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NATE APP (competitors paused operations)

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NATE APP (competitors paused operations)

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After months of earning online shopping credits through the Nate app, Libby Rasmussen had over $500 left on her account going into December. She planned to use her remaining money on Christmas gifts for her family - until the credits abruptly disappeared from her account earlier this month.

Rasmussen, who works in social media marketing and sells home decor products like disco balls, has used the Nate app since October to curate lists of products for followers. The app, launched in 2018, completes the checkout process for shoppers and also allows influencers to share products in exchange for a commission when fans make purchases.

Rasmussen says she quickly saw the benefits of using Nate over other marketing tools. In addition to earning a 5 percent commission, influencers that were part of the creator program could cash in when they hit certain milestones, according to a now-defunct page explaining the program. Users could earn $500 for getting 50 people to make a Nate account and follow their lists or $750 plus sponsored giveaways for 150 followers. Rasmussen says she earned $1,500 within the first two weeks and knew of others who had made upwards of $10,000. Though influencers couldn't cash out their balance, they could spend their Nate Cash on anything on their lists, Rasmussen says.

When Rasmussen noticed that her balance was suddenly zero, she reached out to Nate last week and received a comforting assurance: the company was aware that user wallets were having issues, and a representative would reach out as soon as it was fixed.

"We seriously cannot thank you enough for your partnership"

By Friday, Nate's tone had abruptly changed. The company was "going through some unpredictable and turbulent times," read an email to influencers, and as a result, Nate was halting some operations, including the creator program (the news was first reported by Business Insider). That meant content creators would no longer be able to earn cash to spend and that they wouldn't be able to spend any credit they had left.

"We seriously cannot thank you enough for your partnership in this journey and your excitement in curating and sharing the products you love with the world," the company said in the email, obtained by The Verge. "You've been incredible in inspiring people around you."

A bullet point at the bottom of the list explained the disappearing credit: Nate Cash would be disabled "for now."

Apps like Nate or Liketoknow.it are important tools for content creators and influencers to monetize their platforms, particularly on TikTok and Instagram. In Rasmussen's case, she was already making content about products she uses; Nate and other tools allow her to earn a cut of profits resulting from her posts. Because users must download the app and create an account to view and follow influencer lists, Rasmussen was also helping the company acquire new users from her own following.

On TikTok, dozens of other creators responded to Rasmussen, saying they, too, had lost out on the remaining credit they had earned. Some say they lost a few hundred dollars, while others say thousands of dollars of credit was wiped out overnight.

"I had $4,000 left to spend on the app," another content creator wrote. "Free promo for nothing."

At the time of writing, Nate had not responded to The Verge 's questions.

This isn't the first time the startup's practices have come under scrutiny. Nate has raised over $50 million to date by marketing itself as a low-effort shopping tool that completes purchases for users using artificial intelligence for a small fee.

But according to a report by The Information in June, the high-tech "AI" Nate was using to purchase items was actually just human workers in the Philippines who manually completed the checkout process between 60 and 100 percent of the time in 2021. The company said the figures in the report were "incorrect."

In August, Nate laid off around 30 workers, according to The Information, following a previous round of layoffs that cut 20 percent of its workforce.

Nate launched the creator program last October, hoping to entice influencers into spreading the word about the startup. Creators promoted product lists with followers, shared "haul" videos of items purchased using credit, and extolled the benefits of using Nate over other tools.

But after having their credit taken away without warning, Rasmussen and other influencers responding to her TikTok say they feel misled by the company they took a chance on. Rasmussen says she takes some responsibility for promoting an app she trusted and endorsed but is particularly frustrated for other creators who lost out on the earnings they were promised.

"At the end of the day, we all worked hard to create space for this app, and we deserve compensation in exchange for it," Rasmussen says.

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Creators say shopping app Nate ran off with their promised earnings and rewards

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The Nate app allows influencers to link to products and earn a commission from follower purchases and engagement. Last week, creators noticed their reward balance was wiped out without warning.

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Olivia and Lunden are, as they describe,"Two girls in love", and their Shoppers are just as in love with them! These Georgia-based creators share a perfect blend of southern coastal chic OOTDs and bridal content. Follow Lunden and Olivia on their LTK Shop where they post all their latest Bachelorette finds, accessories, and trending #LTKFinds!

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LTK | Fashion, Home, Beauty, Fitness and More

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Shop the latest in fashion, home, beauty, fitness from LTK influencers you trust. Workwear ideas, wedding guest dresses, travel looks and so much more.

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WISHI: Your Online Personal Stylist - 7 Day Free Trial

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WISHI is an online styling app that connects you with the world's top stylists so you can look your best every day - effortlessly.

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WISHI: Your Online Personal Stylist - 7 Day Free Trial

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WISHI is an online styling app that connects you with the world's top stylists so you can look your best every day - effortlessly.

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At ChiefDX we have the best in Data Science whether you are looking for a Chief Data Scientist or some expertise on a project

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Olivia and Lunden are, as they describe,"Two girls in love", and their Shoppers are just as in love with them! These Georgia-based creators share a perfect blend of southern coastal chic OOTDs and bridal content. Follow Lunden and Olivia on their LTK Shop where they post all their latest Bachelorette finds, accessories, and trending #LTKFinds!

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LTK | Fashion, Home, Beauty, Fitness and More

# data[].attributes.attachment_link.description
Shop the latest in fashion, home, beauty, fitness from LTK influencers you trust. Workwear ideas, wedding guest dresses, travel looks and so much more.

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Nate App (Competitor operations have halted)

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Nate App (Competitor operations have halted)

# data[].attributes.attachment_link.readable_text
After months of earning online shopping credits through the Nate app, Libby Rasmussen had over $500 left on her account going into December. She planned to use her remaining money on Christmas gifts for her family - until the credits abruptly disappeared from her account earlier this month.

Rasmussen, who works in social media marketing and sells home decor products like disco balls, has used the Nate app since October to curate lists of products for followers. The app, launched in 2018, completes the checkout process for shoppers and also allows influencers to share products in exchange for a commission when fans make purchases.

Rasmussen says she quickly saw the benefits of using Nate over other marketing tools. In addition to earning a 5 percent commission, influencers that were part of the creator program could cash in when they hit certain milestones, according to a now-defunct page explaining the program. Users could earn $500 for getting 50 people to make a Nate account and follow their lists or $750 plus sponsored giveaways for 150 followers. Rasmussen says she earned $1,500 within the first two weeks and knew of others who had made upwards of $10,000. Though influencers couldn't cash out their balance, they could spend their Nate Cash on anything on their lists, Rasmussen says.

When Rasmussen noticed that her balance was suddenly zero, she reached out to Nate last week and received a comforting assurance: the company was aware that user wallets were having issues, and a representative would reach out as soon as it was fixed.

"We seriously cannot thank you enough for your partnership"

By Friday, Nate's tone had abruptly changed. The company was "going through some unpredictable and turbulent times," read an email to influencers, and as a result, Nate was halting some operations, including the creator program (the news was first reported by Business Insider). That meant content creators would no longer be able to earn cash to spend and that they wouldn't be able to spend any credit they had left.

"We seriously cannot thank you enough for your partnership in this journey and your excitement in curating and sharing the products you love with the world," the company said in the email, obtained by The Verge. "You've been incredible in inspiring people around you."

A bullet point at the bottom of the list explained the disappearing credit: Nate Cash would be disabled "for now."

Apps like Nate or Liketoknow.it are important tools for content creators and influencers to monetize their platforms, particularly on TikTok and Instagram. In Rasmussen's case, she was already making content about products she uses; Nate and other tools allow her to earn a cut of profits resulting from her posts. Because users must download the app and create an account to view and follow influencer lists, Rasmussen was also helping the company acquire new users from her own following.

On TikTok, dozens of other creators responded to Rasmussen, saying they, too, had lost out on the remaining credit they had earned. Some say they lost a few hundred dollars, while others say thousands of dollars of credit was wiped out overnight.

"I had $4,000 left to spend on the app," another content creator wrote. "Free promo for nothing."

At the time of writing, Nate had not responded to The Verge 's questions.

This isn't the first time the startup's practices have come under scrutiny. Nate has raised over $50 million to date by marketing itself as a low-effort shopping tool that completes purchases for users using artificial intelligence for a small fee.

But according to a report by The Information in June, the high-tech "AI" Nate was using to purchase items was actually just human workers in the Philippines who manually completed the checkout process between 60 and 100 percent of the time in 2021. The company said the figures in the report were "incorrect."

In August, Nate laid off around 30 workers, according to The Information, following a previous round of layoffs that cut 20 percent of its workforce.

Nate launched the creator program last October, hoping to entice influencers into spreading the word about the startup. Creators promoted product lists with followers, shared "haul" videos of items purchased using credit, and extolled the benefits of using Nate over other tools.

But after having their credit taken away without warning, Rasmussen and other influencers responding to her TikTok say they feel misled by the company they took a chance on. Rasmussen says she takes some responsibility for promoting an app she trusted and endorsed but is particularly frustrated for other creators who lost out on the earnings they were promised.

"At the end of the day, we all worked hard to create space for this app, and we deserve compensation in exchange for it," Rasmussen says.

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Creators say shopping app Nate ran off with their promised earnings and rewards

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The Nate app allows influencers to link to products and earn a commission from follower purchases and engagement. Last week, creators noticed their reward balance was wiped out without warning.

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INTERVIEW

1)    What can chief Dx provide to ALGO to create this application?

A: We can develop the algorithms needed to provide customers with a good experience. We can also develop the application and provide data for the algorithms to recognize the product that is being scanned.

2)    How long do you estimate development is going to take?

A: It depends on how complex you want the app to be, the more features you include like UX and UI can function in many ways. But it could take up to a month or a year even, as I said it really does depend.

3)    How much will you charge for development?

A: I can't give you an accurate number because the price will depend on the workload. Clients that we’ve worked with in the past needed image recognition embedded into their software which costs around 56,000 alone. So, a starting price for overall development is 100,000.

4)    Once the application is launched, what should be done afterwards?

A: you’re going to have to monitor the performance for the first week or even the first month depending on how many active users are in there. It’s advised that you gather user feedback so that you can make the CORRECT improvements based on that feedback. You basically need to maintain the app through updates and bug fixes for users to have a good experience while using your app.

5)    How can we obtain user data and data in general?

A: Upon the user consenting to their data being obtained there are different ways it can be done through analytics tools where aggregated data is collected.

6)    What type of data can be sold?

A: it depends if the user’s personal data such as their address is not saved in the database. In this case, the type of data you're looking to sell would be the customer's favourite style or something like that.

7)    Are you able to develop in-app purchases? Such as a monthly membership scheme.

A: yeah of course but how complex do you want it to be is the question, based on the complexity it can be done but no exact time frame is given because we need to investigate it with more detail.

8)    How can cyberattacks be prevented?

A)    Install a good firewall and anti-interception software or we could even provide you with cybersecurity systems.

9)    What legal formalities will I have to go through to set up the application?

A: you will need to make sure that you have a licence to operate, first, ensure the company has been registered of course, and obtain cybersecurity insurance as well to cover the damages or a breach of data.

10) What are the efficient ways to maintain an application?

a)     Regular updates and bug fixes I would say, but you’d also need to check the API’s so that the application can keep operating digitally.

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- BoF Careers Team (2023) What fashion retail professionals need to know Today, The Business of Fashion. The Business of Fashion. Available at: https://www.businessoffashion.com/articles/retail/careers-market-insights-industry-news-fashion-retail/ (Accessed: February 12, 2023).
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- GOV.UK (2015) Income tax rates and personal allowances, GOV.UK. GOV.UK. Available at: https://www.gov.uk/income-tax-rates (Accessed: February 17, 2023).
- IBM (2022) What is machine learning?, IBM. IBM. Available at: https://www.ibm.com/topics/machine-learning (Accessed: February 12, 2023).

- Mariana (2020) Top 9 disadvantages of mobile apps - and what to do instead, Mobile Apps Made Easy | Beezer.com. Beezer.com. Available at: https://www.beezer.com/disadvantages-of-mobile-apps/ (Accessed: February 12, 2023).

- Marr, B. (2021) How to use data strategically in business: 3 essential ways, Bernard Marr. Bernard Marr & Co. Available at: https://bernardmarr.com/how-to-use-data-strategically-in-business-3-essential-ways/ (Accessed: February 12, 2023).

- Microsoft (2023) What is power bi?, What Is Power BI? Definition and Features | Microsoft Power BI. Available at: https://powerbi.microsoft.com/en-gb/what-is-power-bi/ (Accessed: 09 April 2023).

- Nate Tech (2022) Shop and share your world. download the app., Nate. Nate Tech. Available at: https://www.nate.tech/ (Accessed: February 12, 2023).
- Pandey, R. (2023) Can apps be hacked (is it possible? how?), Security Escape. Security Escape. Available at: https://securityescape.com/can-apps-be-hacked/ (Accessed: February 12, 2023).
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- Sandish, L. (2022) Overcoming modern inventory management challenges, Supply & Demand Chain Executive. Supply & Demand Chain Executive. Available at: https://www.sdcexec.com/sourcing-procurement/sourcing-solutions/article/22617926/sps-commerce-inc-overcoming-modern-inventory-management-challenges (Accessed: February 12, 2023).
- Shopstyle (2023) About Us, ShopStyle. ShopStyle. Available at: https://www.shopstyle.co.uk/about (Accessed: February 12, 2023).
- Tan, E. and Humphrey, Q. (2023) Youth Culture 2023, WGSN . WGSN. Available at: https://www.wgsn.com/insight/article/63f35b7162f3cb603d1964de#page1 (Accessed: April 10, 2023).
- Tennenbaum, J. (2019) Automated machine learning, Medium. Towards Data Science. Available at: https://towardsdatascience.com/automated-machine-learning-d8568857bda1 (Accessed: February 23, 2023).
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- Venkataramakrishnan, S. et al. (2023) The battle for UK businesses to hold down customer price rises, Subscribe to read | Financial Times. Financial Times. Available at: https://www.ft.com/content/cdd1ddaf-4e10-440d-b234-2782a942531c (Accessed: April 7, 2023).
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Cost of developing apps and in-app purchases

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Cost of developing apps and in-app purchases

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How much does it cost to make an app in 2023? | Riselabs.co.uk

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The application development process will vary depending on the type and purpose of the app you wish to develop, such as corporate or internal apps and on-demand apps, like Uber, for consumers. Let's get into all the details! These are the first questions you should ask yourself.

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96 SHEETS BILLBOARD (2 Weeks)
96 Sheets Poster £2,153

96 Sheets Digital. £3,700

96 Sheets Backlit. £4,900
48 SHEETS BILLBOARD (2 Weeks)
48 Sheets Poster £1,270

48 Sheets Digital. £2,350

48 Sheets Backlit. £2,950

SUPER 6 DIGITAL. £5,500
(2 Weeks)
CINEMA (6 Sheets). £965
(2 Weeks)
SHOPPING CENTRE. £2,770
(2 Weeks) PHONE BOX (2 Weeks)
(6 Sheets). £562
BUS SHELTER (2 Weeks)
(6 Sheets). £562
TAXI (4 Weeks).
Full Livery. £4,700

Side Livery £2,900

Back Livery £1,975
BUS ADS (4 Weeks)
T- Strip. £6,700

Side/ Street Liner. £4,800

Bus Back. £750

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How much does outdoor advertising cost? | mch.co.uk

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96 SHEETS BILLBOARD (2 Weeks) 96 Sheets Poster £2,153 96 Sheets Digital. £3,700 96 Sheets Backlit. £4,900 48 SHEETS BILLBOARD (2 Weeks) 48 Sheets Poster £1,270 48 Sheets Digital. £2,350 48 Sheets Backlit. £2,950 SUPER 6 DIGITAL. £5,500 (2 Weeks) CINEMA (6 Sheets). £965 (2 Weeks) SHOPPING CENTRE.

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Membership Details

Work from hot desks, lounges, phone booths and more

Book meeting rooms and private offices with credits

Enjoy business essentials, including high-speed internet and unlimited coffee. View all

Individuals and remote employees

Unlock productive, flexible workspace near home or around the world, with fast Wi-Fi, printing, coffee, and more.

Get a tailored solution for your unique business needs. Contact us to build the right solution for your team.

Drop in and out of the office

This flexible membership option allows you to work from hot desks, private phone booths, lounges, pantries and more. Plus, use credits to book meeting rooms and daily private offices.

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Find out more about flexible workspace

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Workspace where you need it

Whether you need office space in New York or a meeting room in London, explore our hundreds of global locations.

Have a question? We have the answer.

What spaces do I get access to as part of my WeWork All Access monthly membership?

Your WeWork All Access monthly membership includes one coworking space booking per day, as well as the ability to book meeting rooms by the hour and private offices by the day using credits at select locations based upon your WeWork All Access membership plan. If it's your first time in a building, check in with the Community Team at the Front Desk.

Does my WeWork All Access membership include credits?

Credits are available based on your WeWork All Access membership plan as set forth in your membership agreement. Credits are refreshed on the first of every month. Unused credits will not roll over into the next month. You are welcome to make additional bookings over your included credits, and the charges for these bookings will be added to your next month's invoice. Find out more about booking a room here, and about additional credit fees here.

Is post and package handling included with WeWork All Access?

Post and package handling is available to purchase as an add-on service to WeWork All Access Basic and Plus membership plans in select locations. Find out more here.

During what hours can I book space with WeWork All Access?

Select WeWork locations offer 24/7 access. For those that do not, you can book workspace from Monday to Friday during the working hours of each location. To view the working hours of a specific location, navigate to the Building Guide on the WeWork app, and select 'Building Information'.

Can I book workspace on the weekend?

Once you have picked up and activated your keycard, your WeWork All Access membership allows you to book a workspace at weekends in select locations that offer 24/7 access. Note that certain amenities are only offered during working hours.

Can I book workspace on a public holiday?

Yes, once you have picked up and activated your keycard during standard working hours, your WeWork All Access membership allows you to book a workspace on public holidays at select 24/7 access locations. In addition, certain amenities are only offered during working hours.

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Coworking Membership | WeWork All Access

# data[].attributes.attachment_link.description
Membership Details Work from hot desks, lounges, phone booths and more Book meeting rooms and private offices with credits Enjoy business essentials, including high-speed internet and unlimited coffee. View all Individuals and remote employees Unlock productive, flexible workspace near home or around the world, with fast Wi-Fi, printing, coffee, and more.

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The costs of creating and running an application

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The costs of creating and running an application

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The overall app creation cost mainly hinges on the software goal and the time required to code it. In fact, plenty of factors influence how much you need to spend on development. Here are some of them:

Complexity

The more complex app you want to create, the higher its development cost. Ensure the quality of the interactive and advanced features to excel the product's performance. Decide if you need an application programming interface (API) to integrate with other services or a content management system (CMS).

Platforms

The number of platforms to place the app on impacts the total cost. For instance, native app development for a single operating system (OS) will be cheaper than a cross-platform one. Nevertheless, it'll be unprofitable. If you choose Android app development, you lose potential iOS device users. And vice versa.

Team

The more, the merrier! And also more costly. The main factors that define the price are skills and experience. Junior, middle, and senior developers will ask for different fees. Keep in mind that the amount of people involved in the application development and their experience influences the quality of the final product and the date of its release.

Maintenance

Post-release development is as crucial as the pre-launching one. You must ensure the application works smoothly without glitches, crashes, or other issues. The total price of building an app worldwide, including the United Kingdom, already includes the mobile app maintenance cost. The support lasts for a specific time after launch, an average of 6 months.

How Application Scale Influences Development Costs

App scalability means its potential to handle many users and effectively maintain interactions with them. Users want an impeccably working solution that works without problems. The absence of adequate scalability will cost not only money but the entire reputation of your business.

The scale of the application deals with its database, back-end, and servers. The wider your target audience is, the more scalable your platform has to be.

The complex multifunctional application related to social media, entertainment, or news could cost over £100,000. Developing a niche and simple app in the UK with minimal features might cost less than £20,000. Next, we'll show examples of different platforms and their average prices.

A good old social media application for sharing media, kind of like Instagram or Facebook, doesn't sound like a wild challenge. Yet, it's essential to consider all the functionalities you want to add that would make it valuable and innovative. The cost of app development in the UK starts at £40,000 and might reach up to £300,000.

To create something like TikTok, be ready to spend a fortune on obtaining music licenses and integrating numerous services and features for more engagement. Such an app creation cost in the UK starts at £130,000.

How about an on-demand app like Uber or Uber Eats? Those require the creation of separate modules for restaurants, clients, drivers, and managers. The total cost of creating an app in the UK might vary from £65,000 to £400,000.

Let's overview an online dating app. Rather than a niche platform, an application like Tinder won't require more than previously listed. The approximate cost of its development is between £20,000 and £50,000.

App Development Options

There are five common ways to create an application:

Develop an app yourself

Developing an app on your own requires significant technical skill and expertise.

You can create an app from scratch if you are well-versed in programming languages, front-end and back-end frameworks, databases, cloud solutions, etc. However, if you are not an experienced developer, you will likely need to invest considerable time and effort to learn the necessary skills.

Additionally, it can be risky, as you may encounter issues and errors that require professional assistance. If you are considering developing an app independently, assess your technical abilities and determine whether it is viable.

There are numerous free resources to learn how to develop apps. For example, Codecademy, Udacity, Coursera, edX, YouTube channels ( The Net Ninja, CodeWithChris), Apple Developer Documentation, Android Developer Documentation, and others.

Hire a freelancer

Hiring a freelancer can be an attractive option for small businesses and startups. It allows you to develop an app without committing to the overhead of hiring a full-time employee.

Freelancers offer flexible schedules and can work remotely, but you should verify their credentials and experience. Look for specialists with a proven track record of building high-quality apps, and don't forget to learn reviews and ratings from past clients.

Freelancers may provide guidance and support throughout the development process, making it easier to navigate potential roadblocks.

There are many platforms where you can find freelancers for app creation. The most popular options include Upwork, Freelancer, Fiverr, Toptal, and Guru.

Partner with a development company

Hiring a software development company can be a reliable and efficient way to create a cutting-edge app. Vendors employ teams of experienced coders and designers working together due to a well-organized workflow and security standards.

Additionally, IT companies typically have access to the latest tools, ensuring your app will use the most advanced technology stack.

When selecting an app development company, check its reputation and read testimonials. Look for a company with an experience similar to your specific needs and schedule an initial consultation to discuss your project requirements.

Inoxoft is a software creation vendor trusted by startups and Fortune 500 companies. Learn more about our Android and iOS app development solutions.

Customize an app template

Buying a pre-designed app template is another cost-effective option. They are often available online and can be customized to suit your specific needs.

However, you should understand that buying a template may result in a less unique or customized app, as your competitors can also use the same example. Additionally, customizing may be challenging and time-consuming if you are unfamiliar with app development.

You can use these resources for purchasing pre-designed app templates: CodeCanyon, TemplateMonster, Instamobile, Sketch App Sources, and Codester.

Build with a constructor

App builder products are designed to simplify the development process for non-technical users. These products often offer drag-and-drop interfaces and pre-built components. This way, you can easily create a basic app without any coding knowledge.

However, app builders may be limited in customization and functionality and unsuitable for complex or sophisticated solutions. If you consider using this option, carefully evaluate your needs and ensure that the product you choose is capable of meeting them.

Examples of app builder platforms include AppyPie, BuildFire, Bubble, Zoho Creator, AppSheet, and others.

Approximate App Developers Cost in the UK

How much does it cost to make an app in the UK? The development team is pivotal in the total sum you invest in the project. And much depends on the specialists' experience:

Junior developers

Industry newbies, aka junior developers, with a year of experience as a maximum, is one of the first options. They are no strangers to Javascript and frameworks like React, Vue, Angular, or Svelte. Also, juniors can develop back-end development using Javascript/Java/Typescript/C# and might even know the Agile methodologies.

The cost to build an app in the UK with the help of junior developers is up to £35,000 per year.

Mid-level developers

Mid-level specialists cover a more comprehensive range of skills. They have experience developing applications for different operating systems and working with cross-platform technologies. Mid-level developers build, test, release and deploy apps on the market. With up to 4 years of experience, they know how to develop cloud-native solutions and work with databases like MongoDB, MySQL, PostgreSQL, etc.

As a result, creating a moderate app with mid-level developers will cost you £50,000 per year.

Senior developers

As great old-timers, senior developers know what they do. By choosing them, you get a strategic plan for the development process. Seniors will provide you with bespoke app architecture design and codebase and excellent problem-solving skills. They know how to work with cloud services like AWS, GCP, and Microsoft Azure.

As a bonus, seniors can lead the dev team and provide exceptional final product quality. To build a complex app with advanced functionality, you'll need a minimum of £200,000.

App Marketing Costs

The cost of marketing an app can vary widely depending on several factors, such as the size and scope of your marketing campaign, the target audience, and the app's niche. The essential costs include the following:

App store optimization (ASO)

ASO involves optimizing your app's metadata, such as its title, description, and keywords, to improve its visibility in app store search results. It is typically a one-time cost that depends on the complexity of your app and the level of optimization required.

App install campaigns

App install campaigns focus on driving installs. They include ads on social media platforms, mobile ad networks, and other channels. The amount charged varies with the platform, targeting options, and the competition level in your niche.

Influencer marketing

Influencer marketing means partnering with social media influencers or other content creators to promote your app to their followers. It can effectively reach a highly targeted audience, and its price varies due to the influencer's reach and engagement.

Public relations (PR)

PR is about building relationships with journalists, bloggers, and other media outlets to generate buzz and credibility for your app. However, it requires a significant investment of time and resources.

Retargeting

Retargeting covers showing ads to users who have already downloaded or interacted with your app. This way, you can re-engage users and encourage them to use it more frequently.

Here's a table that summarizes the approximate cost ranges for each of the app marketing strategies:
We used data from sources such as Business of Apps, Mobile App Daily, and others.
Ongoing Costs of App Maintenance

The ongoing costs for maintaining an app can vary depending on the app type and features, the development team size, and the infrastructure it runs on. They include the following.

Server hosting costs

The cost of hosting the app on a server is determined by the hosting provider, the server type, and the app size. Cloud-based hosting options like Amazon Web Services (AWS), Google Cloud Platform, and Microsoft Azure offer scalable solutions that can grow with the app's usage.

Development costs

Even after launch, an app may require regular updates and bug fixes from experienced developers. You should have a maintenance plan to ensure your software is up-to-date and functioning correctly.

Content and data costs

Some apps require regular updates to their content, including product listings, pricing information, or user data changes. So, you will need ongoing expenses, such as paying for access to data feeds or hiring a team to manage content.

Third-party services costs

Many apps rely on third-party services for features like push notifications, in-app messaging, and analytics. They may demand continuous subscription or usage-based fees, which can increase over time.

Security and compliance costs

As the app evolves, it should meet new security and compliance standards. It includes regular security audits, compliance reviews, and regular monitoring of user data.

Customer support costs

Providing ongoing customer support can be costly, especially if the app has a large user base. Remember about maintaining a help desk, providing live chat support, or offering phone support.

We prepared a table with estimated average costs for maintaining an app, broken down by category:

Costs of Publishing an App on the App Store and Google Play Store

Publishing an app on the App Store and Google Play Store involves certain costs that developers should be aware of:

Developer account

Both platforms require developers to have an account to publish an app. There is a one-time registration fee for Google Play Store and an annual fee for App Store.

In-App purchases and subscriptions

In-app purchases and subscriptions on the App Store and Google Play Store generate revenue, but the platforms also charge a commission fee for these transactions. You should consider these expenses when setting the prices to guarantee app profitability.

And here are the fees for App Store and Google Play Store:

In-App Purchases and Subscriptions

30% share if you make over $1M (£720,000) in annual net app revenue.

1. You make less than $1M (£720,000) in annual app revenue;

2. You're after 12 months of service in the App Store with a subscription-based app.

15% for the first $1M (£720,000) of earnings you make each year.

Once the total earnings exceed $1M (£720,000), the service fee is 30% for the rest of the year.

How to Choose an App Development Company

Choosing an app development company is an important decision that can significantly impact your success. Here are some factors to consider:

Experience

Check the company's portfolio to see the types of apps they have already created. Ensure developers have experience working in your industry and understand its unique challenges and requirements.

Expertise

Ask if the company has expertise in the specific technology stack required to develop your app. For example, this is what Inoxoft uses to create mobile solutions:

Cloud Infrastructure: AWS, Azure, Google Cloud

Programming languages: Java, Kotlin (Android), Swift, Objective-C (iOS), Python

Backend frameworks: Node.js, Ruby on Rails, .NET, Django

Front-end frameworks: React Native, Flutter, HTML, CSS, JavaScript

Cooperation model

Experienced vendors can offer you several models of cooperation. For example, with Inoxoft, you can create applications using:

Remote developers - a flexible option where you pay for the time and materials and can change or add requirements

Dedicated team - a solution with a monthly salary where developers work on the whole project from start to finish

Extended team - a staff augmentation service with a fixed price defined by the contract before the start of cooperation

If you are not sure which model is suitable for your specific needs, please contact our team.

Communication

Communication is critical for successful app development. Pay attention to the company's availability, response time, project status updates, progress reports, and honesty about any issues or challenges.

Security

Ensure the development vendor you choose has expertise in building secure apps. The company should use encryption, secure data storage, and communication protocols and comply with GDPR, HIPAA, and PCI-DSS regulations.

Support and maintenance

Ask the company about their post-development support and maintenance services to keep your app up-to-date and stable functioning.

Price

Price is essential but not the only factor. Be cautious of companies that offer low amounts, as they may sacrifice quality or cut corners to reduce costs. Look for a vendor that offers a fair budget that aligns with the value they provide.

Consider Inoxoft's Team of Experts to Build Your Application

Inoxoft is an IT company that offers mobile app development services. Our certified engineers guarantee bug-free code and fast performance and consider all your specific requirements.

We create mobile software using an advanced technology stack, intuitive UI/UX design, and innovative features. For example, we implement AI/ML solutions based on big data analytics. Also, our apps include payment, scheduling, chatting, media uploading, geolocation, notifications, etc. Inoxoft's portfolio covers dozens of mobile applications for healthcare, real estate, education, fintech, logistics, and other industries. And here are some of our latest projects:

Our client faced the problem of poor equine state management. The idea was to create an application allowing all parties to record and access information about racehorses' health and training.

We have developed a solution supporting seven user roles, including equine owners, trainers, veterinarians, etc. It allowed them to add important information, download media files, view the activity calendar, and leave comments on the animal's profile. And the owner got the ability to hire professional horse care providers and pay for the services directly in the app.

The solution helped make racehorse management transparent, improve animal welfare, and connect owners with service providers.

Beyond Verbal Company is a startup that recognizes emotions and diseases using voice intonations. With a health and emotion monitoring algorithm, the company needed a platform to collect patient data and create a voice database. The ultimate goal was to improve AI predictions for the diagnosis of cardiovascular diseases.

While developing a web platform for doctors and a mobile application for patients, we faced some challenges. The interface had to be equally convenient for all types of users, and the solution itself had to increase the accuracy of voice analysis and comply with HIPAA regulations.

As a result, we have created a secure and intuitive cloud solution. It allowed doctors to manage patients, launch surveys and work with recordings, increasing the accuracy of voice analysis and the diagnosis relevance.

We were approached by a company dealing with employee health insurance. They needed a solution that would allow each insured employee of the enterprise to be informed about the service's details. It was essential to provide instant access to a personal insurance plan with a quick search for the necessary data.

As a result, we created an application, allowing each employee to view personal health insurance conditions, limits, and lists of hospitals and specialists. Users can also quickly contact an insurance agent for additional information.

Final Thoughts

The cost of developing a mobile application is influenced by many factors, including its complexity, platform, team size, and experience level. Also, you should consider the launch expenses on the App Store or Google Play Store, maintenance costs, and marketing fees.

By cooperating with an experienced vendor, you can optimize project prices and avoid making expensive mistakes. Contact us to discuss the budget and timing of your idea, as the exact app development cost in the UK can only be determined considering the project specifics.

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How Much Does It Cost to Make an App in the UK? | Inoxoft

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The overall app creation cost mainly hinges on the software goal and the time required to code it. In fact, plenty of factors influence how much you need to spend on development. Here are some of them: The more complex app you want to create, the higher its development cost.

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Income Tax rates and Personal Allowances

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Personal Allowance, Income Tax rates, bands and thresholds.

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Inspect all incoming requests against known threats, the active firewall determines if the request is 'clean' before the message is passed on, whereas a threat would immediately be intercepted.

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Information on the firewalls used specifically for mobile applications. This is different to firewalls used for websites and search engines.

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Application firewalls, or application layer firewalls, use a series of configured policies to determine whether to block or allow communications to or from an app.

Traditional firewalls control data flow to and from the CPU, examining each packet as it passes through. An application firewall takes it further by controlling the execution of files or code by specific applications. This way, even if an intruder gains entry to a network or server, they can't execute malicious code.

Application firewalls can be active or passive.

Active - Active app firewalls actively inspect all incoming requests-including the actual message being exchanged-against known vulnerabilities such as SQL injections, parameter and cookie tampering, and cross-site scripting. Only requests deemed "clean" are passed to the application.

Passive - Passive app firewalls act in a similar way to an intrusion detection system (IDS) in that they also inspect all incoming requests against known vulnerabilities, but they don't actively reject or deny those requests if a potential attack is discovered.

Application firewalls are generally remotely updateable, which allows them to prevent newly discovered vulnerabilities. They're often more up to date than specific security-focused code included in applications, due to the longer development and testing cycles required to include such code within applications.

Today, you'll most commonly see web application firewalls (WAFs) to filter, monitor, and block HTTP/S traffic to and from a web application, specifically.

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Application firewalls, or application layer firewalls, use a series of configured policies to determine whether to block or allow communications to or from an app. Traditional firewalls control data flow to and from the CPU, examining each packet as it passes through.

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This introduction to machine learning provides an overview of its history, important definitions, applications and concerns within businesses today.

What is machine learning?

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

IBM has a rich history with machine learning. One of its own, Arthur Samuel, is credited for coining the term, "machine learning" with his research (PDF, 481 KB) (link resides outside IBM) around the game of checkers. Robert Nealey, the self-proclaimed checkers master, played the game on an IBM 7094 computer in 1962, and he lost to the computer. Compared to what can be done today, this feat almost seems trivial, but it's considered a major milestone within the field of artificial intelligence. Over the next couple of decades, the technological developments around storage and processing power will enable some innovative products that we know and love today, such as Netflix's recommendation engine or self-driving cars.

Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase, requiring them to assist in the identification of the most relevant business questions and subsequently the data to answer them.

Machine Learning vs. Deep Learning vs. Neural Networks

Since deep learning and machine learning tend to be used interchangeably, it's worth noting the nuances between the two. Machine learning, deep learning, and neural networks are all sub-fields of artificial intelligence. However, deep learning is actually a sub-field of machine learning, and neural networks is a sub-field of deep learning.

The way in which deep learning and machine learning differ is in how each algorithm learns. Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required and enabling the use of larger data sets. You can think of deep learning as "scalable machine learning" as Lex Fridman notes in this MIT lecture (00:30) (link resides outside IBM). Classical, or "non-deep", machine learning is more dependent on human intervention to learn. Human experts determine the set of features to understand the differences between data inputs, usually requiring more structured data to learn.

"Deep" machine learning can leverage labeled datasets, also known as supervised learning, to inform its algorithm, but it doesn't necessarily require a labeled dataset. It can ingest unstructured data in its raw form (e.g. text, images), and it can automatically determine the set of features which distinguish different categories of data from one another. Unlike machine learning, it doesn't require human intervention to process data, allowing us to scale machine learning in more interesting ways. Deep learning and neural networks are primarily credited with accelerating progress in areas, such as computer vision, natural language processing, and speech recognition.

Neural networks, or artificial neural networks (ANNs), are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network. The "deep" in deep learning is just referring to the depth of layers in a neural network. A neural network that consists of more than three layers-which would be inclusive of the inputs and the output-can be considered a deep learning algorithm or a deep neural network. A neural network that only has two or three layers is just a basic neural network.

See the blog post " AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What's the Difference? " for a closer look at how the different concepts relate.

How machine learning works

UC Berkeley (link resides outside IBM) breaks out the learning system of a machine learning algorithm into three main parts.

1. A Decision Process: In general, machine learning algorithms are used to make a prediction or classification. Based on some input data, which can be labelled or unlabeled, your algorithm will produce an estimate about a pattern in the data.

2. An Error Function: An error function serves to evaluate the prediction of the model. If there are known examples, an error function can make a comparison to assess the accuracy of the model.

3. An Model Optimization Process: If the model can fit better to the data points in the training set, then weights are adjusted to reduce the discrepancy between the known example and the model estimate. The algorithm will repeat this evaluate and optimize process, updating weights autonomously until a threshold of accuracy has been met.

Machine learning methods

Machine learning classifiers fall into three primary categories.

Supervised machine learning

Supervised learning, also known as supervised machine learning, is defined by its use of labeled datasets to train algorithms that to classify data or predict outcomes accurately. As input data is fed into the model, it adjusts its weights until the model has been fitted appropriately. This occurs as part of the cross validation process to ensure that the model avoids overfitting or underfitting. Supervised learning helps organizations solve for a variety of real-world problems at scale, such as classifying spam in a separate folder from your inbox. Some methods used in supervised learning include neural networks, naïve bayes, linear regression, logistic regression, random forest, support vector machine (SVM), and more.

Unsupervised machine learning

Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention. Its ability to discover similarities and differences in information make it the ideal solution for exploratory data analysis, cross-selling strategies, customer segmentation, image and pattern recognition. It's also used to reduce the number of features in a model through the process of dimensionality reduction; principal component analysis (PCA) and singular value decomposition (SVD) are two common approaches for this. Other algorithms used in unsupervised learning include neural networks, k-means clustering, probabilistic clustering methods, and more.

Semi-supervised learning

Semi-supervised learning offers a happy medium between supervised and unsupervised learning. During training, it uses a smaller labeled data set to guide classification and feature extraction from a larger, unlabeled data set. Semi-supervised learning can solve the problem of having not enough labeled data (or not being able to afford to label enough data) to train a supervised learning algorithm.

For a deep dive into the differences between these approaches, check out " Supervised vs. Unsupervised Learning: What's the Difference?"

Reinforcement machine learning

Reinforcement machine learning is a behavioral machine learning model that is similar to supervised learning, but the algorithm isn't trained using sample data. This model learns as it goes by using trial and error. A sequence of successful outcomes will be reinforced to develop the best recommendation or policy for a given problem.

The IBM Watson® system that won the Jeopardy! challenge in 2011 makes a good example. The system used reinforcement learning to decide whether to attempt an answer (or question, as it were), which square to select on the board, and how much to wager-especially on daily doubles.

Learn more about reinforcement learning.

Real-world machine learning use cases

Here are just a few examples of machine learning you might encounter every day:

Speech Recognition: It is also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, and it is a capability which uses natural language processing (NLP) to process human speech into a written format. Many mobile devices incorporate speech recognition into their systems to conduct voice search-e.g. Siri-or provide more accessibility around texting.

Customer Service: Online chatbots are replacing human agents along the customer journey. They answer frequently asked questions (FAQs) around topics, like shipping, or provide personalized advice, cross-selling products or suggesting sizes for users, changing the way we think about customer engagement across websites and social media platforms. Examples include messaging bots on e-commerce sites with virtual agents, messaging apps, such as Slack and Facebook Messenger, and tasks usually done by virtual assistants and voice assistants.

Computer Vision: This AI technology enables computers and systems to derive meaningful information from digital images, videos and other visual inputs, and based on those inputs, it can take action. This ability to provide recommendations distinguishes it from image recognition tasks. Powered by convolutional neural networks, computer vision has applications within photo tagging in social media, radiology imaging in healthcare, and self-driving cars within the automotive industry.

Recommendation Engines: Using past consumption behavior data, AI algorithms can help to discover data trends that can be used to develop more effective cross-selling strategies. This is used to make relevant add-on recommendations to customers during the checkout process for online retailers.

Automated stock trading: Designed to optimize stock portfolios, AI-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention.

Challenges of Machine Learning

As machine learning technology advances, it has certainly made our lives easier. However, implementing machine learning within businesses has also raised a number of ethical concerns surrounding AI technologies. Some of these include:

Technological Singularity

While this topic garners a lot of public attention, many researchers are not concerned with the idea of AI surpassing human intelligence in the near or immediate future. This is also referred to as superintelligence, which Nick Bostrum defines as "any intellect that vastly outperforms the best human brains in practically every field, including scientific creativity, general wisdom, and social skills." Despite the fact that Strong AI and superintelligence is not imminent in society, the idea of it raises some interesting questions as we consider the use of autonomous systems, like self-driving cars. It's unrealistic to think that a driverless car would never get into a car accident, but who is responsible and liable under those circumstances? Should we still pursue autonomous vehicles, or do we limit the integration of this technology to create only semi-autonomous vehicles which promote safety among drivers? The jury is still out on this, but these are the types of ethical debates that are occurring as new, innovative AI technology develops.

AI Impact on Jobs:

While a lot of public perception around artificial intelligence centers around job loss, this concern should be probably reframed. With every disruptive, new technology, we see that the market demand for specific job roles shift. For example, when we look at the automotive industry, many manufacturers, like GM, are shifting to focus on electric vehicle production to align with green initiatives. The energy industry isn't going away, but the source of energy is shifting from a fuel economy to an electric one. Artificial intelligence should be viewed in a similar manner, where artificial intelligence will shift the demand of jobs to other areas. There will need to be individuals to help manage these systems as data grows and changes every day. There will still need to be resources to address more complex problems within the industries that are most likely to be affected by job demand shifts, like customer service. The important aspect of artificial intelligence and its effect on the job market will be helping individuals transition to these new areas of market demand.

Privacy:

Privacy tends to be discussed in the context of data privacy, data protection and data security, and these concerns have allowed policymakers to make more strides here in recent years. For example, in 2016, GDPR legislation was created to protect the personal data of people in the European Union and European Economic Area, giving individuals more control of their data. In the United States, individual states are developing policies, such as the California Consumer Privacy Act (CCPA), which require businesses to inform consumers about the collection of their data. This recent legislation has forced companies to rethink how they store and use personally identifiable data (PII). As a result, investments within security have become an increasing priority for businesses as they seek to eliminate any vulnerabilities and opportunities for surveillance, hacking, and cyberattacks.

Bias and Discrimination:

Instances of bias and discrimination across a number of intelligent systems have raised many ethical questions regarding the use of artificial intelligence. How can we safeguard against bias and discrimination when the training data itself can lend itself to bias? While companies typically have well-meaning intentions around their automation efforts, Reuters (link resides outside IBM) highlights some of the unforeseen consequences of incorporating AI into hiring practices. In their effort to automate and simplify a process, Amazon unintentionally biased potential job candidates by gender for open technical roles, and they ultimately had to scrap the project. As events like these surface, Harvard Business Review (link resides outside IBM) has raised other pointed questions around the use of AI within hiring practices, such as what data should you be able to use when evaluating a candidate for a role.

Bias and discrimination aren't limited to the human resources function either; it can be found in a number of applications from facial recognition software to social media algorithms.

As businesses become more aware of the risks with AI, they've also become more active this discussion around AI ethics and values. For example, last year IBM's CEO Arvind Krishna shared that IBM has sunset its general purpose IBM facial recognition and analysis products, emphasizing that "IBM firmly opposes and will not condone uses of any technology, including facial recognition technology offered by other vendors, for mass surveillance, racial profiling, violations of basic human rights and freedoms, or any purpose which is not consistent with our values and Principles of Trust and Transparency."

To read more about this, check out IBM's policy blog, relaying its point of view on " A Precision Regulation Approach to Controlling Facial Recognition Technology Exports."

Accountability

Since there isn't significant legislation to regulate AI practices, there is no real enforcement mechanism to ensure that ethical AI is practiced. The current incentives for companies to adhere to these guidelines are the negative repercussions of an unethical AI system to the bottom line. To fill the gap, ethical frameworks have emerged as part of a collaboration between ethicists and researchers to govern the construction and distribution of AI models within society. However, at the moment, these only serve to guide, and research (link resides outside IBM) (PDF, 984 KB) shows that the combination of distributed responsibility and lack of foresight into potential consequences isn't necessarily conducive to preventing harm to society.

To read more on IBM's position around AI Ethics, read more here.

Machine learning and IBM Cloud

IBM Watson Machine Learning supports the machine learning lifecycle end to end. It is available in a range of offerings that let you build machine learning models wherever your data lives and deploy them anywhere in your hybrid multicloud environment.

IBM Watson Machine Learning on IBM Cloud Pak for Data helps enterprise data science and AI teams speed AI development and deployment anywhere, on a cloud native data and AI platform. IBM Watson Machine Learning Cloud, a managed service in the IBM Cloud environment, is the fastest way to move models from experimentation on the desktop to deployment for production workloads. For smaller teams looking to scale machine learning deployments, IBM Watson Machine Learning Server offers simple installation on any private or public cloud.

To get started, sign up for an IBMid and create your IBM Cloud account.

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This introduction to machine learning provides an overview of its history, important definitions, applications and concerns within businesses today.