Remote work is here in force, and here to stay for many workers. Depending on mission, this can extend to Federal agencies, many of whom are now focusing on low-code and no-code application development platforms to support remote work for developers. According to Business Wire, low-code/no-code platforms are expected to grow at 44.4% by 2022, reaching $27.23 billion (up from $4.32 billion in 2017). IDC estimates that more than 500 million apps could be created with these platforms by 2023. But how can they best support the Enterprise? 

Large Enterprises rely on software applications to build and run core business functions (sales, marketing, supply chain, logistics, business intelligence, and others). They can be integrated or interconnected with other applications to create an overall Enterprise system. Even though Enterprise software is created to support large-scale organizations, the reality of implementation can take up more technical support resources than they’re supposed to spare. 

Low–code and no-code advantages, for Enterprise and beyond:

• Bandwidth Issues: Internal cross-Enterprise IT teams may have little time to manage client or Enterprise-level applications.  
• Cost-Constraints: Purchasing semi-custom apps or hiring a mobile/web application development firm can rack up a huge bill quickly. 
• A faster time to market: The most significant benefit is that the development duration is cut between weeks and months or days. In addition to bringing your app to market quicker, it is also possible to quickly take feedback from users and create the latest features.  
• Multiple Deployments: These development platforms allow businesses to create applications deployed on several platforms simultaneously. No-code, low-code mobile application development makes deploying an app to a platform much simpler.  
• Reduced Errors: Less code equals fewer errors, equals less dev time.  
• Lower Development Costs: Due to the reduced development process, the faster speed and less resource requirements development costs for low-code and non-code applications are lower.  

Low-code and no-code dev allows companies to build applications with visual development techniques, thus eliminating other development methods that require the writing of many lines of code. While low-code applications and no-code software development work best when combined, there are fundamental differences between low-code and no-code: 

• No-code platforms let teams that do not know about software development or coding use functional, reusable components to build applications.  
• Low-code platforms require a certain level of programming, but they allow developers to create applications developed in faster turnaround times.  

Top Low–Code and No-Code App Development Platforms: 

• Siemens Mendix 
• Microsoft PowerApps 
• Appian 
• Out Systems 
• Airtable 
• Amazon Honey code 
• Salesforce.com Lightning Platform 
• Zapier 
• Google AppSheet

Great care must still be given to ensure the necessary governance of the full business process while using such platforms. When done thoughtfully, developers are able to tackle workflow and process issues with greater speed, even remotely. Rest-assured there is still demand for traditional programming techniques for complex applications – but expect to see big increases in corporations and agencies investing funds and strategies in these low-code and no-code platforms. Have you used any of the platforms? Which is your favorite? 

The demand for Machine Learning for use in different areas is growing because the quantity of data increases with time. Machine learning provides a wealth of methods for separating information from data and converting into a set of goals.

Machine learning algorithms can enhance the information and automate functions mostly connected to optimization and regulation. In addition, computer vision and machine learning have expanded many fields of study, medical diagnostics statistics, statistical data analysis, algorithms, and scientific research. ML is being implemented in mobile applications and computer devices, online websites, cybersecurity, and other areas.

The expanded data has a significant impact on a variety of disciplines. The ability to extract valuable knowledge and inferences from data has emerged as the newest research and commercial applications model. On this page, we will look at some of the applications of machine learning that are being implemented into our everyday practices.

Machine Learning Applications in Daily Life

1. Commute Predictions

• Predicting traffic: We all use GPS services to guide us while driving. ML, in such scenarios, assists us in our everyday routine to get around traffic jams and get to our destination in time. GPS is a system that we use to locate and save our location and speed in the server central to manage traffic. The data is used later to create the map of present traffic. ML also analyzes congestion; the one drawback is that it can be untrue if only a few vehicles use GPS when driving.
• Online Transportation Networks: When booking a taxi using the app, the app determines the cost of the journey. If sharing these services, what can they do to minimize detours? Machine learning is the answer. In an interview, Jeff Schneider, the engineering director of engineering at Uber ATC, reveals that they employ machine learning to identify price surge hours by predicting the riders’ demand. Through the entire life cycle of these products, ML plays an important part.

2. Email Spam and Malware Filtering

There are several spam filtering methods that email clients utilize. Ensure that the spam filters are continually up-to-date and driven by machine learning. If rule-based spam filtering is implemented, it cannot keep up with the latest spammers’ techniques. Multi-layer Perceptron, C 4.5 Decision Tree Induction, and C 4.5 Decision are just a few methods used to filter spam powered by the ML.
More than 325 000 malwares are identified every day, and each bit of software is between 90 and 98 percent like its predecessors. Security programs for the system that runs on machine learning comprehend the code pattern. Therefore, they can easily identify new malware with a 2-10% range and provide security against them.

3. Social Media

• Many of us are obsessed with social media today, and for a good reason. Social media can be fun and informative in all aspects, from teaching DIYs and other new techniques via videos to news and social networking. ML technology plays a significant part in creating web-based social media platforms that are friendly to users and applications.
  Recommending Friends: Social network websites like Facebook maintain a record of people we have connected with, the profiles we check often, shared groups, and our work, and interests. Based on ongoing education, Facebook suggests people with whom we can form friendships.
• Face Recognition: Facebook and other social websites and apps like Facebook and Instagram instantly recognize our friends when uploading photos to media. They then send notifications to add them to our profiles. While the interface is easy to use and appears seamless on the front, the complete process on the back end can be quite complex.

4. Medical Diagnosis and Healthcare

Machine Learning incorporates a soup of methods and tools that tackle diagnostic and prognostic concerns in the various medical fields. Machine learning algorithms are widely employed for

• the study of medical data to detect patterns in the data,
• managing inappropriate data,
• explaining the information produced by medical units
• also, to ensure the effective surveillance of the patients.

Machine learning is also helpful in estimating breakthroughs in diseases and generating medical data to research outcomes, planning, and aiding the treatment process, and overall management of patients. Alongside the machine-learning process, AI is utilized to ensure efficient monitoring.

5. Personal Smart Assistants

We have seen a significant increase in personal smart assistants such as, Siri, Cortana, and Google Assistant, as well as Amazon Alexa and Google Home.
By implementing AI to its fullest extent and integrating it into the home devices and personal assistants, follow instructions, such as setting reminders and searching for online information, controlling lights, etc.
Personal assistants and devices that include ML chatbots rely heavily on Ml algorithms to gather information, learn about users’ preferences, and provide a better experience based on previous interactions with people.

Conclusion

It is not hard to see how artificial intelligence and machine learning have transformed our lives by making them more straightforward and efficient. With the emergence of AI and ML trends, we take advantage of smart technology. We have reviewed a variety of apps here. Machine learning technology is utilized in the field to affect our daily lives. It can also help us make decisions in business, improve operations, and boost productivity in industries that stand out in the marketplace.