Emerging Trends and Technologies in Data Science

The field of data science is emerging at a faster rate than ever. Time to value (results of business activity) is the primary business driver for companies to innovate to exceed customer expectations. The data science team helps companies be customer-centric by offering contextual recommendations, solving problems and achieving more customer value.

Data science used to be a complex and process-heavy field where practitioners have mastered data science skills over the years. Data science practitioners know the right framework, processes, and optimal technology 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 ML/AI algorithms so that these algorithms can be easily integrated into products and services. Many software vendors offer ready-to-use basic machine learning models in areas such as vision, language, prediction, and more. These basic machine models are pre-trained and only require fine-tuning based on the customer’s proprietary data sets. These models allow many data scientists to more quickly create solutions to solve customer-centric problems. Since these models are offered as services, there is less reliance on the infrastructure team and software developers to provision the cloud machines and author the code respectively.

Many third-party vendors offer custom machine learning models as APIs at a lower price than major cloud providers. These paid third-party APIs allow data scientists to avoid reinventing the wheel and quickly deploy solutions. These third-party vendors also provide support for customizing their machine learning algorithms using proprietary data for each company. This leads to accessing custom ML models through their APIs.

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

There are two areas where data science is emerging, namely algorithm development and technological tools. New algorithms are developed using large amounts of data and training larger models, so many algorithms can be generalized. This is particularly focused on the area of ​​natural language understanding (NLU) in which language models are generalized. This leads to the creation of sophisticated algorithms for word auto-completion, prompts, question-answering, text summarization, content generation, etc.

Using Graphics Processing Units (GPUs) instead of generic Central Processing Units (CPUs) to train models with huge amount of data in less time is the latest trend in rapid computer model building. personalized machine learning. Frameworks like MXNet, PyTorch, TensorFlow use GPUs to train deep learning models in frameworks. Computer vision models are pre-trained, making face detection, image classification, and objection detection easier to implement with a few lines of code.

Deep learning is getting a lot of attention these days, as it achieves unprecedented levels of precision, comparable to humans. Google’s Alpha Go Algorithm that beats the best Go player in the world is based on deep learning algorithms and their implementation. The next frontier of data science is the realization of artificial general intelligence (AGI), through which an AI achieves human-level intelligence and the ability to learn more generically rather than perform specific tasks. . There is a lot of research going on in the area of ​​AGI and deep learning. If you are an aspiring data scientist, it is best to connect with the AGI, Deep Learning and NLU research community to receive the latest technology news.

On the other hand, cloud providers and other AI companies are innovating to commoditize ML/AI algorithms so that these algorithms can be easily integrated into products and services. ML-as-a-Service cloud providers such as Azure, AWS, and GCP offer basic out-of-the-box machine learning models in areas like vision, language, prediction, and more. These basic machine models are pre-trained and only require fine-tuning based on the customer’s proprietary data sets. These models allow many data scientists to more quickly create solutions to solve customer-centric problems. Since these models are offered as services, there is less reliance on the infrastructure team and software developers to provision the cloud machines and author the code respectively.

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

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