2021 trends data scientists should follow

I read analyst reports to keep myself up to date with what is going on in the analytics and AI world. An analyst report offers an unbiased, side-by-side, third-party evaluation of the technology in the market. I trust them because I have lived through the rigorous evaluation process that goes on behind the scenes for creating those reports.

The recently released 2021 Gartner MQ for Data Science and Machine Learning contains a wealth of information and here are my takes on key market trends from that report for data scientists. This evaluation features SAS Viya with its SAS Data Science offerings.

1. Composite AI

You must often push the boundaries of innovation when asked to solve key business problems. That’s because the problems that you are asked to solve are complex and often require both structured and unstructured data to solve, calling for the application of different AI techniques or composite AI. That’s where SAS Viya comes in, by providing machine learning, deep learning, NLP, computer vision, forecasting, and optimization capabilities that can easily be used together to solve the most complex of business problems.

2. Decision Intelligence

Ultimately, you create models to help businesses make better decisions. In many cases, these models can help automate decision-making in real-time when combined with business rules and embedded in a decision process. Gartner acknowledges the decision intelligence in SAS Viya as a strength.

3. MLOps

Data science and machine learning platforms must support model operationalization in addition to model building. This includes model performance monitoring, model governance, and lineage. Why is MLOps so important? On average, only half the analytics models built ever make it into production. That’s right 50%! That can be disheartening to those of you who pour your time and energy into modeling only to never have those models see the light of day. MLOps is another strength of SAS’ – but don’t take just our word for it, Gartner says so too.

4. Cloud-native architecture

You want instant access to the latest innovations and enhancements in your modeling toolkit. The most likely way to achieve this is through applications running on the cloud. The integration between Microsoft Azure and the SAS Viya analytics platform empowers organizations to stand up SAS analytics in their cloud environments with ease and quickly gives users access to the latest and greatest.

5. Innate integration with open source

You look for the most innovative tools and technologies to help them solve the business challenges in the best way. Often this is a modeling melting pot of open source and commercial analytics tools which need governance to manage the disparity of the code base and processes. SAS Viya is praised for its innate integration with open source by supporting models in different languages, moving them from sandbox to production in a centralized, governed manner that meets any scalability requirement.

6. Automated feature engineering and automated modeling

Think about the tasks that you perform across the analytics life cycle: data access, data prep, feature engineering, building models, training models, tuning models, and deploying models. Now imagine if those could be automated? How many more models could be built? The end game is to build as many models as needed, as easily as possible, to find the one that can be used to solve the business problem at hand. SAS Viya is praised by Gartner for its automated pipeline generation and its hyperparameter autotuning to facilitate the experimentation process.

This report is a great way to educate yourself about the trends in data science and machine learning. No registration is required to get the report. Happy reading!