This is the list of top machine learning companies and machine learning consultants. These companies are using advanced data-driven, cognitive, autonomous, and other modern technology trends for the software solutions.

1. Azumo LLC:

https://azumo.com

Azumo is a leading nearshore software development services provider. We provide software engineers who build web, mobile, data, and cloud apps. Our engineers work from Latin America for our customers globally.

Services Provided by Azumo LLC:

Nearshore Software Development, IT Staffing, Virtual CTO

Key Clients: Facebook, Twitter, Discovery Channel, Zynga, and NCSoft.

United States101-250chike@azumo.comFounded: 2016$25-$49/hr. +1-415-610-7002

2. Miquido:

https://www.miquido.com

Miquido is a software design and development company that excels at building AI-powered apps and web services. With more than 9 years of experience, we’ve shaped over 110 ideas into successful solutions for music & video streaming, m-commerce, Fintech, healthcare, and other industries. We’re chosen as a partner by both startups and large-scale enterprises. As a result, 9 of 10 of our projects are coming from our satisfied clients’ referrals.

Miquido is elected as an APN Select Consulting Partner within the Amazon Web Services (AWS) Partner Network. By using the AWS cloud solutions, we guarantee services of the highest performance, cost-effectiveness, and security level to our clients. We’re also a Google-certified software house and a member of the google developer agency program. We were covered by Time and Forbes and named a Top Mobile App Development Company in the UK.

Services Provided by Miquido:

Miquido is a one-stop software development company that aims to solve business challenges with cutting-edge technology and data-driven research. One-stop because we can do it all:

• Web & Mobile Development: Frontend, Backend, Android, iOS and Cross-platform apps;
• Artificial Intelligence: Data Science, Machine Learning, Computer Vision, NLP;
• Product Design & Strategy: Product strategy, Workshops, Market research, Prototyping & usability testing, UX/UI Design

Key Clients: Skyscanner, Herbalife, Abbey Road Studios, Play, TUI, Empik, HelloFresh, Pando, Klassik Radio, HID, Nestle, Aviva, Nextbank, Lepaya, PZU, Brainly, Picniic, BNP Paribas, AXA

Poland, UK, Germany101 - 250hello@miquido.comFounded: 2011$50-$99/hr. +48 536 083 559

3. BairesDev:

https://www.bairesdev.com

We are the fastest growing Technology Solutions company in Latin America, with offices across the main cities in LATAM and the US. We have the largest applicants pool in the industry with over 145.000 Software Engineers applying to work with us each year, from which we hire only the Top 1% most talented through a rigorous staffing method. This is how we create solid teams of 100% bilingual veteran developers.

We work providing end-to-end delivery for companies of all sizes, from startups to some of the biggest companies in the world, leading projects for Google, Sirius XM, Volkswagen, Groupon, IBM & Viacom, among others. The heart of our work is Software Outsourcing, Testing and Operational Support which we provide through a service of fully managed teams that integrate seamlessly into your projects. We are a group led and guided by an Agile mentality. We are a firm founded and run by Developers. We are a company Powered by Technology and Driven by Talent.

Services Provided by BairesDev:

Software Outsourcing, Staff Augmentation,  UI/UX Design, Mobile, Quality Engineering, Software Support, Big Data, Gaming, MVP Development Services, Cloud Computing

Key Clients: SiriusXM, Google, Viacom, Groupon, Netgear, Univision, AP, Turner, IBM, Thomson Reuters, RightSide, Salesforce, Whirlpool, HP, BBVA, AdRoll, R/GA, Instructure, Leaf Group, iSeatz, EPublishing, BlackBoard, Roemmers, MuleSoft, Fresenius, Square, Kapsch, Gemalto, IPG Mediabrands

USA, Argentina, Mexico501-1000info@bairesdev.comFounded: 2009$30 – $50/hr. +54-11-5353-9840

4. AI Superior:

https://aisuperior.com

AI Superior is a service provider on Data Science, Machine Learning and AI that helps your business applying analytics to unlock the potential of your data. We provide actionable insights for your products and services as well as deploy machine learning models to transform your business. We provide rapid prototypes and end-to-end products to solve your complex business challenges.

Services Provided by AI Superior: 

AI, Machine Learning and Data Science Development: End-to-end product and solution design based on big data, machine learning, and artificial intelligence. Our experienced team will build a solution that fulfills your requirements and allows flexibility for future evolution.

Key Clients: Merck, HUK-Coburg, Finiata, Zeil7, 6Nomads, DigitAI

Germany11-50sales@aisuperior.comFounded: 2019$120/hr. +49 159 022 460 90

5. Innowise Group:

https://innowise-group.com

Innowise Group is an international full-cycle software development house bringing wise digital innovations to clients from more than 30 countries across the globe.

Founded in 2007 by a group of passionate IT enthusiasts, Innowise Group has turned into a multi-national business with key delivery centres in Eastern Europe, offices all over the world, and more than 500 top-notch IT professionals leveraging their software engineering expertise to make the businesses of our customers even more successful.

Services Provided by Innowise Group:

Development and Consulting

Key Clients:

United States251-500innowise2007@gmail.comFounded: 2007$25 – $49/hr. +1 (917) 26 777 27

6. Fively:

https://5ly.co

Filey is a full-cycle software development company. Our experienced engineers are always ready to come up with elegant software solutions for challenges of any complexity to boost your business growth and streamline workflow within your company. We develop complex enterprise solutions using web technologies such as Python, NodeJS, React.js, .NET, etc. At Fively, we also provide services for building highly efficient business automation systems and reliable cloud-based apps using AWS and Google Cloud. We guarantee support during the whole lifecycle of a project involving project management, quality assurance, product design, and business intelligence services.

Services Provided by Fively:

Web Application Development, Mobile Application Development, Cloud Application Development, Browser Extension Development, Business Prosess Automation, UI/UX Design, IT Staff Augmentation

Key Clients: Twolog, Insly, WebinarNinja, Uniqkey, Snap, Volt, Littlefund

Poland51-100info@5ly.coFounded: 2018$25-$49/hr. +48571937272

7. Vention:

https://ventionteams.com

Vention is the premier global leader in software engineering, synonymous with technology designed for scale and the common denominator behind the world’s most successful tech-empowered enterprises, industry innovators, and startups. Headquartered in New York with 20+ offices, Vention provides access to 3,000+ engineers worldwide and equips technology leaders with the top engineering talent from the world’s most respected tech hubs. Our teams sync with clients’ in-house engineers to advise and execute their product vision to accelerate their roadmap, innovate faster and more efficiently, and ultimately scale their operations to new heights.

Services Provided by Vention:

Custom Software Development, Mobile App Development, Web Development, AI Development, AR/VR Development, Blockchain, IT Staff Augmentation

Key Clients: IBM, PayPal, IC Markets, StoneX, pwc, EY, ClassPass, DealCloud, Postman, Bevi, Thirty Madison, Costa Coffee, Mount Sinai, Médecins sans frontières, Motum, Glassdoor

USA, Germany, UK1000+hello@ventionteams.comFounded: 2002$25-$49/hr. +1-718-374-5043

8. Diffco:

https://diffco.us

Our team specializes in transforming your vision into a robust mobile application or an AI-enhanced solution for enterprise. With years of experience in the industry, a deep understanding of various markets and technical expertise our team brings out truly valuable products to our clients.

At Diffco we not only develop mobile apps and AI solutions, we offer a deeper dive into project planning from both business and software perspectives. Our goal is to ensure your product reaches the highest productivity results and changes the lives of your customers for the better.

Services Provided by Diffco:

Our workflow and solutions are specifically designed to meet the needs of enterprise clients. Diffco team consists of experienced senior developers and project managers, who are ready to solve client’s requests in the most effective and convenient way. We believe that collaboration is key to any project success. From the moment of initial discussion to the launch, we put the client’s needs first and share the project as if we’re the same team. Once the development stage is completed, we provide continuous support.

Key Clients: FREDD, ORB Intelligence (Acquired by Dun & Bradstreet), L.E. Solutions, Mi3 Security (Acquired by ZIMPERIUM), NCRIC, FOLA, Genius One, FinalPrice, PHEW, QuestRoom, Aerostarter, Clovitek, Nokia care, Centerlight, GLFG, ORB Intelligence.

United Stated11-50media@diffco.usFounded: 2008$50 – $99/hr. +1-415-655-1002

9. Konstant Infosolutions:

https://www.konstantinfo.com

Konstant Infosolutions is a premier software development company, offering a wide range of web and mobile solutions across the globe since 2003. The company has a team of 180+ highly qualified and experienced IT professionals who can deliver best of technology solutions and consulting services across diverse business needs.

Services Provided by Konstant Infosolutions:

As a leading software development company, Konstant provides reliable web and mobile solutions spanning across Native Mobile Apps Development, Cross-Platform Mobile App Development, Custom Web Development, UI/UX Design Solutions, Artificial Intelligence, AR/VR Development, E-Commerce Solutions, Database Programming, CMS Development, Enterprise Mobility Solutions, Cloud Consulting and IT Consulting – offering businesses delightfully tailored and trendy solutions in their budget.

Key Clients: United Nations, Wonder Cement, Volkswagen, Stanley, Citrix, Project Action Star, RawBank, Houghton Mifflin Harcourt, ThomasVille, Scholastic, Nestle, NASSCOM, TABCO.Food and more.

Client’s Review:

“The team provided excellent communication and updates in a timely manner. Deliveries were on time and prompt with their responses.Their cost-effectiveness, timely deliveries, and communications were impressive.” – Yong Kim

USA, India, UAE101 - 250sales@konstantinfo.comFounded: 2003<$25/hr. +1-310-933-5465

10. Yalantis:

https://yalantis.com

Yalatis is a trusted software engineering and IT consulting company from Ukraine that was founded in 2008. The Yalantis team of more than 300 specialists builds custom software that allows businesses to meet their needs and work within their constraints. As a strategic partner, Yalantis helps clients digitally transform to improve their products, speed up service delivery, increase operational efficiency, and expand to new markets.

Services provided by Yalantis:

Custom software development, Development teams augmentation, IT consulting and digital advisory, Software re-engineering and support

Key Clients: Zillow, Healthfully, KPMG

Ukraine251-500hello@yalantis.comFounded: 2008$35/hr. +1-213-4019311

More Industry Leaders:

11. InData Labs:

https://indatalabs.com

InData Labs is a high-quality provider of Big Data solutions and AI software development services that was founded in 2014. The company has an extensive set of competencies and proven experience in developing complex solutions that meet Clients’ needs and requirements and allow them to automate business processes, add AI-powered features, and gain competitive advantage.

The company plays great attention to communication with its customers via the ability to talk to any team member directly, and constant coordination throughout each development stage. Thus, projects meet both customers’ requirements and engineers’ recommendations.

Services Provided by InData Labs:

Big data development/consulting, AI software development, data analysis, BI implementation, predictive analytics, machine learning (ML), intelligent automation, computer vision.

The company has implemented more than 40 successful projects worldwide to Clients from different industries.

Key Clients: Captiv8, Wargaming, FLO, Skorebee

Belarus, Cyprus, USA51 - 100info@indatalabs.comFounded: 2014$50 – $99/hr. +375-29-199-16-44

12. Avenga:

https://www.avenga.com

Avenga is a global IT and digital transformation champion, with over 20 years of experience. We understand the complexities of modern markets and translate them into real business solutions for verticals such as pharma & life science, insurance and finance. We are more than 2500 professionals with offices in Europe, the USA and Asia, along with strong financial backing to further expand internationally. Our clients, like Roche, GSK, Credit Suisse, Postbank, ABB, Allianz, SwissLife, Mazda, Volvo, and Olo, trust us with customer centricity and the readiness to go the extra mile to deliver solutions that work. With our mission to shake up the conventional IT market, we consult, design, engineer and deliver real-world reliable solutions with fast results.

Services Provided by Avenga:

Digital Strategy, Customer Experience, Solution Engineering, Managed Services, Staffing as a Service

Key Clients: Roche, Liechtensteinische Ladesbank, GSK, QPharma, PZU, Credit Suisse, Postbank, ABB, Allianz, Trov, SwissLife, HDI, NovoFerm, Opel, Mazda, Volvo, Olo

USA, Germany, Ukraine1000+hello@avenga.comFounded: 2019$25 – $49/hr. +490221 846 300

13. AppInventiv:

https://appinventiv.com

Appinventiv is one of the top-notch app development company with headquarter in Noida (India) and a branch office in Dubai. Since its inception, it has delivered 350+ ‘top-grossing’ applications to clients globally and has set a new benchmark in the app market with its innovations and services.

In last 2 years, we have gained strong exposure to different industries such as Real Estate, Healthcare, Finance, Education, Enterprise, and Travel to name a few. With our market experience and cutting-edge technologies, we have catered the needs of different startups and well-established firms with result-oriented, top-quality and reliable mobility solutions. We have proven our expertise in Android app development, iOS app development, Chatbot development, Wearable app development, Beacon app development, and IoT development, and are continuously ranking among the top app development companies on AppFutura and Clutch.

Services provided by AppInventiv:

We have expertise in following fields:

Native app development (Android, iPhone, iPad), Cross-platform app development, Web app development, Wearable app development, IoT development, Beacon app development, Chatbot development

India, United States251 – 500sales@appinventiv.comFounded: 2014$25/hr. +91 882-690-9998

14. HatchWorks:

https://hatchworks.com

HatchWorks is your US-based Nearshore software development partner combining Generative-Driven Development™ with the affordability and scale of Nearshore outsourcing. Built from the ground up, our Latin American teams have a 98.5% retention rate, ensuring no project disruptions for our clients. Headquartered in Atlanta with a network of eight offices across six countries, our teams are English-fluent and located in US time zones, enabling improved collaboration and outcomes. Our proven Generative-Driven Development increases speed to value and reduces your cost throughout the software development lifecycle so you achieve your desired outcome faster.

Services Provided by HatchWorks:

Staff Augmentation, Dedicated Agile Teams, Outcome-Based Projects, Product Fundamentals, Product Design, Software Development, Data & Analytics, Technology Consulting, Nearshore Software Development

Key Clients: AT&T, Diebold Nixdorf, FleetCor, Anthem, Cox, Charter, Viasat, PeopleReady, PeopleScout, Capital Choice, Integrated Care Solutions, Tevora

United States101-250connect@hatchworks.comFounded: 2017$50-$99/hr. +1-406-690-7182

15. Edvantis:

https://www.edvantis.com

Edvantis is a mature, value-oriented software development partner with the HQ in Berlin, Germany, and the development centers in Eastern Europe. Our goal is to assist you in efficiently achieving your software product development goals within your timeline, budget, and quality standards. By partnering with us, you benefit from complete transparency, top talent, established processes, and long-term commitment to every client.

Services Provided by Edvantis:

IT Consulting, Engineering: Software & Hardware, Capacity: Managed Team & Staff Augmentation, Business Process Outsourcing, Digital Transformation

Key Clients: TESTCo, BigCommerce, Doc Cirrus, Modulsystem, KPCLabs, SEMDATEX, Promenade Group, Kardex Remstar, Collenda

Germany, Poland, Ukraine251-500marketing@edvantis.comFounded: 2005$50-$99/hr. +380677624449

16. Altar.io:

https://altar.io

Altar.io is an award-winning product & software agency based in Lisbon, London and Milan. We build digital products for entrepreneurs and business leaders who want to revolutionize their industries. We’ve been described as an “extended team of co-founders” because we’re very passionate and rigorous about all the work we do.

Services Provided by Altar.io:

Custom Software Development, Web and Mobile Development, Big Data Analysis, AI Applications

Key Clients: Apiax, AlixPartners, QuartalFS, Audio Test Kitchen, McKinsey & Co., Legartis

Portugal, Italy, UK11-50rui.lou@altar.ioFounded: 2015$100/hr +351 919359369

17. tapptitude:

https://tapptitude.com

tapptitude is a mobile app development company specialized in building high-quality iOS and Android mobile apps, for startups and brands alike. A lively team of skilled app developers and app designers based in Europe, we provide full-stack mobile app development services to entrepreneurs looking to innovate on mobile.

From product strategy, UI/UX app design, native development on iOS and Android, to back-end and API services, along with testing and go-to market solutions, we cover the entire flow of taking an idea and launching it in the market as a fully functioning mobile product. We are a mobile app development agency that focuses on a clean code architecture and an intuitive design for every iOS and Android app we work on. What sets us apart is our can-do attitude about the mobile projects we take up, our enthusiasm for new challenges, and our expert-level mobile app development services. We get what makes people and mobile apps click.

Services provided by tapptitude:

Product & Market Strategy, UI/UX Design, iOS & Android App Development, Mobile App QA & Testing, App Marketing.

UK, USA, Romania10 - 49contact@tapptitude.comFounded: 2013$25 - $49/hr. +40 743 142 383

18. Fingent:

https://www.fingent.com

Fingent has been in the IT software services industry since 2003 and we are a reliable and affordable Web and Mobile Development company for enterprise and mid-sized organizations.

We are a full service web and mobile development services provider with two offices in the United States (Boston and New York), India and United Arab Emirates. Our global team of over 230 talented full-time employees have helped hundreds of mid and large size organizations implement software solutions that increase productivity and profits.

Services provided by Fingent:

Web Application Development, Mobile Application Development, Product Development (SaaS) and Enterprise Software Development. We will help you solve your business challenges through software in any technology area.

United States100 – 249info@fingent.comFounded: 2003< $30/hr. +1-914-615-9170

 

19. Dotsquares:

https://www.dotsquares.com

Founded in 2002, Dotsquares is a renowned IT service provider having a team of 650+ in-house resources. Dotsquares team has worked on 10,000+ projects for fortune 500 companies, SMBs, and start-ups related to almost all the Industries and business models. It provides state-of-the-art solutions to help clients generate maximum out of their idea & investments.

Headquartered in the UK, Dotsquares has manned offices in USA, Australia, Dubai and three development centers in India. It is ISO 9001 Certified and partnered with many reputed firms such as Google Partners, Microsoft reg. partner, NASSCOM Member, Salesforce Partner and IAOP.

Services Provided by Dotsquares:

Native Mobile Apps Development, Cross-Platform Mobile Apps Development, Block Chain, IoT, Augmented Reality & Virtual Reality and hosting are few among the long list of its services.

Key Clients: Kenwood, Bose, NHS (National Health Service), Ericsson, Daily-News, Beatthebrochure, Conservatives, Travelsoon

India, UK, USA501-1000office@dotsquares.comFounded: 2002< $25 / hr. +44-127-357-5190

 

20. Hyena Information Technologies:

https://www.hyena.ai

Hyena Information Technologies is the best software development services and solutions provider specializing in offering Mobile App Development, AI development, cloud migration, and IoT development services. We help businesses accelerate their digital transformation by deploying our performance-driven digital tech solutions.

Services Provided by Hyena Information Technologies:

Mobile App Development, Artificial Intelligence, Machine Learning, Android App Development, RPA, Big data, HR Management, Workforce Management, IoT, IOS App Development, Cloud Migration

Key Clients:

USA, India, UAE501-1000sales@hyena.aiFounded: 1999$25-$49/hr. +91-70326-30855

 

The top-notch Artificial Intelligence Companies are using best tech practices, vast industrial experience, and professional expertise to deliver innovative consultancies and solutions to drive growth. Also, The experts using machine learning to alter the procedures and build robust services across different industries. With the skilled and trending tech-enabled solutions, these are extending limits for the growth of businesses.

The post Hyena Information Technologies appeared first on Top ITFirms - Result of In-depth Research & Analysis.

“This blog got a curated list of the best machine learning app ideas to help you take advantage of this rapidly growing technology.”

How does Spotify recommend great songs to you based on your preferences? The answer is that Spotify is using machine learning to learn to evaluate user preferences.

According to a McKinsey survey, 56% of companies like Facebook, Twitter, Google, Amazon, etc. use machine learning to deliver personalized content and engage customers.

With machine learning entering the mobile app development sphere, innovative mobile-centric solutions are becoming more accessible.

A rise in the use of machine learning-based applications is on the horizon. ML is anticipated to reach 126 billion dollars in market value by 2025, up from 22.6 billion dollars. (Statista)

Are you interested in tapping into the burgeoning market for machine learning? This blog suggests the machine learning app ideas that can be implemented to ensure a profitable startup.

So, let’s get started.

Top Machine Learning App Ideas

Here goes the list of the best machine learning app ideas:

1. Fleet Management Application Using ML

Nowadays, every industry uses machine learning to reduce human errors and increase efficiency in their operations.

Software for fleet management is rapidly growing in the global market. According to a study by Global Newswire, revenue will climb from USD 18.20 billion in 2021 to USD 67.38 billion by 2029 registering a CAGR of 18.3%.

The need for intelligent fleet management mobile apps is rising, so it is time to invest in these tools. You can extend the fleet management software’s capabilities to their fullest extent using machine learning. Fleet vehicle management has proven to be more efficient and safer when using machine learning. By automating operational processes, fleet management becomes straightforward and more uncomplicated. Manual processes made fleet management tedious and challenging.

2. Personalized Chatbots for eCommerce

According to multiple surveys, the usage of chatbots can increase sales by an average of 67%. These AI-powered chatbots can make your business available to your customers 24*7.

These chatbots can listen to the user’s queries and respond to them with the appropriate answers to increase sales and grow revenue. Companies using chatbots can optimize their customer’s journeys.

Driven by machine learning technology, chatbots can develop human intelligence to better serve the customer. By analyzing the customer’s behavior using ML, these chatbots can personalize customers’ journeys to improve their chances of turning into buyers.

3. Financial Fraud Detection App

Machine learning technology can also be proven helpful in detecting fraudulent transactions in the financial system.

Over 2.8 million consumers reported financial fraud in 2021, which cost them an estimated $5.8 billion, a 70% increase, according to a report released by the Federal Trade Commission.

A fraud detection app powered by machine learning can analyze users’ spending habits and use the pattern to find similarities between transactions. An alert can be sent to users when an anomaly is detected so that they can take action to prevent fraudulent transactions from taking place.

4. Machine Learning Powered Content Generation Tools

A marketing campaign is only complete with content. It is an irreplaceable asset. With this in mind, organizations are investing in tools that help them create user-centric brands that adhere to their industry standards.

According to Future Market Insights, the content creation market was valued at USD 13.4 billion in 2022, growing with a CAGR of 12.2%, the market is expected to reach a value of USD 47.2 billion by 2032.

As the demand for content-generation tools continues to grow, you can develop a machine-learning-based content-generation app that can analyze existing data and generate content tailored to the user’s needs.

5. Customer Sentiment Analysis App

It is becoming increasingly important to analyze customer sentiment to better understand your customers and to ensure your services are engaging to them.

Using machine learning, businesses will be able to analyze public opinion, improve customer support, and automate tasks with rapid turnaround times, which will enable them to grow and take advantage of economic opportunities.

The market for global customer sentiment analysis tools is growing at a rapid pace. According to Polaris Market Research, the market for such tools is expected to reach USD 3.15 billion in 2021 and grow at 14.4% CAGR over the upcoming years.

6. Machine Learning Powered Weather Forecast Apps

In today’s world, weather forecasting applications are becoming increasingly common. By 2028, the weather app market is expected to reach $1.4 billion, representing a CAGR of 9.2%, according to the business wire.

The goal of accuracy can be achieved by weather forecast apps using machine learning. With machine learning, an accurate forecast of weather can be determined in the preferred area using the preferred locality.

7. Geofencing App

Geofencing involves the process of triggering an action whenever a device enters or exits a predefined area.

These apps can be made more accurate with the use of machine learning and trigger action right on time. Geofencing apps can be used to serve a variety of purposes such as kids monitoring, location-based marketing, and so on.

8. Machine Learning Based Recruitment App

Recruiting online is one of the most captive markets in the world. According to Global Newswire, the online recruitment market is expected to reach $47.31 billion by 2028 up from $29.29 billion in 2021 with a CAGR of 7.1%.

Considering the growing market for machine-learning-based apps, it is the right time to tap into it. ML can be used to streamline the process of the hiring process to make sure the best candidate is chosen. Such types of apps can screen the resume of the candidate and filter those resumes that best adhere to the organization’s requirements.

9. Portfolio Management App

The market for portfolio management apps was estimated at USD 4.7 billion in 2022, with a CAGR of 6.7%, the market is expected to reach USD 6.7 billion by 2027. (Source: Markets and Markets)

With portfolio management apps gaining popularity, it has proven to be a profitable business venture. Using machine learning, these apps can build a strong portfolio and generate more accurate financial information by analyzing the data.

10. Customer Retention Analysis App

Business owners face a great deal of difficulty retaining existing customers. Achieving revenue growth while not increasing costs is challenging when you do not understand why customers stop supporting you.

Customer retention analysis apps can be proven to be helpful in these scenarios. Thanks to machine learning technology, these apps can identify user behavior, thus making it easier for organizations to find out what strategies they need to fine-tune to improve the customer retention rate.

11. ML Powered Education App

The education sector can benefit greatly from machine learning technology. Educators will be able to track student performance and spot problems more effectively with this technology.

Using these applications, which collect, measure, and analyze data about students, you can analyze student behavior. Thus they can create a personalized learning experience for the students.

Activity Type

Also a smartwatch fitness tracker app is capable of tracking many different types of activities: Walking, jogging, aerobics, American football, Australian football, backcountry skiing, badminton, baseball, basketball, beach volleyball, biathlon, boxing, calisthenics, circuit training, cricket, cross skating, cross country skiing, crossfit, curling, cycling, diving, downhill skiing, elliptical, ergometer, fencing, fitness walking, flossing, football, frisbee, gardening, handball, hand-cycling, high-intensity interval training, hiking, hockey, horse riding, ice skating, indoor skating, indoor volleyball, inline skating, interval training, jogging, kayaking, kettlebell, kick scooter, kickboxing, kite skiing, kitesurfing, martial arts, meditating, mixed martial arts, mountain biking, nordic walking, open water swimming, other, P90x, paced walking, paragliding, pilates, polo, pool swimming, pushchair walking, Racquetball, road biking, rock climbing, roller skiing, rowing, rowing machine, rugby, running, sailing, sand running, scuba diving, skateboarding, skating, skiing, skipping rope, sledding, snowboarding, snowshoeing, softball, spinning, squash, stair climbing, stand up paddle boarding, stationary biking, surfing, swimming, table tennis, treadmill, walking, volleyball, wakeboarding, water poplo, zumba, windsurfing, yoga, that’s it.

Applications like Accuweather, have inbuilt sensors that effectively predict hourly, daily, radar, minute-cast monthly forecast, air quality, and health and activities that directly or indirectly affect a person’s daily routine.

Final Take: How to Bring These Machine Learning App Ideas Into Reality?

The field of machine learning (ML) is one of the most powerful subsets of artificial intelligence. Through ML, machines can be made smarter to perform tasks by strengthening their learning capacity.

Above we have explained a few of the best machine-learning app ideas that can be implemented with machine-learning technology. The best practice to implement these ideas is to partner with the best machine learning development companies that are driven by the rich expertise in using this technology to steer smart solutions.

Frequently Asked Questions

What are machine learning models?

The process of finding the best ML app ideas starts with learning the potential of machine learning. It is then necessary to figure out how these capabilities can be incorporated into an application to solve challenges.

How to find the best machine learning app ideas?

The process of finding the best ML app ideas starts with learning the potential of machine learning. It is then necessary to figure out how these capabilities can be incorporated into an application to solve challenges.

How much does it cost to develop a machine learning-based mobile application?

The cost ML app idea is subject to multiple variables such as complexity, and feature. ML app development company region and so forth. Considering that these variables vary from project to project, it is not possible to estimate the cost until you know your requirements.

The post 2023 Vision: Machine Learning App Ideas to Get Ahead of the Curve Now appeared first on Top ITFirms - Result of In-depth Research & Analysis.

“Here’s a precise study on the coalescence of data analytics and AI in the supply chain and the reasons for its widespread use by enterprises and large-scale organizations!”

A supply chain is a network of resources, organizations, individuals, activities, and technologies involved in the creation and sale of a product. It includes the delivery of source materials from the supplier to the manufacturer through delivery to the end user.

The Problem Statement

Think about augmenting your manufacturing, production, packaging, supply, storage, transportation, and logistics departments with a high level of transparency and security. Make your supply chain future-ready by introducing indispensable techniques that honour the need of the hour and optimize the business fully.

The Solution: What are the applications of ML and AI for SCM?

Machine learning is a subset of artificial intelligence that allows an algorithm, software or system to learn and adjust without being specifically programmed to do so. Integrating machine learning in supply chain management can help automate a number of mundane tasks and allow enterprises to focus on more strategic and impactful business activities.

AI helps examine warehouse processes and optimize the sending, receiving, storing, picking and management of individual products. It helps check and ensure the right distribution channels to get goods to retailers and other customers.

AI in Supply Chain Management and Logistics Examples

• Coupa
• Echo Global Logistics
• Zebra Technologies
• LivePerson
• Epicor
• Infor
• Covariant
• Symbotic
• C3 AI
• HAVI

What challenges in the supply chain can machine learning solves?

According to inbound logistics, AI/ML transformations undertaken by manufacturing companies will lead to productivity gains of more than 20% across the supply chain, by 2024. Companies using machine learning to improve their supply chain management are Amazon, Microsoft, Rolls Royce, Alphabet, and P&G.

Improving the efficiency of the supply chain plays a crucial role in any enterprise. Operating their businesses within tough profit margins, any kind of process improvements can have a great impact on the bottom line profit.

Gartner predicts that at least 50% of global companies in supply chain operations would be using AI and ML-related transformational technologies by 2023.

A few challenges that ML resolves in SCM:

• Poor resource planning
• Inefficient supplier relationship management
• Satisfying customer needs
• Quality and safety
• Technical downtimes
• Cost Inefficiency
• determining Pricing
• Transportation Costs

What are the top use cases of machine learning in the supply chain?

• Predictive Analytics
• Automated Quality Inspections
• Real-Time visibility
• Streamlining Production Planning
• Reduces Cost and Response Times
• warehouse Management
• Reduction in Forecast Errors
• Advanced Last Mile Tracking
• Fraud Prevention

Which benefits does machine learning deliver to supply chain management?

Here are some examples of frequently outsourced IT services:

• Cost efficiency drives waste reduction and quality improvement.
• Optimization of product flow, hence lesser inventory.
• Seamless supplier relationship management due to simpler, faster and proven administrative practices.
• Derive actionable insights, allowing for quick problem-solving, and continual improvement.

What is supply chain management and how does AI help in the process?

AI-based supply chain optimization software overstates cognitive predictions and recommendations on optimal actions, helping manufacturers with potential implications across time, cost, and revenue.

AI was developed to create machines to imitate human intelligence and improve human decision-making. Any machine that comes into being automates the manual process, reduces the chances of errors, speeds up the process, and improves the overall performance of the system.

AI has been widely used as a decision-aid tool but has seen limited application in supply chain management (SCM).

Which sub-fields of AI help in solving supply chain management problems?

Can man-made reasoning function in this ever-advancing world of work and constant innovation? Along with deep learning the three types of AI – Managed learning, unaided learning, and fortification learning have a significant impact on supply chain management. It is inclusive of inventory management, warehouse management, and logistics. With the penetration of computers in supply chain management, e-commerce platforms like Amazon are utilizing computerized reasoning for web-based shopping and have automated its warehouse. Other similar e-stores are using machine learning to improve the management and predictability of their supply chain.

Right since 1980’s, supply chain has evolved from being product driven to customer driven till date.

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SCM activities include logistics and planning, finance, procurement and inventory management, sales and customer service, quality assurance, and operations utilizing computerized machines and gear to save time and reduce error. It examines weather, and traffic, and predicts the future on the basis of feedback from customers. AI can help in designing and promoting products, catalyze the mechanical process including transport and logistics to improve accuracy, reduce human labour costs and decrease lead times.

IIoT runs smart industry manufacturing to drive the entire supply chain without any manual participation. AI in the supply chain with advanced sensors and facial recognition capabilities helps companies improve supply chain visibility and security. AI applications in the supply chain need vast amounts of data to feed systems and meet the diversified needs of supply chain operators.

What are the advantages of artificial intelligence in supply chain management?

Streamlining Process – Using data and trends, AI can streamline every aspect from demand to inventory to supply with minimal human input. It saves time and reduces errors. Example: Chat Box, IBM Watson for operational procurement.
Precision Planning – With AI, analyzing the infinite source of data at once. It helps examine information like weather, and traffic to improve demand and supply. Example – Machine learning using Robots, Smart Drones or the Internet of Things (IoT)
Market Shaping – AI products can help in designing and improving the products, easily access data reports along with future predictions and feedback from customers. Example: Convergence of AI and Blockchain.
Faster Transport – AI also catalyzes mechanical processes like transportation and logistics, improves accuracy, reduces human labour costs, and decreases lead times. Example: Autonomous Vehicle.

What are the prerequisites of artificial intelligence in supply chain management?

Some pre-essentials of viably actualizing AI into the supply chain give standard outcomes if pre-requisites are:

• Constant information
• Multi-basis information
• Shopper-driven targets
• Expense of progress
• Identity check process
• Dynamic and accessible devices
• Operator artificial intelligence collaboration

By far Amazon E-Commerce Marketplace has been voraciously making use of artificial intelligence in managing their supply chain. It figures administration, client gadgets, computerized text, and nearby administrations in goods and day-by-day purchasing.

How do AI and Analytics optimize the supply chain?

Enterprises that make use of AI in the supply chain are more instrumented with the machine-generated data flowing out of IoT devices. They are more intelligent as they are able to competitively assume with help of data analytics and modelling. Such organizations are more interconnected with extensive connectivity for better decision making.

Widespread supply chain data helps optimize the workflow to provide forecasting, identify inefficiencies and drive innovation. The coalescence of these is called supply chain analytics. It is basically of four types:

(1) Predictive Analytics – to predict the future outcome and mitigate risks,
(2) Descriptive Analytics – to provide visibility of all kinds of internal and external data across supply chain management,
(3) Prescriptive Analytics – It involves collaborating with logistics partners, reduce time and maximize business value,
(4) Cognitive Analytics – To improve customer experience. The feedback received through AI-driven systems is used to answer complex queries. Moreover, complex AI and Analytics systems offer breakthrough ideas and provide better customer needs and demands.

Expressible Takeaways

We aimed to examine the influence of artificial intelligence (AI) in the modern world, with respect to supply chain management (SCM). We also aimed to identify how AI applies to supply chain management. AI has created a value chain for supply chain management and persuades corporate revenue growth and cost savings in modern organizations.

There is no one trick that pays off, but myriad options to go by. The use of virtual assistants, chatbots, predictive capabilities, operational costs, data collection, and inventory management all improve with the adoption of AI technologies. Besides the use of automated warehouses, data collection, and inventory processes are being improved with the adoption of AI technologies.

Organizations are making use of genetic algorithms to strengthen their logistics process, improve delivery times and reduce costs. AI streamlines every aspect of demand from inventory to supply with minimal human involvement. It is also effective in precision planning, makes transportation faster, and provides access to real-time data and multi-source data used by business organizations. The automation of warehouses is the key to success of supply chain management in organizations.

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We’re enclosing the Features, Benefits, Steps and Technology Stack of an ML-based MVP in brief!

Artificial Intelligence zeroes down to machine learning. It is one way to hunt patterns and anomalies in human behaviour, understand customer behaviour, and check their past purchase history, preferences, and satisfaction levels.

Popular examples include: How Facebook automatically tags you on your friend’s photo? Or why does Spotify replenish its “Discover Weekly’s”?

AI has become so general that we don’t realize that we are making use of it all the time. Google search is able to give accurate search results with long tail keywords; Alexa; Siri and Facebook feed gives content based on human interest.

Machine Learning vs. Deep Learning vs. Artificial Intelligence

But Machine learning, deep learning, artificial intelligence, and data science are all different, yet interconnected. Machine learning and deep learning aids artificial intelligence by providing a set of algorithms and neural networks to solve data-driven problems.

Artificial Intelligence (AI) is the science of getting machines to mimic the behaviour of humans.
Machine Learning (ML) is a subset of AI that focuses on getting machines to make decisions by feeding them data.
Deep Learning (DL) is a subset of machine learning that uses the concept of neural networks to solve complex problems.

AI covers a vast domain including natural language processing, object detection, expert system, robotics, and computer system. They can be structured along three evolutionary stages – Artificial Narrow Intelligence, Artificial General Intelligence, and Artificial Super Intelligence.

Artificial Narrow Intelligence (ANI) also known as weak AI involves applying AI only to specific tasks. Most of the systems that claim to use AI are based on weak AI. For example – Alexa operates within a set of predefined functions. There is no genuine intelligence or self-awareness. Apple iPhone faces verification, and Autopilot features at Tesla, the social humanoid Sophia (built at Hanson Robotics), and finding the optimal path through Google Maps.

Artificial General Intelligence also known as strong AI, involves machines that possess the ability to perform any intellectual task that a human being can. Machines don’t possess human-like abilities. They are not yet capable of thinking and reasoning like a human. Stephan Hawkings warned that “Strong AI would take off on its own, and re-design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete and would be superseded.”

There has been a rapid expansion of AI/ML in medical applications, finance applications, logistic/supply chain applications, customer-facing applications, metaverse, gaming applications, data science assets and investments, IBM Watson, Twitter, Google Assistant, and Tesla Cars. Machine Learning Systems scrutinize data, learn from that data, and make decisions. Examples: NFT Streaming, Snapchat’s Filters, Google Maps, Uber and Lyft, Financial Applications, Dango, Healthcare, robotics, marketing, and business analytics.

How to build a machine learning app?

The simplest way to harness the potential of machine learning in mobile applications is by making use of ready-made ML services from Apple like Core ML, and ready-made ML services from Google like ML Kit or Firebase ML. You have the option to keep all the functionality in the cloud, or to keep all the functionality in the mobile app.

To start with, design the prototype and verify it. Code and test your MVP. Deploy and maintain the app. Let’s look into it in detail:

Define the problem: What do you wish to achieve with your ML-based app? How will it benefit the customers? Can it solve any task without using ML? If you are creating an AI-based chatbot app with well-defined options where all options are already coded, it won’t touch the AI aspect. But it won’t fulfill much of your business needs either, with all the regular algorithms. It will be incapable of dealing with any unknown challenges.

While sketching the outline of your AI project, consider any challenges that come through. What will happen when ML algorithms no longer work to rectify a specific situation? Do you have enough data to train a sustainable model? Will your ML models evolve over time?

Align the right professionals who are skilled and experienced in business, development, and testing.

Secure powerful servers with ML infrastructure, data analysis, cloud hosting with APIs, on-device SDKs or custom libraries, or a hybrid approach.

You need to discern if your target audience uses old devices, slower phones or networks. It needs to train the data non-stop in real-time. ML developers need to check if the ML model is too large to fit on a mobile device. ML models may require more than 100MB of disk space, making downloads less likely to happen. Your ML AI model should combine with other data pulled from third-party APIs.

If you are building an AI App like Netflix, you would want to fetch information from IMDB about your reviews and ratings, to serve more movie recommendations, solely based on your activity in the Netflix app.

Technology Stack for Machine Learning App

When you create a machine learning app from scratch, you may use a custom solution to align off-the-shelf machine learning components. This approach can be more flexible. But using canned ML services can remove a lot of burdens associated with ML functionality. Decipher the frameworks, technologies, tools, and development environment at hand to create ML apps. Python is the most frequently used programming language that ML development companies use in AI App Development. Use NLTK, Scikit – learn AI/ML Library. Use TensorFlow, PyTorch, Caffe, Scikit, Keras, Pandas, Numpy, and MXNet – ML Frameworks. In detail:

• Programming Languages: Python, C++, Golang, R, SQL, Java/Scala
• Frameworks: TensorFlow, PyTorch, MXNet, Caffe2, Keras, SciKit
• Data Warehousing: Hadoop, Spark, Ray, BigQuery, Redshift, Snowflake
• Libraries: Pandas, NumPy, DeskML, Keras, NLTK, SciPy
• Cloud Services: AWS SageMaker, Google Cloud AI Platform, Azure ML Studio, IBM Watson, Azure DataBricks, AWS Glue
• DevOps: CometML, Kuberflow, MLFlow, HopsWorks, Docker
• Tools: Jupiter Notebooks, Google Colab, Airflow, Google Data Lab, Hevo, SQL Alchemy

Besides the steps that we followed here, a slight diversion will allow you to host the code of your project as a Jupiter Notebook. You can also build a web app for your project. You can make your code publically available by making use of Github, or Jupyter Notebook Viewer. The project will have to be downloaded and run on the notebook on the local machine or run notebook online in Jupyter Notebook Viewer. You can also make use of HTML or JavaScript and Flask to build a web app around the data science project and then run the project through the app. To avoid the intricate details of HTML, CSS, and Javascript, enter Stremlit. You may use Tweepy as a Python wrapper, Vader sentiment as a rule-based sentiment analysis library, and deploy on Heroku.

Conclusive

Access the URL and check the app. It becomes a lot easier to deploy the ML App with Streamlit and Heroku at absolutely no cost. Streamlit is also deployable on AWS, Google Cloud or any other cloud app. That was a relatively easier implementation of ML Apps in AI. Acquiring AI Skills like AIOps can simplify the process further. Follow up with ITFirms and application development companies for the best AI Blogs, most in-demand AI skills, how AI and ML are emerging etc.

Related Topic: Selecting Top AI Developers: A Comprehensive Guide

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AI and ML are increasingly gaining traction with their ability to integrate with technologies, which is the reason why app development companies are reaping best benefits by its usage!

Machine Learning, AI and neural networks have so much to offer, with their idiosyncrasies in features that top AI companies need to figure out best deals from the roaster of offerings. As technology seeps into our daily lives, it affects the way we live, work, get entertained, eat, search, and do various other things. It has elevated from voice-powered personal assistants like Siri and Alexa to more complex underlying technologies involving suggestive searches, behavioral algorithms, applications with predictive capabilities, autonomously-powered self-driving vehicles, and many more general-purpose applications like Tesla, Cogito, Boxever, John Paul, Amazon.com, Netflix, Pandora, Nest, etc.

A true-artificial intelligent system is capable of learning on its own. These are from the likes of neural networks like Google’s DeepMind capable of making connections and reach meanings without relying on pre-defined behavioral algorithms. Implementation of AI into mobile applications can improve past iterations, letting users get smarter, becoming more aware, and allowing to enhance its capabilities and its knowledge.

It’s arduous to think about an application without a database in the present-day context. Every sort of application – be it mobile, web or desktop relies on some kind of database. Irrespective of the file structure, database, whether small or large is an essential part of every mobile app. 

Basic Steps to Implement AI into Mobile App

Every mobile application will depend upon some form of Artificial Intelligence (AI) in coming times. AI can be enabled in enterprise applications in three basic ways: 

STEP 1: Most prevalent and non-disruptive way to get started with AI is to integrate API’s into existing applications by language understanding, image pattern recognition, speech to text, text to speech, natural language processing, video search API’s, etc.

An example is the random sampling of all the inbound calls received by customer care representatives within the customer care cell. A supervisor routinely listens to all the calls to check the quality and overall satisfaction level of the customers. With limited time to analyze each call, the supervisor is normally left with very less time to assess the quality of calls, escalate the incidents to respective departments and handle unhappy customers as well as rude call-center agents. This scenario prevails in all industry verticals like banking, finance, insurance, online shopping malls, etc. Implementation of chat-bots (AI enabled voice-assistants) tremendously help customers in getting the desired solution within the stipulated time. Multiple AI platforms expose simple API at affordable price points.

• Amazon AI Services
• Google Cloud ML Services
• IBM Watson Services
• Microsoft Cognitive Services
• Vize.it
• Algorithima
• Clarifai
• AIception
• Lexalytics

STEP 2: Integrating API in enterprise applications can be a decent start of making use of AI into the applications, but it often remains concentrated on enterprise apps. Major sub-steps that must be followed can be to acquire data from various existing sources, implement a custom machine learning model, create data processing pipeline, identify the right algorithms, train and test the machine learning models and deploy them in production.

Machine Learning as a service offering takes the data and exposes the final model as an API endpoint by making use of cloud infrastructure for training and testing the models. This is the initiation of the requirement for the enterprises to start investing in a data engineering and data science teams. This will in-turn allow the customers to spin up infrastructure powered by advanced hardware configuration based on GPU’s and FPGA’s. Here is a sample list of platforms that offer Machine Learning as a Service:

STEP 3: Third and final step in AI implementation involves running open-source AI platforms on-premises. This calls for investment in infrastructure and teams to generate and run the models locally. Enterprise applications come with a high degree of customization. These are particularly suited for those who have to comply with certain policies before sharing confidential customer data.

As machine-learning-as-a-service (MaaS) is similar to platform-as-a-service (PaaS), running AI infrastructure is similar to a private cloud. Major cloud computing companies like Amazon, IBM, Microsoft, and Google offer facilities to construct and run neural networks, machine learning and other types of AI in their public cloud computing facilities. Their pricing is decided by the variety of tools that they use. Another class of service is provided by the cloud SaaS, Oracle, and Salesforce. Here is a list of open-source platforms for machine learning and deep learning especially for those who wish to implement the AI infrastructure:

• MXNet
• Microsoft Cognitive Toolkit
• Tensorflow
• Theano
• Caffe
• Torch

Additional Steps in AI Implementation in Mobile Apps

• Understand the features and need to add AI into the mobile app
• Mark the app area where AI can help improve the app
• Estimate the cost required
• Check feasibility of the MVP and practical changes that are required
• Involve ML-AI experts to design strategy
• Implement data and security features into the app
• Make use of robust supporting technological tools like storage aids, security tools, backup software, optimization solutions

All in All

AI has evolved to become the core building block of contemporary applications. Being as common and as important as a database, AI has created the roadmap for building intelligent applications. Top app developers need to be able to read the landscape, stay prepared to move quickly to implement changes to minimize the chances of disruption moving forward. Machine learning, artificial intelligence, and automation have enhanced the opportunities for similar reasons. Exploring AI API must be the first step besides hosted MLaaS offerings. Insurance and finance companies are focussing on investing in top AI companiesthat are at the forefront of creating new and interesting technologies that meet existing business needs.

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While it is easy to embrace technology, waiting for the financial return on investment can be challenging. Sometimes, it can be even more thwarting to wait for any significant improvements or breakthroughs.

Artificial Intelligence (AI) has proven to be a double-edged sword of modern technology. Human intelligence has flourished even more after combining machine learning (ML) concepts with AI. Cyborg technology, automated transportation, diffusing bombs, solving issues of climatic changes (might however seem like a tall order from a robot but machines are likely to have more access to data than humans in coming times), gaming companions like C-3PO and Pepper and elder care – AI technology has taken big steps in influencing our future and shouldering professionals across the industry and this trend will grow in the coming years. AI can be effectual enough in enhancing internal and external operations, increasing sales, reduce costs, optimizing processes, building data strategy and promoting better business decisions.

Artificial Intelligence and Machine Learning, Virtual Reality (VR), Internet of Technology (IoT) Platforms and Blockchain Software-defined security are the key mainstream technologies to create a transformative impact in coming years. Out of the emerging AI trends, deep learning, cognitive computing, and machine learning trends will prove to be trendsetters in years to come. More trends to follow in 2019:

Virtual Assistants and Chatbots

These work on one simple principle: Processing natural language. While it is a small script that understands the text; when combined with speech recognition solutions, these stimulate understanding along-with usable solution to deliver business value. Chatbots have gradually become the face of any business, due to their 24/7 availability and human-like responses.

Reduces the Time Needed for Training

AI-based academic work focusses on reducing time and computing power that is required to train a model effectively with a goal to make daily work increasingly affordable. Out of the various ways to optimize the time required to train a model are to optimize the required hardware infrastructure. Google Cloud Platform offers a cloud-based tailored environment, for building machine learning models. Scaling and redesigning the architecture of neural networks via Google’s Gpipe infrastructure to make use of existing resources is another way in which performance is optimized.

Powering Autonomous Vehicles

AI enabled autonomous vehicles can see, hear, think and of course drive just like normal human drivers do. They are complemented with sensors, cameras and communication systems to generate a massive amount of data and make appropriate decisions while on road.

AI-enabled chips will become prominent

AI is based on very specialized processors that complement CPU. It is often difficult for even most advanced CPU to improve the speed of the training on the AI model. It requires additional hardware to perform complex mathematical computations to speed up tasks such as object detection and facial recognition. Recognized chip makers like Intel, NVIDIA, AMD, ARM, and Qualcomm are going to ship specialized chips that will speed up the execution of AI-enabled applications.  These chips will be helpful in optimizing specific use cases and scenarios related to computer vision, natural language processing, and speech recognition. AI in healthcare, automobile, and finance industries will as well escalate.

IoT and AI Bring Competitive Advantage

IoT is going to be the best driver of artificial intelligence in the enterprise. This will involve the advanced ML models based on deep neural networks to be optimized to run at the edge. Right from performing outlier detection, root cause analysis, predictive maintenance of the equipment, speech synthesis and time-series data, edge devices will be equipped with the special AI chips based on FPGA’s and ASIC’s.

Neural Networks are Interoperable

Selecting the correct framework to develop a neural network model was important as it was not possible to port the trained model to another framework. AWS, Facebook and Microsoft recently collaborated to address the above challenge and built Open Neural Network Exchange (ONNX). This enables the reusability of trained neural network models across multiple frameworks.

Automated Machine Learning Will Outstand

Automated Machine learning models will outshine the traditional process of training ML models. These perfectly align between cognitive API’s and custom ML platforms thus delivering the right level of customization without forcing the developers to navigate entire workflow. This model is flexible and portable.

AI will automate DevOps through AIOps

This convergence will help teams perform precise and accurate root cause analysis and benefit public cloud vendors and enterprises.

Compendium

After learning the importance and effectiveness of machine learning and artificial intelligence, top app development companies have adopted these techniques for developing applications for various industries like healthcare, manufacturing, automobile, agriculture, and finance, etc. Large organizations like Amazon, Google, Apple, Facebook, Microsoft, and IBM have already invested huge sums in research and development to fill in the gap between consumer and AI. Adding more to the top trends in AI and ML discussed above, some less prominent and even lesser known trends will keep up with their progress. These include: Facial Recognition, Deep Learning, Increase in public cloud providers, AI-enabled chips, and AI coupled with GDPR standards to bring in more secrecy and protection of user’s digital data that will eventually promote safety and awareness of the complicated technologies of AI.

All these trends will give a boost to topical business applications, and make them focus on business value instead of cost-efficiency. As the power of data is democratized with the widespread adoption of analytics and data-driven decision making, it will become central to enterprises plan and execute strategy.

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