In the next decade the role of a data scientist as we know it will be quite different from what it is currently. However, don’t fret about it. No person is predicting job loss or even shifting jobs.
Data scientists should be fine as per the Bureau of Labor Statistics, this job is predicted to grow at a greater than average rate up to 2029. However, technological advancements are the catalyst for major changes in data scientist’s job responsibilities as well as the way companies are approaching analytics in general. Additionally, AutoML software aid in automatizing the machine learning process from raw data into a usable model, will drive this transformation.
In 10 years Data scientists will have completely different skills and tools, however their job will remain the same: serving as knowledgeable and skilled technology guides , able to interpret complicated data in order to solve business issues.
AutoML democratizes data science
In the past, machine learning algorithms and methods were exclusively the realm of traditional data science jobs, namely those who have formal education or advanced degrees or employed by large tech companies. Scientists in data science have had a vital function in all aspects of the machine learning development spectrum. However, in the future their role will be more strategic and collaborative. With tools such as AutoML to automatize some of their academic abilities, data scientists can concentrate on helping businesses find solutions for business problems using data. Python is an open source high-level language that is interpreted and offers a powerful approach to object-oriented programming. It is one of the best languages used by data scientists for various data science projects/applications. The need for data analysts and data scientists will rise by more than 1000% in the coming years. Now is the time to take the first step. If you’re looking to develop into a professional data analyst, or take the leap to become a data scientist, learning and mastering Data Science with Python is essential!
It is due to AutoML simplifying the process of applying machine learning to. Cloud-based vendors from startups to hyperscalers have created solutions that are simple enough for programmers to utilize and play with without having an extensive educational or experience hurdle to the entry point. Similarly, some AutoML applications are intuitive and simple enough that non-technical workers can try their hands at creating solutions to problems in their own departments–creating a “citizen data scientist” of sorts within organizations.
To understand the possibilities these tools can open for researchers and developers it is first necessary to know the current status of data science in relation with machine-learning development. It is easier to comprehend when put on the maturity scale.
Smaller businesses and companies that have more traditional roles in the area of the digital revolution (i.e., not traditionally trained data scientists) generally fall on this side of the scale. At the moment, they’re the largest customers for standard machine learning programs that are geared towards an audience that isn’t familiar with the complexities that machine learning has to offer.
- BenefitsThese programs are usually straightforward to implement and are relatively inexpensive and simple to implement. For smaller businesses with an extremely specific procedure to automatize or enhance it, there are probably a number of feasible options available. The ease of entry makes these programs ideal for data scientists who are trying machine learning in the very first place. Because some of the applications are so intuitive, they even allow non-technical employees a chance to experiment with automation and advanced data capabilities–potentially introducing a valuable sandbox into an organization.
- Pros:This class of machine learning programs is known for being rigid. Although they are simple to use, they’re not easily customizable. Therefore, certain levels of precision may be unattainable for specific applications. Furthermore, these applications could be severely restricted due to their reliance on models that are pre-trained or information.
Some examples of these are Amazon Comprehend, Amazon Lex, and Amazon Forecast from Amazon Web Services and Azure Speech Services and Azure Language Understanding (LUIS) from Microsoft Azure. These tools are usually sufficient for budding data scientists to take their first steps into machine learning and propel their companies further along the spectrum of maturity.
Solutions that can be customized using AutoML
Large organizations with similar data sets – think the data on customer transactions or marketing email metrics – require more flexibility in using machine learning to address issues. This is the reason for AutoML. AutoML uses the processes of a traditional machine learning workflow (data discovery exploration, exploratory analysis of data and tuning of hyperparameters, etc.) and combines them into a programmable stack.
- Advantages AutoML software allows greater experiments to run with data spread across a wider space. However, the true strength of AutoML is its accessibility and the ability to create custom configurations that can be made and inputs can be reworked quite quickly. Additionally, AutoML isn’t made exclusively by data scientists, but rather with them for an intended audience. Developers can easily experiment in the sandbox and incorporate machine learning components into their own projects or products.
- Pros:While it comes close to what AutoML can do, its limitations mean that accuracy of outputs will be challenging to attain. This is why degrees-holding, card-carrying information scientists are often snubbed by applications created with the help of AutoML -even if the outcome is precise enough to resolve the issue at hand.
Examples of these programs are Amazon SageMaker AutoPilot or Google Cloud AutoML. Data scientists in ten years in the future will definitely have to be proficient with such tools. As a developer adept in multiple programming languages, Data scientists will also need to be proficient in several AutoML environments to be considered the top of the line.
“Hand-rolled” and other home-grown machine learning solutions
The most significant enterprise-scale businesses as well as Fortune 500 companies are where the majority of the most advanced and proprietary machine learning software are being created. Data scientists from these organizations are part of teams that are perfecting machine learning algorithms using vast amounts of company records from the past and building the applications from scratch. These kinds of custom applications can only be made with a lot of skills and resources, which is the reason why the rewards and risk are so high.
- BenefitsLike every application that is built entirely from scratch, custom machine learning has been deemed “state-of-the-art” that is developed with a thorough understanding of the challenge that is being addressed. It’s also more precise, even if it’s by small margins than AutoML and standard machine learning software.
- Con:Getting a custom machine learning software to achieve certain accuracy thresholds is very difficult and requires heavy lifting from groups of data scientists. In addition, custom machine-learning alternatives are the slowest and costly to create.
A hand-rolled machine learning system begins with an empty Jupyter notebook and manually importing data and then performing every step, from exploratory data analysis to the tuning of models by hand. This is typically accomplished by writing custom-written code with open-source machine learning frameworks like Scikit-learn, TensorFlow, PyTorch, and many more. This technique requires a large level of experience as well as experience, yet it can yield results that typically outperform the turnkey machine learning solutions and AutoML.
Tools such as AutoML can shift the roles and responsibilities of data scientists in the coming 10 years. AutoML removes the responsibility of creating algorithms for machine learning from data scientists and allows the capabilities in machine-learning technology in other problem-solvers. With their time freed to concentrate on the data they have as well as the inputs themselvesData scientists a decade from now will be the most valuable guide to their companies.
Eric Miller serves as the senior director of technology strategy at Rackspace where he offers direction in strategic consulting and has an established track record of developing within the Amazon Partner Network (APN) ecosystem. A highly skilled tech leader with more than 20 years of successful experience working in the field of the field of enterprise IT, Eric has led numerous AWS and architecture-based solutions, such as the AWS Well Architected Framework (WAF) Assessment Partner Program, Amazon EC2 for Windows Server AWS Service Delivery Program as well as a broad variety of AWS redesigns for multi-billion dollar companies.
