Data Science

Emerging trends and technologies in Data Science

Data science field is emerging at a faster pace than ever before. Time to value (results of business activity) is the key business driver enabling business to innovate to exceed customer expectations. Data science team are helping business to be customer-centric by offering contextual recommendations, resolving issues and get more customer value.

Data science used to be a complex and process-heavy field where practitioners mastered data science skills over the years. Data science practitioners know the right framework, good processes, and optimal technological tools to design, build and deploy machine learning (ML) models at scale.  

Many software vendors and other Artificial Intelligence (AI) companies are innovating to commoditize the ML/AI algorithms such that these algorithms can be incorporated into products and services at ease. Many software vendors offer out-of-the-box basic machine learning models in domains such as vision, language, forecasting, and so on. These basic machine models are pre-training and only need fine-tuning based on the customer’s proprietary datasets. These models empower many data scientists to build solutions quicker for solving customer-centric problems. Since these models are offered as services, there is less reliance on the infrastructure team and software developers to provision cloud machines and build code respectively.

Many third-party providers are offering customized machine learning models as an API at a lower price compared to major cloud vendors. These pay-as-you-go third-party APIs enables data scientist not to reinvent the wheel and quickly deploy solutions. These third-party providers are also providing support to customize their machine learning algorithms using proprietary data for each company. This leads to getting access to custom ML models via their APIs. 

For an applied data scientist, these third-party APIs and cloud provider’s toolsets are good enough to solve many common business use-cases. 

There are two sectors where data science is emerging namely algorithm development and technological tools. Newer algorithms are being developed utilizing a vast amount of data and training larger models such that many algorithms can be generalized. This is especially focused on Natural Language Understanding (NLU) field whereby language models are generalized. This leads to building of sophisticated algorithms for auto-completion of words, prompting, Q&A, text summarization, content generation, and so on.

Utilizing Graphic Processing Units (GPUs) instead of generic Central Processing Units (CPUs) to train models with a huge amount of data in lesser time is the latest trend in building custom machine learning models quickly. Frameworks such as MXNet, PyTorch, TensorFlow utilizes GPUs to train deep learning models in frameworks. Computer vision models come pre-trained leading to face detection, image classification and objection detection must easier to implement with few lines of code. 

Deep learning is getting a lot of attention these days as it is achieving unprecedented levels of accuracy on par with humans. Google’s Alpha Go algorithm that beats the world’s best Go player is based on deep learning algorithms and implementation. The next frontier in data science is achieving Artificial General Intelligence (AGI) whereby an AI achieves human-level intelligence and ability to learn more generically rather than doing specific tasks. There is much active research being done in the field of the AGI and deep learning. If you are aspiring data scientist, it is better to get plugged into the research community on the AGI, deep learning, and NLU to receive the latest tech news.

On the other hand, the cloud vendors and other AI companies are innovating to commoditize the ML/AI algorithms such that these algorithms can be incorporated into products and services at ease. The ML as a Service- Cloud vendors such as Azure, AWS, and GCP offer out-of-the-box basic machine learning models in domains such as vision, language, forecasting, and so on. These basic machine models are pre-training and only need fine-tuning based on the customer’s proprietary datasets. These models empower many data scientists to build solutions quicker for solving customer-centric problems. Since these models are offered as services, there is less reliance on the infrastructure team and software developers to provision cloud machines and build code respectively.

The article has been written by Selvaraaju Murugesan, Data Strategist, Kovai.co

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