Question 1

What are data science and machine learning (DSML) platforms?

Accepted Answer

The amount of data being produced within companies is increasing rapidly. Businesses are realizing its importance and are leveraging this accumulated data to gain a competitive advantage. Companies are turning their data into insights to drive business decisions and improve product offerings. With data science, of which artificial intelligence (AI) is a part, users can mine vast amounts of data. Whether structured or unstructured, it uncovers patterns and makes data-driven predictions. 
 One crucial aspect of data science is the development of machine learning models. Users leverage data science and machine learning engineering platforms that facilitate the entire process, from data integration to model management. With this single platform, data scientists, engineers, developers, and other business stakeholders collaborate to ensure that the data is appropriately managed and mined for meaning.

Question 2

Types of DSML platforms

Accepted Answer

Not all data science and machine learning software platforms are designed equal. These tools allow developers and data scientists to build, train, and deploy machine learning models. However, they differ in terms of the data types supported and the method and manner of deployment.  
 Cloud data science and machine learning platforms 
 With the ability to store data in remote servers and easily access it, businesses can focus less on building infrastructure and more on their data, both in terms of how to derive insight from it and to ensure its quality. Cloud-based DSML platforms afford them the ability to both train and deploy the models in the cloud. This also helps when these models are being built into various applications, as it provides easier access to change and tweak the models that have been deployed. 
 On-premises data science and machine learning platforms 
 Cloud is not always the answer, as it is not always a viable solution. Not all data experts have the luxury of working in the cloud for several reasons, including data security and issues related to latency. In cases like health care, strict regulations, such as HIPAA, require data to be secure. Therefore, on-premises DSML solutions can be vital for some professionals, such as those in the healthcare industry and government sector, where privacy compliance is stringent and sometimes necessary. 
 Edge platforms 
 Some DSML tools and software allow for spinning up algorithms on the edge, consisting of a mesh network of data centers that process and store data locally before being sent to a centralized storage center or cloud. Edge computing optimizes cloud computing systems to avoid disruptions or slowing in the sending and receiving of data.

Question 3

What are the common features of data science and machine learning solutions?

Accepted Answer

The following are some core features within data science and machine learning platforms that can help users prepare data and train, manage, and deploy models. 
 Data preparation: Data ingestion features allow users to integrate and ingest data from various internal or external sources, such as enterprise applications, databases, or Internet of Things (IoT) devices. 
 Dirty data (i.e., incomplete, inaccurate, or incoherent data) is a nonstarter for building machine learning models. Bad AI training begets bad models, which in turn begets bad predictions that may be useful at best and detrimental at worst. Therefore, data preparation capabilities allow for data cleansing and data augmentation (in which related datasets are brought to bear on company data) to ensure that the data journey gets off to a good start. 
 Model training: Feature engineering transforms raw data into features that better represent the underlying problem to the predictive models. It is a key step in building a model and improves model accuracy on unseen data. 
 Building a model requires training it by feeding it data. Training a model is the process of determining the proper values for all the weights and the bias from the inputted data. Two key methods used for this purpose are supervised learning and unsupervised learning. The former is a method in which the input is labeled, whereas the latter deals with unlabeled data. 
 Model management: The process does not end once the model is released. Businesses must monitor and manage their models to ensure that they remain accurate and updated. Model comparison allows users to quickly compare models to a baseline or to a previous result to determine the quality of the model built. Many of these platforms also have tools for tracking metrics, such as accuracy and loss. 
 Model deployment: The deployment of machine learning models is the process of making them available in production environments, where they provide predictions to other software systems. Methods of deployment include REST APIs, GUI for on-demand analysis, and more.

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