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API monetisation and API first strategies have become a new normal with businesses with digital maturity.

Last years pandemic catalysed digital maturity across organisations. The niche markets found even more niche business opportunities, thanks to the widespread adoption and development of APIs (Application Programming Interface). In their most basic form, APIs are doorways between two software applications and become extremely powerful when tailored to the needs of the developers. Web, mobile and automation are some of the key applications powered by APIs. According to a report by Google Cloud, API programs are the core drivers of digital transformation by playing a significant role in digital experiences, business operations, innovation, and growth.

Companies around the world possess valuable data ready to be capitalised. All they need are the services that can bridge the gap between customers and third parties. APIs fit right into this mix. For instance, the banking sector has witnessed a tremendous revolution with the advent of fintech products. The infrastructure behind the payment gateways are powered by the APIs like those of Stripe or Razorpay. These fintech API providers are multi-billion dollar companies today. Machine learning-based API service providers are next in line to take the markets by storm.

Databricks API supports services to manage clusters, instance pools, libraries, tokens, and MLflow models. Databricks is currently valued at $28 billion.

For example, last week, Databricks, a company that offers unified platform services raised $1 billion that rocketed its market value to $28 billion. Though AWS too offers Spark services, Databricks Spark services seem to have an edge over them. They offer additional customisations while combining the synergies of top players to serve an user.

According to an Apigee survey, AI- and ML-powered API security and monitoring solutions used for anomaly detection and security analytics grew 230% year-over-year between September 2019 and September 2020.

When easily reusable, APIs let developers modularly combine, and recombine functionality and data for new uses, with virtually no marginal cost for each additional use of the API. If one developer builds a new application by leveraging an API that looks up store locations, another developer can leverage the same API for another application without the enterprise incurring any additional overhead.

The APIs will (source: Gartner):

APIs also allow the organisations to take smart decisions by providing details of the product consumption at the user level, which in turn can be used by the developers to enhance the end product. This sounds like every other business strategy, but APIs make it more accessible. It helps them understand the value of an organisations digital assets. Beyond helping enterprises, writes Bala Kasiviswanathan of Google Cloud, API analytics can help both IT and business leaders refine the KPIs they use for analytics. If an API becomes popular with developers in a new vertical for example, that may persuade the enterprise to focus on KPIs like adoption among these specific developers, rather than on overall adoption, said Bala.

In 2019, machine learning as a service (MLaaS) raked in an estimated $1 billion and is expected to grow to $8.4 billion by the end of 2025. The success of these services can be traced to the customised APIs. For example, Googles prediction API, can be used to classify an image for $0.0015 and even perform sentiment analysis on text for just $0.00025 only. The user gets to avail Googles state-of-the-art tech and Google gets compensated for its research. APIs can act as conduit between innovation and incentives.

No matter what kind of machine learning product you are building, it eventually boils down to whether the customer can deploy these models with just a few clicks. APIs help do this. Research labs like OpenAI resorted to releasing APIs to commercialise their exotic research. The much talked about language model, GPT-3 was tapped through these APIs and was leveraged to set up many million dollar startups. Now, customers can access state-of-the-art ML models without the headaches of training from scratch; GPT-3 training that cost OpenAI over $4 million.

If you are an API service provider, then here are a few takeaways from OpenAIs success:

Also Read: This Framework Leads to 50% Cost Reduction From ML API Calls

Going forward, more Cloud and AI based services will be offered as API-centric services. Services like AWS Lambda are designed for producing exclusively API/event-centric application services. According to Gartner, adoption of API-centric models for SaaS delivery is expected to increase and the API economy has already established itself as a precursor of digital transformations and the primary way to grow an ecosystem.

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How APIs Breathe Life Into Machine Learning Organisations - Analytics India Magazine

Delivering fast and reliable machine learningbusinesssolutions

JERSEY CITY, N.J., Feb. 9, 2021 /PRNewswire/ --ElectrifAi, one of the world's leading companies inpractical artificial intelligence (AI) and pre-built machine learning models, today announcedit has a new and improved spend analytics and procurement tool called SpendAi.

What makes SpendAi different from other products on the market? SpendAi combines the power of machine learning models to construct a solid foundation of a high-quality comprehensive data set and a highly configurable user experience. ElectrifAi puts its industry leading data cleansing and structuring expertise to practical use in this solution. Our scientists and engineers have applied their unique skill sets to produce the most highly automated and effective data transformation architecture in the market. This bedrock of data then enables a uniquely configurable experience to the end user. The industry has been lacking a flexible tool such as SpendAi. Every company has a different way of looking at procurement and categorizing their vendors and spend. SpendAi is the only tool on the market that gives companies the ability to change the vendor and spend classification on their own.

Why is machine learning important? How does machine learning change spend analytics? ElectrifAi's machine learning drastically reduces unclassified and misclassified spend, giving procurement professionals a much clearer picture of their vendor leverage and dependencies. It also provides far greater insight to maverick and off-contract spend, optimization of discount opportunities along with other features. In short, users have much more visibility into their risks and opportunities. AI is then used to find and prioritize those risks and opportunities. As a result, teams spend less time searching and more time acting on insights. This turns procurement into a strategic business partner for the business.

SpendAi enables companies to look deeply into their data and generates insights that procurement professionals can use right away. Making structural changes on their own is also very simple with this tool and they don't have to pay a professional or wait overnight for results. This again gives people a way to look at procurement strategically, not just reactively or pulled together haphazardly.

Companies can now quickly analyze all their dataincluding direct and indirect spend materials and servicesacross every system they use to get insights into how they can reduce costs and improve their cash position, all in one convenient location. The flexibility of SpendAi is very user friendly and enables users to make quick and comprehensive decisions.

Insights provided by the machine learning capabilities of SpendAi allow companies to spot unexpected or disadvantageous spend patterns that warrant further attention. SpendAi gives them a prioritized list of things to look at and consider as either risk or savings opportunities or something that looks amiss.

Nisreen Bagasra, Chief Procurement Officer from Veolia said: "We're looking forward to SpendAi because of the flexibility it provides. This tool is going to allow us to be more dynamic and accelerate our business. This is like nothing we've seen in the market before. There's never been a way to see all your data in one place before. This is the first tool that uses machine learning to organize the system and tie all the data with high-quality so you can really be strategic. This is a new generation of spend analytics."

About ElectrifAiElectrifAi is a global leader in business-ready machine-learning models. ElectrifAi's missionis tohelp organizations change the way they work through machine learning: driving costreductionas wellasprofit and performance improvement. Founded in 2004,ElectrifAi boastsseasoned industryleadership,aglobal team of domain experts, andaproven record oftransforming structured and unstructured data at scale.A large library ofAI-basedproductsreachesacrossbusiness functions, data systems, and teams to drive superior resultsin record time. ElectrifAi has approximately 200 data scientists, software engineers andemployees with a proven record of dealing with over 2,000 customer implementations,mostly for Fortune 500 companies. At the heart of ElectrifAi's mission is a commitment tomakingAI and machine learning more understandable, practical and profitable forbusinesses andindustries across the globe. ElectrifAi is headquartered in Jersey City, withoffices located in Shanghai and New Delhi.

SOURCE ElectrifAi

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ElectrifAi Announces Updates to SpendAi, an Innovative and Flexible Procurement Tool - PRNewswire

Advanced computer programs that use machine learning are transforming the way medical research is done in Nova Scotia and around the world.

Work that might have taken years to complete, or would have been astronomically expensive, can now be done faster and at lower cost.

It has allowed teams in this province to develop better ways to identify and treat cancer, discover new drugs to help blind children see, and speed up medical tests.

Acomputer program learns from data and identifies patterns with little human intervention. A more advanced form of machine learning is often referred to as neural networks.

For example, a program can be shown millions of pictures of cars and, eventually, it will identify a particular car, says Thomas Trappenberg, a Dalhousie University computer science professor.

Medical researchers have turned that learning power inward, setting up programs to recognize cancer cells and proteins.

One group is trying to figure out how to better identify the differences between cancer cells and healthy cells,and, in doing so, what drug treatments will work best for an individual.

"Target discovery is very important right now," said Brendan Leung, an assistant professor in applied oral sciences at Dalhousie. "So knowing what to hit is just as important as designing the weapon to hit it."

The research team he's part of also includes a tumour biologist and computer scientist.

Leung hopes the technology will eventually allow scientists to design drugs to better target cancer cells without harming healthy cells.

He said the research would be almost impossible without a computer capable of machine learning.

"With all this big data it just surpasses humans' ability to comprehend what is going on," he said.

"Not to mention human beings are notoriously biased.If you've been working with a particular gene for the past 20 years, you know, it's your favourite thing to look at, you will find what you want to see. So the way I see it, it's a great way to take away that bias."

Leung said software can be biased as well, but perhaps not as biased as a person.

Machine learning has already helped develop new drugs that treat a rare hereditary disease that can cause children to go blind.

The disease is called Familial Exudative Vitreoretinopathy, orFEVR.It prevents the proper amount of blood from reaching the eye.

Depending on severity, it can result in poor vision or blindness, said Christopher McMaster, a Dalhousie professor of pharmacology.

McMaster's goal is to turn off a protein that prevents the arteries and veins in the eye from growing properly. The computer uses all available information to create a three-dimensional model of what that protein could look like.

"Once you have this three-dimensional picture you can then use the AI to say, 'OK,I need to stick a drug-like molecule essentially into the gears of this protein to turn it off. Give me a list of drugs, not known drugs but anything you could synthesize in a lab that we could stick into this spot that could turn it on or off,'" said McMaster.

The system has worked.McMaster and his team have created a drug that treats FEVR.

"If you were a mouse with FEVR right now we could restore your vision quite well," he said.

It will be a year or longer before McMaster files the documents to start human trials.

Doing this work without computers capable of machine learning would have been challenging as thousands, evenmillions, of drugs would need to be tested in a lab,as opposed to the computer running virtual tests, said McMaster.

"Diseases like this one that don't affect a lot of children, they'd not have any shot at a therapy whatsoever," he said. "So this has really opened up the avenue for a lot of different diseases that would never see the light of day."

That's not the only success story in the province.

Another professor at Dalhousie has helped develop a device that can quickly perform a blood test without a technician or doctor present.

Alan Fine is a professor with the faculty of medicine in the school of biomedical engineering. He's also founder of the company Alentic Microscience.

Fine developed a device, called the Prospector, that isabout the size of a debit machine and can take images of blood cells with a sensor. The machine's neural network has been taught to recognize different parts of the blood and perform a complete blood count.

That test can tell the number of red blood cells, the number of white cells, platelets and gather other information.

"It's a sort of snapshot image of the overall health of an individual and it provides clues to many different kinds of illnesses," said Fine.

In the best-case scenario, the traditional test for a complete blood count would take 20 minutes.More often it can take hours or a day to get results back, said Fine.

His device takes five minutes and is portable.

Right now, the machine is in its testing form and hasn't yet been approved for diagnostic use by Health Canada or other regulatory agencies.Fine hopes those approvals will come later this year.

"These neural network approaches, they have proven so massively effective," said Fine.

"We were very early beneficiaries of this novel computing technology. It's totally transformed the way that we do this and as I think you can see it's not just our little application, it's spreading throughout medicine."

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How machine learning is revolutionizing medical research in Nova Scotia and beyond - CBC.ca

AI and machine learning are revolutionizing modern businesses - here's how to get ahead

By Pierre Fricke- February 4, 2021

Fierce competition means every business must adapt to succeed. AI and machine learning have emerged as modern, vital ways for organizations to get ahead. Many businesses today prioritize data, analytics and AI/machine learning projects to power new business models, enhance product and service offerings, improve efficiency, drive revenue and deliver superior customer experiences.

But analyst figures on project implementation make for sobering reading. Gartner predicts that under half of modern data analytics and machine learning projects will be successfully deployed in production by 2022. Less than a fifth will move piloted AI projects into production without delays caused by a range of problems - from technical skills gaps and lack of IT/business process maturity, to insufficient organizational collaboration.

For example, these businesses may not have expertise in mathematics, algorithm design or data science and engineering. Or their data may not be in a unified data lake infrastructure for ready access. These conditions create challenges for any organization looking to advance in the market and derive value from AI and machine learning.

This combination of pressure and challenges can overwhelm your business, especially if you're at the start of your AI and machine learning journey. So let's dig into why your business should make the effort - and how doing so might require different skills sets and data from what you might think.

Let's start with the basics. When a machine completes tasks based on a set of stipulated rules that solve problems, we're into the realm of artificial intelligence. This might include understanding and interpreting natural language, recognizing when objects move and providing intelligent answers. Business benefits follow, such as analyzing data sets that are too large for humans to process, answering questions in real time that draw from existing data and experiences, and automation that can reduce costs and boost productivity.

Machine learning is a discipline within the AI domain. It enables machines to learn by themselves using data. They use this knowledge to make increasingly accurate predictions and drive actions. For this to happen, you need a model that's trained on existing data, after which point it can process additional data and make predictions. Throughout the process, it's important to track and understand your model, building quality and eliminating bias.

Finally, deep learning is a subfield of machine learning. It structures algorithms in layers to create an artificial neural network that can learn and make intelligent decisions on its own.

We've so far explored AI, machine learning and deep learning in the abstract, but in what specific ways can they benefit your business?

If you're looking to machine learning and deep learning but have concerns about your existing data, be mindful that they don't always need massive data sets. While completely new models with no data nor training do require tens of thousands to millions of data points, trained models exist that can give a project leader a head start. Even if you have just 100 or so examples for a specific use case, building on a general model's foundation could yield more accurate results than human experts would provide.

Additionally, it's worth thinking differently about hiring for the delivery of AI/machine learning enabled applications and solutions. There's an assumption you need PhD-level data scientists. Although they do add value and can be necessary in some circumstances, existing staff can often be trained in about 100 hours, building on high-school math and a year of coding experience. With modern tools on AWS or Google Cloud including AutoML, they can build the solutions you need.

In all, it's as much about changing your mindset as anything else. You must think about what AI and machine learning can bring to your business and the most effective way to achieve that, thereby keeping your company ahead. Machine learning is today driving change in thinking of data as code - where machine learning uses data to write the program, which is the output.

This methodology coupled with the tools and education I mentioned earlier set the stage for many more people collaborating to fashion a new generation of intelligent solutions that will revolutionize business for years to come.

For more information on AI and machine learning, check out our panel discussion, which dives deep into these topics. The discussion covers: toolsets and methodologies; capabilities and constraints; data, computer and expertise requirements; examples of successful applications; and how to get started.

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Rackspace Technology Inc. published this content on 04 February 2021 and is solely responsible for the information contained therein. Distributed by Public, unedited and unaltered, on 08 February 2021 22:08:06 UTC.

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Rackspace Technology : AI and machine learning are revolutionizing modern businesses here's how to get ahead - Marketscreener.com

REACH is available in award-winning Millennium System International's scheduling software product, Meevo 2, and serves thousands of beauty businesses in over 30 countries."We are thrilled to announce another Meevo 2 business building integration offering within our MeevoXchange marketplace REACH by Octopi. REACH delivers the AI-powered smart scheduling features to help keep our salons and spas booked and growing. This partnership aligns with our strategic goals for our award-winning software Meevo 2 as we continuously add value to our platform and ultimately our salon and spa customers," says CEO John Harms, Millennium Systems International.

"REACH is so special because it requires virtually no setup or upkeep as it follows your existing Meevo 2 online booking settings. REACH plays 'matchmaker' by connecting your clients that are due and overdue with open spaces in your Meevo 2 appointment book over the next few days, automatically. It has taken us years of research and development to create such successful and exciting tool that will begin to show value to your business starting on day one!" CEO Patrick Blickman, REACH by Octopi

Performance Guarantee and Affordability

The platform includes the REACH Revenue Guarantee thatensures each location will see a minimum of $600-$1400 in new booking revenue every month. There are never any contracts or commitments with REACH. Simply turn it on and let it start filling your Meevo 2 appointment book. Pricing starts at $149/month.

About REACH by OCTOPI

REACH was founded to make the client booking experience easier and far more automated for the health and beauty businesses we serve. Headquartered in Scottsdale, Arizona; REACH is built on decades of consolidated industry and channel expertise. Visitwww.octopi.com/reach

About Millennium Systems International:

Millennium Systems International has been a leading business management software for the salon, spa and wellness industry for more than three decades. The award-winning Meevo 2 platform provides a true cloud-based business management software that is HIPAA compliant and fully responsive, so users can gain complete access using any device, built by wellness and beauty veterans exclusively for the wellness and beauty industry. Visit https://www.millenniumsi.com

SOURCE Octopi

octopi.com

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REACH and Millennium Systems International Partner to offer Machine Learning Driven Booking Automation to the MeevoXchange Marketplace - PRNewswire