Machine learning engineers have diverse roles to play in an organization. If developing new algorithms and working with data sounds enticing to you, this could be a great role for you.
For any organization that desires to gain a competitive advantage in the market by deploying artificial intelligence in its operations, a machine learning engineer would play an indispensable role to make this happen. Machine learning engineers are guardians of some of the most revolutionary technological advances happening in the world today, and it is their understanding of the nitty-gritty of how different projects sync up together that makes them an extremely valuable asset for the organization they work at.
Simply put, a machine learning engineer would be responsible for getting the machine learning based solutions up and running as per the plan laid out. To make this happen, there could be a need for some programming to integrate the machine learning algorithm into the project, and the person responsible would also need to have an experience working in an IT environment to be able to deploy the final solution into production. It is naturally assumed then, that the machine learning engineer would have ample knowledge of machine learning concepts to be able to comprehend the role of the algorithms in the application at hand, and be capable of solving potential bugs, pitfalls and malfunctions.
This is the prime reason why machine learning engineers generally need to excel at programming and mathematics. It always a huge advantage for a machine learning engineer to have a thorough understanding of a programming language like Python, and be extremely fluent in mathematics.
Maciej Baranowski, machine learning and marketing automation specialist at Zety opines that machine learning engineers are top-echelon programmers who design self-running software that can learn and apply knowledge on autopilot without any human intervention. He goes on to say that machine learning engineers are extremely high in demand based on the specific goals that the organizations want to achieve and the time efficiencies involved. Companies, after all, have huge haystacks of data sets to process and if they even attempt to do it all manually, it would be like watching 13 Star Trek movies in one hour!
The normal job responsibilities involve preparing the data required to train a machine learning model and work with these machine learning models. They need to design machine learning models, choose relevant activation, optimize functions, tune the machine learning models’ hyper-parameters, train and re-train these machine learning models and make sure that these models are fully capable of differentiating between the signals and noise. Working with the machine learning models involves a wide array of high-level tasks, which span the entire length and breadth of the machine learning development lifecycle. Their work involves a variety of rote tasks that deal with the incessant need for data curation and handling. Some of these tasks can be automated, while the rest require the exquisite expertise of a machine learning engineer.
Organizations essentially have to bestow an immense amount of trust on the machine learning engineers and give them a certain amount of power over the process, as well as granting them access to sensitive information. Organizations need to be extremely careful in vetting the machine learning engineers before hiring them and put requisite controls in place for monitoring the work being done, as well as to help the machine learning engineers to do their work effectively.
A large part of a machine learning engineer’s job is to stay abreast of the developments happening in the field of artificial intelligence and machine learning across the world. This could be done by going through the GitHub trending page or keeping an eye on arXiv or just checking out the machine learning threads on Reddit.
Their job also involves repurposing the research-oriented technologies for more commercial contexts. This repurposing would involve adding the extra functionality and usability that the original academic work would generally not contain. Machine learning engineers begin by creating a Proof-of-Concept (PoC), which would add great value, not only to the business but also for the customers. Thereafter, if the PoC works as desired, then it can be used to create a whole new product. This could open up multiple opportunities, not just internally for the organization, but also for the end customers as they now have an increased technical leverage, courtesy of the outstanding solution delivered by deploying machine learning.
In a nutshell, a machine learning engineer should be ready for anything to come their way. They could be required to do anything from getting the data into the right input format, to writing custom web scrappers.
Does this give you a better understanding about what to expect from the role of a machine learning engineer? Machine learning engineers are, without a doubt, some of the most ‘powerful’ individuals in an organization considering the tasks and they can accomplish, the data they have access to and what they can accomplish from that data. They are generally irreplaceable and indispensable assets to their organizations.
Cognixia – world’s leading digital workforce solutions company, understands the importance of machine learning skills for an organization and has carefully crafted its machine learning programs to help equip individuals as well as organizational workforce with the right skills and knowledge that would help them accomplish and perform the duties & responsibilities of a machine learning engineer successfully. Our machine learning training program also encompasses the fundamentals of Python, to make sure there is a complete all-round development of the individual’s skillset, and that they are fully prepared to handle all situations and tasks at their workplace. We make sure to cover all the bases essential to transform enthusiasts into trained, skilled professionals who can deliver outstanding results in real-life industrial settings.
Tag : Machine Learning (ML)