1Data Scientist vs Machine Learning
Because data science has been so effective in providing global corporations and organizations with predictive intelligence and data-driven decision-making, the discipline is no longer regarded as a topic outside of academic study. Professionals with expertise in data science are in high demand at firms of all stripes, from startups to established conglomerates.
Prior to this decade, data scientists spent much of their time working on algorithms and models to better derive actionable insights from large datasets. Yet, as the field of data science has progressed over the past decade, it has become abundantly evident that it entails more than just modeling. Experts in fields such as data engineering, data science, machine learning engineering, and product/business management is now essential throughout the whole machine learning lifecycle, from raw data through deployment. However, it’s unclear where a Data Scientist’s responsibilities stop and those of a Machine Learning engineer begin.
1.1Data Scientist Vs Machine Learning Engineer
With more and more businesses realizing the value of data science depends on a model being successfully deployed to production, the position of machine learning engineer is rising in importance. The MLOps of bringing models into production and tracking their operation is still fairly unorganized, despite the fact that a variety of tools and technologies, such as Cloud APIs, AutoML, and a number of Python-based libraries, have made the role of a data scientist simpler.
1.2Stages of Data Science Project
A data science project consists of four main phases:
- Framing of the problem – recasting a commercial issue as a data science challenge
- Data engineering – constructing systems and procedures to clean, organize, and analyze data in preparation for modeling.
- Modeling – developing appropriate algorithms and models through iterative prototyping.
- Deployment – the process of putting the model into production and then keeping an eye on how it does.
In large technology corporations and startup businesses, there is a more established procedure for going about data science, and the activity is explicitly delimited along these lines. This applies both to the data science process itself and to the division of labor. As a result, it is normal practice to anticipate that experts working in distinct sub-domains will concentrate on their individual fields of expertise while still collaborating with one another as necessary. However, in smaller firms that do not have the benefit of having an extensive data science team, it is expected that the initial few data science employees will work across these diverse responsibilities as “full-stack” data scientists. This is the case because smaller companies do not have the resources to build large data science teams.
As a result, the definition of a data scientist as opposed to a machine learning engineer is very context-dependent and is dependent upon the level of maturity of the data science team. In the following sections of this post, we will elaborate on the responsibilities of a data scientist and a machine learning engineer as they relate to a large and well-established data science team.
When it comes to solving business problems, a data scientist’s primary duty is to create machine learning or deep learning models. Developing new algorithms or models is a time-consuming and labor-intensive process that is not always necessary. Generally speaking, it is sufficient to make use of pre-existing algorithms or pre-trained models and optimize them within the context of the issue statement. However, scientists may be expected to create original research and model artifacts in more creative and R&D-focused teams or firms.
In contrast, machine learning engineers’ primary focus is on putting into production the models developed by data scientists. Building an MLOps infrastructure for experimentation, A/B testing, model management, containerization, deployment, and monitoring the model’s performance after deployment is just one part of this process.
The following tables illustrate how these variables translate into fundamental variations in required abilities, typical responsibilities, and typical technology used in the various professions.
|Machine Learning Engineer
|Machine Learning Engineer
Find and verify organizational challenges that can be resolved by ML.
|Put machine learning and deep learning models into action.
|Assess and visualize data at various phases of the machine learning lifecycle
Improve model efficiency, latency, memory usage, and throughput by optimizing these factors
Construct unique models and algorithms
Several types of hardware, including central processing units, graphics processing units, and edge devices, are put through inference tests.
Discover supplementary data sets and fabricate artificial data
Track the status of model upkeep and issue fixing
Formulate methods for annotating data
Managing different iterations of metadata, models, and experiments
Create specialized software to streamline the deployment process in its entirety.
In order to improve the modeling process as a whole, it is necessary to create specialized software.
2Data Scientist And Machine Learning Engineer Similarities
It should come as no surprise that the competencies and responsibilities of data scientists and machine learning engineers do, to some extent, overlap with one another. The technology stacks are also quite comparable, and while data scientists are generally anticipated to do the majority of their coding in Python, machine learning engineers are required to have knowledge of C++ in order to port the model artifacts into a format that is both more effective and quicker.
Machine learning engineers may not have the same level of subject matter expertise as data scientists, but they make up for this deficiency with their detailed knowledge of engineering tools and frameworks, such as Kubernetes, which data scientists tend to have less experience with.
Data scientists typically have a background in science, technology, engineering, or mathematics (STEM), or even advanced degrees such as a doctorate, in a variety of subjects including biology, economics, physics, and mathematics, amongst others. On the other hand, machine learning engineers typically have previous experience working as software engineers in a professional capacity. Data Science Vs MAchine Learning is always discussed widely to understand its importance.
Despite the fact that the primary focus of data scientists is on algorithmic and model development, and the primary focus of machine learning engineers is on scalable software engineering relevant to model deployment and monitoring, many of the remaining tasks are often shared between the two profiles.
In a few instances, depending on the size and experience level of the data science team, these responsibilities could be split out, and things could proceed without a hitch as a result. However, in most cases, particularly in larger teams and organizations, this can create significant conflict and friction, particularly when data scientists and machine learning engineers work on various groups and report to different managers. This is especially true in situations where the two types of workers are working under separate management.
It is feasible to differentiate between the responsibilities of data scientists and those of machine learning engineers in a straightforward manner. In most cases, data scientists will create one or more candidate machine learning models and then hand them over to machine learning engineers in accordance with a particular contract.
It should be specified in the contract that amongst other criteria, the model’s accuracy, memory, latency, number of parameters, model versions, the machine learning or deep learning framework that was utilized, and the ground labels for the validation or test set.
A well-structured handover contract guarantees that the machine learning engineers have access to all of the information that they require in order to work on the optimization of the model, as well as any additional experimentation and deployment processes. Following the handover, the data scientists will have the freedom to concentrate on the subsequent machine learning use cases that will be transferred to production.
Post-deployment, the data scientists and the machine learning engineers will continue to work together, which will become especially important if any of the models break when they are being used in production. Because data scientists now have a deeper understanding of how the models work, they are in a better position to diagnose problems and make necessary corrections to the models.
While this is happening, some of the problems with the models are due to flaws in the foundational architecture that was constructed by machine learning engineers. These developers are in the greatest position to fix these problems. The process of active learning, which involves the model continually improving itself based on live data that it is receiving, is another responsibility that falls under the purview of data scientists.
2.2The exchange of information and the working together of ML Engineers and Data Scientists
Strong collaboration among all of the different types of team members is essential to the accomplishments of a data science project. Data scientists and machine learning engineers work closely together throughout the whole process of developing and deploying a model, as well as monitoring and refining it after it has been deployed. If at all possible, it would be best for these two profiles to work together as part of the same team and report to the same management. When conditions are like these, collaboration becomes not only much simpler but also more likely to result in good collegiality and mutual learning.
The collaboration, on the other hand, is not as robust as it should be when data scientists and machine learning engineers are part of distinct teams and report to different leadership. Data scientists and machine learning engineers rely on technologies for team productivity and project management like Slack, Teams, JIRA, and Asana in these types of organizational contexts because they do not get the opportunity to engage directly with one another as frequently.
The use of such collaboration tools is actually a blessing and saves the team a lot of time and effort because it is used for a lot of use cases that are repetitive and common. The transactional nature of relying on tools whose atomic units are tickets or tasks does not, however, promote a sense of team cohesion and collaboration because these tools are organized into tasks or tickets. This is a common gripe voiced by members of data science teams that rely extensively on technologies of this kind.
Management shouldn’t overlook the importance of face-to-face or video conferencing for tackling more difficult tasks or projects. It is in these situations that the business leaders may learn of a new technical breakthrough that could answer upcoming business use cases, and the technical experts may learn of new use cases or clients from the company’s executives. Data scientists and machine learning engineers can both benefit from sharing knowledge of new algorithms, models, and frameworks that can be used to improve data science efficiency and output.
2.3Trends in the Present Industry
If the article in Harvard Business Review were updated for 2023, it would name “machine learning engineer” as the sexiest career in the decade. While data scientists and model developers are still in demand in business and academia, in recent years the focus has switched significantly toward developing the infrastructure necessary to scale up the delivery of data science models to millions of users. The demand for machine learning engineers has recently surpassed that for data scientists in the whole IT sector.
Leaders in the field have realized that it’s great to have large, complex machine learning and deep learning models perform at the state-of-the-art on academic benchmarks or training data, but that these models don’t provide any commercial value to the business until they’re deployed and serving customer requests reliably, quickly, and accurately. With more businesses going the data-driven route and setting up data science and machine learning departments or divisions, it’s more important than ever to track and meet ROI targets.
Companies with a strong customer focus in the tech industry that was among the first to invest in AI have formed strong teams of scientists and are now trying to increase their production capabilities and market the R&D artifacts developed by the data and research scientists. While top data scientists, especially those with advanced degrees like PhDs, will always be in great demand, the present overall economy is seeking talented machine learning engineers, of which there is a smaller supply compared to data scientists.
2.4Career Change: From Data Scientist to Machine Learning Engineer
Data science model development can take place in a sandbox environment like Kaggle, where models are not intended to serve real-world predictions; however, expertise in scalable model deployment, monitoring, and related machine learning engineering tasks can only be acquired through experience in a professional setting. Due to the applied nature of machine learning engineering and MLOps, there is a dearth of professionals with the requisite competence to create and maintain stable infrastructure.
Current data scientists are also looking to make the switch to MLE positions due to the allure of higher pay, longer job security, and broader influence. Software engineering skills, such as the ability to write optimized code (preferably in C++), rigorous testing, and an understanding of and proficiency with existing or custom tools and platforms for dependable model deployment and management, are the most important for a data scientist to acquire in order to become an effective machine learning engineer.
Data scientists can acquire knowledge of C++ and software engineering and testing best practices from a variety of resources, and they can also learn to use new tools and technologies like Docker, Kubernetes, ONNX, and model serving platforms. Companies prefer machine learning engineers with relevant work experience, making it difficult for data scientists to make a case for a machine learning profile without such experience.
Current data scientists have the best chance of making the switch to machine learning engineers within their current organization. The easiest way for a data scientist to make the transition from data science to machine learning engineering is to exhibit interest to their supervisors and ask if they may observe or even assist and cooperate with machine learning engineers on certain projects. This can be difficult for recent college grads who lack work experience in the field; one solution is to take the same internal transition route from data science or software engineering to machine learning engineering.
More people will be able to make the leap from data scientist to machine learning engineer as the industry develops and businesses improve their machine learning platforms and related procedures, such as hiring and upskilling.
Robotics and AI are crucial to today’s business. The demand for data scientists has skyrocketed as the pace of the AI revolution has quickened over the past decade. The field of data science has also developed, with specialized fields including data science, modeling, engineering, and product and customer success management. To bring the models created by data scientists from the data prepared by data engineers and for use cases defined and built by product or business managers to fruition, machine learning engineers play a crucial role.
Engineers skilled in machine learning are in high demand right now, much like data scientists were a decade ago. Engineering, scientific, and commercial personnel will all have to adapt to new opportunities as the AI industry continues to expand and diversify in response to new challenges.