If you ever had a chance to compare the resumes of 2 developers who come from nearly identical backgrounds and skills – languages, frameworks, platforms, methodologies, industries; you may find them practically to be the same person – on paper. However, not both could be equal while comparing their success metrics.
The roadmap to success for a developer is not limited to bearing technical skills and credible experience. It is a long journey of immense hard work and dedication as well as crucial behavioral characteristics and optimal mindset that sets one apart from the crowd. To make a mark in the software development industry, there are a few traits and habits that are widely considered by professionals as value-boosting add-ons that give them an edge over the rest. 1. Write Clean, Readable, And Reusable Code
In the context of software development, everyone can write code, but not everyone can write good code. A successful developer is the one who makes sure their code is clean, robust, flexible, and reusable – not just for themselves in the future but also for anyone else working on it in the future, irrespective of the coding standard or methodology used. While it is easy to write line after line and not pay attention to your function size, you need to pause and remember that you’re going to be spending more time reading and testing it than writing it. It is important to understand that when you go back to read it, you should be able to understand it quickly and know what you’re meant to do. 2. Constantly Expand Your Knowledge Base
Underestimating oneself is dangerous – but so is overestimating. A successful software developer is never complacent; they know there is always something new that they do not know about and are always levelling up their know-how.
Be on the lookout for new improvement opportunities. To keep pace with the advancements, you can join skill enhancement programs, online training, read tutorials, watch videos, etc. Upskilling yourself will be a valuable habit to develop in your career. 3. Be Disciplined And Persistent
A successful software developer is constantly looking to expand their knowledge and sticking their nose into new languages. However, that route is not free of obstacles. To successfully learn something new and gain expertise, one needs to be stubbornly disciplined and not too attached to their comfort zones.
Being disciplined helps finish what you started – and on time – without compromising on quality for speed. Persistence, on the other hand, is what will help you to go beyond the pain-points, beat all the odds and obstacles, and ace your deadlines – both personal and professional. 4. Get Help From Strangers On The Internet
Don’t confuse persistence with pride. Successful developers don’t allow their egos to turn a programming problem into an unnecessary productivity drain—especially not when a solution may be readily available online. Sometimes, asking for help—yes, Google counts—is the most efficient first step toward a solution. Research what the code does and why it solves the problems. Do not just cut/copy and paste. Lack of background knowledge could create problems in your code. 5. Know Your Forte
Not everyone is made for doing everything. You may be good at developing UI and UX but may not be an expert at developing the back-end of an application. Successful software developers know their forte and work around that to succeed in whatever they do. 6. Be Open For New Things
Another habit of successful developers is being open to new things and going the distance in embracing them when it is required. To meet a client’s expectation, a developer may be required to go an extra mile in researching and discovering every aspect of technology. Successful developers are in a constant feat of exploring new technologies, methodologies, and frameworks that help them stay abreast with the latest trending topics. 7. Listen More Than You Speak—Or At Least Listen Before You Speak
If you’re in an office with other developers, listen first, then speak. It’s the quickest way to learn.
This requires humility, especially if you think you’re the smartest person in the room. Great developers have the ability and willingness to admit when they do not know something. Refrain from being that programmer who spews out a bunch of technical jargon instead of acknowledging they might not yet know the answer to a particular question. 8. Pay Attention To The Details
Any successful developer can tell you that to write a perfect code or to complete a project, one needs to pay detailed attention to the client’s requirements and work accordingly. You would not want to work on a piece of code again and again because of a small semicolon that you forgot to add while terminating a line of code. Similarly, be responsible for the objective and the goal that the code you are working on is intended to achieve. Successful developers are always aware of how each line of their code helps to drive the business as a whole. 9. Deeply Focus On The Right Thing
The ability to focus on the shared goal of a particular project without getting side-tracked by nice-to-haves or pie-in-the-sky thinking that isn’t moving you closer to your target is vital. It is understandable to go down rabbit holes or get wrapped up in the edge cases of a particular technical implementation. When you step back a little bit, you might realize that you’re putting a lot of effort into something that is not that crucial to the broader goal that everyone is trying to achieve. Make sure you’re on the same wavelength as the rest of the team. Successful developers understand that innovation is a team sport. 10. Have The End Product In Mind All The Time
Low-Code and No-Code platforms are going to be game-changer for tech professionals across the world. The increasing number of low-code and no-code machine learning (ML) libraries is making it extremely faster and easier to develop top-notch projects. Here are top no-code and low-code libraries that you should be aware of.
This is an open source, low-code machine learning (ML) library in Python which automates ML workflows.
It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive.
You can easily tune the hyperparameters of the various models on GPU.
2. H2O AutoML
Automated machine learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems. AutoML tends to automate the maximum number of steps in an ML pipeline — with minimum amount of human effort — without compromising the model’s performance.
H2O AutoML is an automation tool used as the combined interface for multiple models and algorithms.
It is fully open-source, distributed in-memory machine learning platform with linear scalability.
It supports both Python and R programming languages. For beginners, it helps to automate preprocessing, training, validation and fine-tuning models.
This low-code library is also known as ‘Auto_ViML’ or “Automatic Variant Interpretable Machine Learning” (pronounced “Auto_Vimal”). It accepts any dataset that is in the form of the Pandas data frame.
One of the library’s unique differentiators is that it performs feature reduction (or feature selection) automatically in order to produce the simplest model which in this case is the model with the least number of features needed to produce reasonably high performance.
This tool performs category feature transformation and simple data cleaning steps such as identifying missing values as “missing” so that they can be best left to the model to decide how to use them.
Auto-ViML provides verbose output to allow for a great deal of understanding and interpretability.
4. Create ML
Create ML is a purely no-code, drag and drop solution developed by Apple. It works on macOS and comes with a bunch of pre-trained model templates.
You can also train models to perform tasks like recognizing images, extracting meaning from text, or finding relationships between numerical values.
Before the training, you can set the iteration count and fine-tune the metrics. For models such as style transfer, Create ML provides real-time results on the validation model.
5. Google Cloud AutoML
Google has created the Apple-like AutoML tool. AutoML by Google Cloud offers various natural language, AutoML translation, and video intelligence products.
Rather than starting from scratch when training models from your data, Google Cloud AutoML implements automatic deep transfer learning and neural architecture search for language pair translation, natural language classification, and image classification.
Google Cloud AutoML helps developers with limited ML expertise to build models specific to their use-case and business needs.
Ambient Intelligence is set to rise in its scope and potential as machine learning continues advancing and the number of IoT devices and sensors continue increasing.
Ambient Intelligence (AmI) is a new paradigm in information technology that’s rapidly transforming the healthcare industry. What started out as merely a concept – by tech company Philips, and European Commission’s Information Society and Technology Advisory Group (ISTAG) – in the 1990s is, today, an amalgamation of two, primary, disruptive technologies – Artificial Intelligence (AI) and Internet of Things (IoT). It is because of AmI that the world has witnessed impressive development in AI assistants like Siri, robotics, sensors and more. With thoughtful use, this technology is on the crux of disrupting healthcare too.
What is Ambient Intelligence?
Ambient Intelligence refers to the combination of IoT sensors, sensor networks, and Human-Computer Interaction (HCI) technologies powered by Pervasive-Ubiquitous Computing, big data and artificial intelligence frameworks. Or in simpler terms, they are physical spaces capable of being sensitive and responsive to the presence of humans. This technology paves the way to a futuristic world where sensors embedded in daily use devices will create an intelligent environment which adapts to its user’s needs and wishes seamlessly.
AmI can be leveraged in a wide range of technologies such as biometrics, affective computing, RFID, Bluetooth low energy, microchip implants, sensors like the thermometer, motion detectors, photo-detectors, proximity sensors, and nano-biometrics. These sensors will gather data, and interpret and analyze it to adjust to or predict user expectations.
Ambient intelligence-powered environments have the following characteristics:
Awareness of individuals’ presence
Recognition of their identities
Awareness of the context (e.g. weather, traffic, news)
Recognition of activities
Adaptation to the changing needs of every individual
How Will It Help Healthcare?
Early applications of AmI could enable more efficient clinical workflows and improved patient safety in ICUs and operating rooms. It can –
Help by recording patient health stats (with patient permission) and update the patient Electronic Medical Record (EMR) to provide a better and more accurate narrative.
Aid health care workers (physicians and nurses) in delivering quality care by analyzing patient information like prior treatments, allergic responses of the patient and more.
Help the elderly by remotely monitoring their health and enables them to have an independent living, in countries with a higher population of senior citizens. (Through Ambient Assisted Living (AAL) technology.)
Enrich overall patient experience, physician satisfaction, and quality of care.
Smart Hospital Rooms
Ambient intelligence can pioneer smart hospital rooms equipped with AI systems that can do a range of things to improve outcomes. The School of Engineering at Stanford University is reportedly exploring how a combination of electronic sensors and artificial intelligence could be installed in hospital rooms and elder care homes to help medical professionals monitor and treat patients more effectively.
It suggests using two types of infrared technologies, i.e. the low-cost active infrared and passive detectors which can be incorporated into the patient environment. The first type of infrared is already being used outside hospital rooms, for instance, to discern whether a person washed their hands before entering and, if not, issue an alert. The second infrared technology, i.e. the passive detectors will help night vision goggles to create thermal images from the infrared rays generated by body heat.
In the hospital setting, a thermal sensor above an ICU bed would enable the governing AI to detect twitching or writhing beneath the sheets, and alert clinical team members to impending health crises without constantly going from room to room. During the research, passive detectors helped the team of researchers avoid relying on high-definition video sensors since capturing video imagery could unnecessarily infringe the privacy of clinicians and patients. Meanwhile, the active infrared helped them in tracking hospital-acquired nosocomial infections. Leveraging such ambient intelligence applications can also help in computer-assisted monitoring of patient mobilization in ICUs, and automating surgical tool counts to prevent objects from being accidentally left in a patient.
Ambient Intelligence is still emerging. Currently, it has already empowered users’ capabilities via the creation of a sensor-based environment which is sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions.
In healthcare, it will help in numerous ways like continuous monitoring, smart hospitals, assisted therapy, etc. Not only that, but Ambient Intelligence is also on the threshold of disrupting businesses and industries like e-commerce, retail and more. With the proliferation of IoT devices, Ambient Intelligence will surge, however, company vendors should be careful about factors like data usage, privacy and overall security.