The time humanity is experiencing can be designated as the most precarious in history. And charting a safer path through these circumstances is of the essence for even mere survival. Due to the advancements in computer sciences and allied disciplines, charting a safe path is now a possibility. We have enough capabilities to analyze and make sense of gargantuan amounts of data. And that too, by the deployment of trained machine learning tools.
The semi-automated process of data analytics is revolutionizing commerce and a variety of public sectors. Sectors including healthcare, traffic management, and logistics are heavily leaning on automatîon. And the results are showing. Innovation is ruling the industry and new roles are emerging every day. With relevant machine learning training, an experienced professional can easily make a mark on this emerging new scenario. Especially, in these very precarious times for tech professionals.
The major economies at the helm of outsourced work in the sector collapsed under the sheer pressure of lockdowns. And unemployment became commonplace in the sector. Automation and data science emerged as alternative careers at the most opportune moment. Diversification was easy and there was practically no shortage of students either. Today, the two allied disciplines are determining the fate of humanity. And professionals in the field are rewarded generously.
Why is an upgrade essential?
The tech and IT sectors in countries like India were largely funded by outsourced work from western countries. And with the lockdowns kicking in these major economies shattered under the sheer financial weight. However, the outsourced work was outsourced in the first place due to its redundancy in respective societies. In many cases the expertise was absent or the work was too underproductive for an entire population. Thus this mundane work was already draining the productivity of Indian tech professionals. And after the post-pandemic collapse, this work too was abruptly terminated. And at that very moment ventures of all stature and standards started to lean toward automation and data science.
The goal was to navigate through these very unpredictable times. Thus, opportunities for the adept and enthusiasts alike opened up. Professionals started to opt for online machine learning training. And students were more exposed to the industrial environment than ever before. The emergence of an entire generation of data and tech-dependent ventures opened new avenues of skill development and employment. And the blessings in these dire times were accessible only through an upgrade.
Is a career in machine learning sustainable?
Sustainability varies from person to person when it comes to profession. To achieve a sustainable career, one must possess the mindset of upgrading. The times, markets and demands of humanity are rapidly changing. And remaining relevant in this very dynamic scenario is itself a challenge. Machine learning and automation as a discipline are flourishing. And given the tendencies, the relevance and dependence are expected to reach new heights. Machine learning is an essential component of automation, and with the heightening relevance of AI in all sectors, machine learning is also poised to become more popular.
In the case of data science, business analytics and data analytics, machine learning tools are extensively deployed for achieving smooth and fast operability. Vast amounts of data are being used every hour for the betterment of commerce and a variety of data-dependent ventures. Often these humongous sets are impossible to handle or made into sense by human intervention alone. Thus machine learning-based data analytics tools are in heavy use in all sectors that are currently utilizing huge amounts of data.
What does the industry expect?
ML professionals in a plethora of fields are being treated as assets by their employers. Automation is key to the complete removal of human error from day-to-day processes and increasing the value of human labor in the process. Thus, the dedicated roles of machine learning engineers are the essential instruments for companies and ventures looking forward to such a future. Given the responsibilities that are bestowed upon a machine learning engineer, the expectations always remain high among employers.
It is recommended for an Ml professional to gain some industry-relevant, hands-on training before embarking on an employment search. Employers usually refrain from hiring freshers and investing huge amounts of effort and time in them. Thus the development of relevant skills and learning their deployment is crucial for a professional.
An ML professional is expected to be versatile. Machine learning being an essential component of automation, is being extensively used in a plethora of sectors. They are ranging from healthcare to commerce and finance-related ventures. Thus it is wise to develop a wide-ranging and versatile skill set. By working in a plethora of different fields.
Ml engineers are expected to be team players and work in collaboration with professionals from multiple allied fields. A skill essential in multidivisional commercial bodies and startups. However, teamwork can only be learnt by working on a day-to-day basis and interacting with people of different mindsets. Thus, just by taking up machine learning training, it is impossible to satisfy the demands of contemporary industry, training on the frontlines is essential.
After acquiring machine learning training on the frontlines, a professional can embark on an employment search full of diversity.
The Healthcare sectors around the world are rich with underutilized data. Just a couple of years ago utilizing them was out of the question, due to the sheer need for human intervention. But now with the rapid incorporation of automation in the sector, the obstacles are nullified. Massive amounts of data are being utilized by machine learning tools trained with the help of similar real-world data. And this gargantuan activity is paving the path for the development of personalized medicine. Thus the infusion of machine learning in healthcare is bringing in the aspect of precise and efficient therapies.
In disaster management
Thousands of lives are being saved every year by analyzing recurring natural events and calamities. The data we need is available in plenty, and we also possess the necessary processing power to make sense of the same. Thus earthquakes and storms are being predicted days or weeks before their onset and precautions and mitigation preparations can be made. In many cases, entire populations can be evacuated to safety. And lives can be saved by millions.
In traffic management
Traffic in big cities is managed through an automated paradigm. Machine learning tools for motion detection. Character recognition and case assignments are used extensively. That too coupled with high throughput cameras and sensors. Thus identification and prosecution of rogue vehicles are now an inexpensive affair.
- In marketing, machine learning is being used for reaching potential buyers. Mostly in the form of engagement and suggestion bots. These bots are designed to engage potential buyers and help the existing ones by performing simple troubleshoots.
- In administration and forecasting, huge chunks of data are analyzed with the help of machine learning and efficient and sustainable plans are made.
- End-user feedback data is being extensively used in the up-gradation and development of products. A process essential for the preservation of product relevance.
Many public and commercial sectors are experiencing a boom in the deployment of ML tools. With machine learning training and relevant skills in possession, a machine learning engineer can shine in any sector with much ease. A career in machine learning is full of surprises and challenges that might intrigue an enthusiast. And the scope for innovation is plenty. The discipline is in its infancy, thus for the sincere ones, this is the time to act and make a mark.