their move to the cloud. Moving internal enterprise IT workloads like SAP to the cloud, that's a big trend. Creating new analytics capabilities that many times didn't even exist before and running those in the cloud. More startups than ever are building innovative new businesses in AWS. Our public-sector business continues to grow, serving both federal as well as state and local and educational institutions around the world. Only … in the vicinity of 10% of IT has moved to the cloud. It really is still day one. The opportunity is still very much in front of us, very much in front of our customers, and they continue to see that opportunity and to move rapidly to the cloud.
Do you ever see a cloud environment where customers could easily run say your machine learning services and Google's data offerings and Microsoft’s X offerings as one big tech stack easily?
In general, when we look across our worldwide customer base, we see time after time that the most innovation and the most efficient cost structure happens when customers choose one provider, when they're running predominantly on AWS. A lot of benefits of scale for our customers, including the expertise that they develop on learning one stack and really getting expert, rather than dividing up their expertise and having to go back to basics on the next parallel stack.
That being said, many customers are in a hybrid state, where they run IT in different environments. In some cases, that's by choice; in other cases, it's due to acquisitions, like buying companies and inherited technology. We understand and embrace the fact that it's a messy world in IT, and that many of our customers for years are going to have some of their resources on premises, some on AWS. Some may have resources that run in other clouds. We want to make that entire hybrid environment as easy and as powerful for customers as possible, so we've actually invested and continue to invest very heavily in these hybrid capabilities.
For example, in the management capabilities, that’s the first thing that customers ask for: “We want to be able to see and have visibility into and, in some cases, manage resources on AWS, on my own premises and, in some cases, on other clouds.” So we've built capabilities, many of our management services, to see and, in some cases, control what's going on across those environments.
A lot of customers are using containerized workloads now, and one of the big container technologies is Kubernetes. We have a managed Kubernetes service, Elastic Kubernetes Service, and we have a … distribution of Kubernetes (Amazon EKS Distro) that customers can take and run on their own premises and even use to boot up resources in another public cloud and have all that be done in a consistent fashion and be able to observe and manage across all those environments. So we're very committed to providing hybrid capabilities, including running on premises, including running in other clouds, and making the world as easy and as cost-efficient as possible for customers.
Can you talk about why you brought Dilip Kumar, who was Amazon's vice president of physical retail and tech, into AWS as vice president applications and how that will play out?
He's a longtime, tenured Amazonian with many, many different roles – important roles – in the company over a many-year period. Dilip has come over to AWS to report directly to me, running an applications group. We do have more and more customers who want to interact with the cloud a higher level – higher up the stack or more on the application layer.
We talked about Connect, our contact center solution, and we've also built services specifically for the healthcare industry like a data lake for healthcare records called (Amazon) HealthLake. We've built a lot of industrial services like IoT services for industrial settings, for example, to monitor industrial equipment to understand when it needs preventive maintenance. We have a lot of capabilities we're building that are either for … horizontal use cases like (Amazon Connect) or industry verticals like automotive, healthcare, financial services. We see more and more demand for those, and Dilip has come in to really coalesce a lot of teams' capabilities, who will be focusing on those (areas). You can expect to see us invest significantly in those areas and to come out with some really exciting innovations.
Would that include going into CRM or ERP or other higher-level, run-your-business applications?
I don't think we have immediate plans in those particular areas, but as we've always said, we're going to be completely guided by our customers, and we'll go where our customers tell us it's most important to go next. It's always been our north star.
Correction: This story was updated Nov. 18, 2022, to correct the name of Amazon EKS Distro.
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We launched Protocol in February 2022 to cover the evolving power center of tech. It is with deep sadness that just under three years later, we are winding down the publication.
As of today, we will not publish any more stories. All of our newsletters, apart from our flagship, Source Code, will no longer be sent. Source Code will be published and sent for the next few weeks, but it will also close down in December.
Building this publication has not been easy; as with any small startup organization, it has often been chaotic. But it has also been hugely fulfilling for those involved. We could not be prouder of, or more grateful to, the team we have assembled here over the last three years to build the publication. They are an inspirational group of people who have gone above and beyond, week after week. Today, we thank them deeply for all the work they have done.
We also thank you, our readers, for subscribing to our newsletters and reading our stories. We hope you have enjoyed our work.
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On any given day, Lily AI runs hundreds of machine learning models using computer vision and natural language processing that are customized for its retail and ecommerce clients to make website product recommendations, forecast demand, and plan merchandising. But this spring when the company was in the market for a machine learning operations platform to manage its expanding model roster, it wasn’t easy to find a suitable off-the-shelf system that could handle such a large number of models in deployment while also meeting other criteria.
Some MLops platforms are not well-suited for maintaining even more than 10 machine learning models when it comes to keeping track of data, navigating their user interfaces, or reporting capabilities, Matthew Nokleby, machine learning manager for Lily AI’s product intelligence team, told Protocol earlier this year. “The duct tape starts to show,” he said.
Nokleby, who has since left the company, said that for a long time Lily AI got by using a homegrown system, but that wasn’t cutting it anymore. And he said that while some MLops systems can manage a larger number of models, they might not have desired features such as robust data visualization capabilities or the ability to work on premises rather than in cloud environments.
As for finding an MLops platform that works for the company, Lily AI’s CTO and co-founder Sowmiya Chocka Narayanan said last week, "We're still looking.”
As companies expand their use of AI beyond running just a few ML models, and as larger enterprises go from deploying hundreds of models to thousands and even millions of models, many machine learning practitioners Protocol interviewed for this story say that they have yet to find what they need from prepackaged MLops systems.
“That is the biggest gap in the tech industry right now,” said Nicola Morini Bianzino, global chief client technology officer EY. The auditing firm has thousands of models in deployment that are used for its customers’ tax returns and other purposes, but has not come across a suitable system for managing various MLops modules, he said.
“I’m actually surprised that none of the big companies have jumped in this space because the opportunity is massive,” Morini Bianzino said.
Depending on how it is defined, projections for the global MLops platform market vary from $3 billion by 2027 to $4 billion by 2025 to $6 billion by 2028. Companies hawking MLops platforms for building and managing machine learning models include tech giants like Amazon, Google, Microsoft, and IBM and lesser-known vendors such as Comet, Cloudera, DataRobot, and Domino Data Lab.
Although the MLops-related platforms available today are “extremely valuable,” said Danny Lange, vice president of AI and machine learning gaming and automotive AI company Unity Technologies, “nobody right now is doing it a level that you ideally want. It's actually a complex problem.” Right now, Unity is using a custom-built system to manage the thousands of ML models it has in deployment, Lange said
Millions of models
Like other large enterprises that have invested in ML for years, Southeast Asia’s banking giant DBS has had to build in-house to manage its data analytics and the 400-plus ML models it runs for things like personalized banking, said Sameer Gupta, group chief analytics officer and managing director.
“When DBS started our journey several years ago, the solutions available in the market primarily focused more on AI/ML activities as experiments and did not meet our requirements to iterate and operationalize quickly,” Gupta told Protocol.
“We had to leverage what was available to develop our in-house capabilities that allows us to better tailor our solutions across the bank.” The company erected its own internal analytics and AI platform, which features an operational cluster to manage data ingestion, computation, storage, and model production, as well as an analytical cluster for data scientists to experiment and develop new tools before they go into production.
Intuit also has constructed its own systems for building and monitoring the immense number of ML models it has in production, including models that are customized for each of its QuickBooks software customers. Sometimes the distinctions in each model are minimal — one company might label certain types of purchases as “office supplies” while another categorizes them with the name of their office retailer of choice, for instance. The model must recognize those distinctions.
“We actually build models that are personalized to each [customer],” said Diane Chang, director of data science Intuit. “When you look that, each of those individual models that we built, then we’re over millions.”
Intuit had MLops systems in place before a lot of vendors sold products for managing machine learning, said Brett Hollman, Intuit’s director of engineering and product development in machine learning.
For instance, Hollman said the company built an ML feature management platform from the ground up. “A set of features can help you train a new model. If somebody generates good features on cash flow, some other person that’s doing some other cash flow thing might come along and say, ‘Oh, well, this feature set actually fits my use case.’ We're trying to promote reuse,” he said.
Open or closed
For companies that have been forced to go DIY, building these platforms themselves does not always require forging parts from raw materials. DBS has incorporated open-source tools for coding and application security purposes such as Nexus, Jenkins, Bitbucket, and Confluence to ensure the smooth integration and delivery of ML models, Gupta said.
Intuit has also used open-source tools or components sold by vendors to improve existing in-house systems or solve a particular problem, Hollman said. However, he emphasized the need to be selective about which route to take.
“A vendor may not have all the capabilities [we] need. Looking an open-source solution and extending an open-source solution might be a better way of approaching that particular component versus going with a vendor,” he said. “If you go with a vendor, you drive their road map, you work with them and drive their road map, but you’re dependent upon their road map versus your own internal software development lifecycle.”
The age-old “build or buy” question is the wrong one to ask, said Zoe Hillenmeyer, chief commercial officer Peak, which sells an AI decision intelligence platform and related services. When it comes to MLops, she said, “There’s a false dichotomy between build versus buy. That’s an incorrect strategy. I think that the best AI will be a build plus buy.”
If you go with a vendor, you drive their road map, you work with them and drive their road map, but you’re dependent upon their road map versus your own internal software development lifecycle.”
However, creating consistency through the ML lifecycle from model training to deployment to monitoring becomes increasingly difficult as companies cobble together open-source or vendor-built machine learning components, said John Thomas, vice president and distinguished engineer IBM.
“The enterprise might try to force everyone to use a single development platform. The reality is most people are not there, so you have a whole bunch of different tools. People fight over it — it’s a religious thing,” Thomas said.
IBM has responded to that reality by allowing clients to use its MLops pipelines in conjunction with non-IBM technology, an approach that Thomas said is “new” for IBM.
Engineering talent crunch
Companies struggling to find suitable off-the-shelf MLops platforms are up against another major challenge, too: finding engineering talent.
Many companies do not have software engineers on staff with the level of expertise necessary to architect systems that can handle large numbers of models or accommodate millions of split-second decision requests, said Abhishek Gupta, founder and principal researcher Montreal AI Ethics Institute and senior responsible AI leader and expert Boston Consulting Group.
“A lot of these places that are attempting to do this are just not tech-native or tech-first companies,” BCG’s Gupta said. For one thing, smaller companies are competing for talent against big tech firms that offer higher salaries and better resources. “There is a lack of technical talent to a significant degree that hinders the implementation of scalable MLops systems because that knowledge is locked up in those tech-first firms,” he said.
Despite the obstacles, Intuit’s Hollman said it makes sense for companies that have graduated to more sophisticated ML efforts to build for themselves. “If you’re somebody that’s been in AI for a long time and has maturity in it and are doing things that are the cutting edge of AI, then there’s [a] reason for you to have built some of your own solutions to do some of those things,” he said.
For companies with less-advanced AI operations, shopping the existing MLops platform marketplace may be good enough, Hollman said.
“If you’re a new entrant into the machine learning space, those platforms are the best place to start. They’re going to have a soup-to-nuts experience,” he said. “Trying to build your own ML platform from scratch is a big undertaking.”
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