Learn More About Data Science and Machine Learning Platforms
Who Uses Data Science and Machine Learning Platforms?
Data scientists are in high demand, but there is a shortage in the number of skilled professionals available. The skillset is varied and vast (for example, there is a need to understand a vast array of algorithms, advanced mathematics, programming skills, and more) and therefore such professionals are difficult to come by and command high compensation. To tackle this issue, platforms are increasingly including features that make it easier to develop AI solutions, such as drag-and-drop capabilities and prebuilt algorithms.
In addition, for data science projects to initiate, it is key that the broader business buys into these projects. The more robust platforms provide resources that give nontechnical users the ability to understand the models, the data involved, and the aspects of the business which have been impacted.
Data engineers: With robust data integration capabilities, data engineers tasked with the design, integration, and management of data use these platforms to collaborate with data scientists and other stakeholders within the organization.
Citizen data scientists: Especially with the rise of more user-friendly features, citizen data scientists who are not professionally trained but have developed data skills, are increasingly turning to data science and machine learning platforms to bring AI into their organization.
Professional data scientists: Expert data scientists take advantage of these platforms to scale data science operations across the lifecycle, simplifying the process of experimentation to deployment, speeding up data exploration and preparation as well as model development and training.
Business stakeholders: Business stakeholders use these tools to gain clarity into the machine learning models and better understand how they tie in with the broader business and its operations.
What are the Alternatives to Data Science and Machine Learning Platforms?
Alternatives to data science and machine learning platforms can replace this type of software, either partially or completely:
AI & machine learning operationalization software: Depending on the use case, businesses might consider AI & machine learning operationalization software. This software does not provide a platform for the full end-to-end development of machine learning models but can provide more robust features around operationalizing these algorithms. This includes monitoring the health, performance, and accuracy of models.
Machine learning software: Data science and machine learning platforms are great for the full-scale development of models, whether that be for computer vision, natural language processing (NLP), and more. However, in some cases, businesses may want a solution that is more readily available off the shelf, which they can use in a plug-and-play fashion. In such a case, they can consider machine learning software, which will involve less set up time, as well as development costs.
There are many different types of machine learning algorithms that perform a variety of tasks and functions. These algorithms may consist of more specific machine learning algorithms, such as association rule learning, Bayesian networks, clustering, decision tree learning, genetic algorithms, learning classifier systems, and support vector machines, among others. This helps organizations looking for point solutions.
Software Related to Data Science and Machine Learning Platforms
Related solutions that can be used together with data science and machine learning platforms include:
Data preparation software: Data preparation software helps companies with their data management. These solutions allow users to discover, combine, clean, and enrich data for simple analysis. Although data science and machine learning platforms offer data preparation features, businesses might opt for a dedicated preparation tool.
Data warehouse software: Most companies have a large number of disparate data sources and to best integrate all their data, they implement a data warehouse. Data warehouses house data from multiple databases and business applications which allows business intelligence and analytics tools to pull all company data from a single repository. This organization is critical to the quality of the data that is ingested by data science and machine learning platforms.
Data labeling software: To achieve supervised learning off the ground, it is key to have labeled data. Putting in place a systematic, sustained labeling effort can be aided by data labeling software, which provides a toolset for businesses to turn unlabeled data into labeled data and build corresponding AI algorithms.
Natural language processing (NLP) software: NLP allows applications to interact with human language using a deep learning algorithm. NLP algorithms input language and give a variety of outputs based on the learned task. NLP algorithms provide voice recognition and natural language generation (NLG), which converts data into understandable human language. Some examples of NLP uses include chatbots, translation applications, and social media monitoring tools that scan social media networks for mentions.
How to Buy Data Science and Machine Learning Platforms
Requirements Gathering (RFI/RFP) for Data Science and Machine Learning Platforms
If a company is just starting out and looking to purchase their first data science and machine learning platform, or wherever a business is in its buying process, g2.com can help select the best option.
The first step in the buying process must involve a careful look at one’s company data. As a fundamental part of the data science journey involves data engineering (i.e., data collection and analysis), businesses must ensure that their data quality is high and the platform in question can adequately handle their data, both in terms of format, as well as volume. If the company has amassed a lot of data, the need is to look for a solution that can grow with the organization. Users should think about the pain points and jot them down; these should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees who will need to use this software, as this drives the number of licenses they are likely to buy.
Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more.
Depending on the scope of the deployment, it might be helpful to produce an RFI, a one-page list with a few bullet points describing what is needed from a data science platform.
Compare Data Science and Machine Learning Platforms Products
Create a long list
From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor.
Create a short list
From the long list of vendors, it is helpful to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions.
Conduct demos
To ensure the comparison is thoroughgoing, the user should demo each solution on the short list with the same use case and datasets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition.
Selection of Data Science and Machine Learning Platforms
Choose a selection team
Before getting started, it's crucial to create a winning team that will work together throughout the entire process, from identifying pain points to implementation. The software selection team should consist of members of the organization who have the right interest, skills, and time to participate in this process. A good starting point is to aim for three to five people who fill roles such as the main decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator, or security administrator. In smaller companies, the vendor selection team may be smaller, with fewer participants multitasking and taking on more responsibilities.
Negotiation
Just because something is written on a company’s pricing page, does not mean it is fixed (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others.
Final decision
After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board.
Implementation of Data Science and Machine Learning Platforms
How are Data Science and Machine Learning Platforms Implemented?
Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether that be an implementation specialist from the vendor or a third-party consultancy. With vast experience under their belts, they can help businesses understand how to connect and consolidate their data sources and how to use the software efficiently and effectively.
Who is Responsible for Data Science and Machine Learning Platforms Implementation?
It may require a lot of people, or many teams, to properly deploy a data science platform, including data engineers, data scientists, and software engineers. This is because, as mentioned, data can cut across teams and functions. As a result, it is rare that one person or even one team has a full understanding of all of a company’s data assets. With a cross-functional team in place, a business can begin to piece together their data and begin the journey of data science, starting with proper data preparation and management.
What Does the Implementation Process Look Like for Data Science and Machine Learning Platforms?
In terms of implementation, it is typical for the deployment of the platform to begin in a limited fashion and subsequently rolled out in a broader fashion. For example, a retail brand might decide to A/B test their use of a personalization algorithm for a limited number of visitors to their site, to better understand how it is performing. If the deployment is successful, the data science team can present their findings to their leadership team (which might be the CTO, depending on the structure of the business).
If the deployment was not successful, the team can go back to the drawing board, attempting to figure out what went wrong. This will involve examining the training data, as well as the algorithms used. If they try again, yet nothing seems to be successful (i.e., the outcome is faulty or there is no improvement in regards to predictions), the business might need to go back to basics and review their data as a whole.
When Should You Implement Data Science and Machine Learning Platforms?
As previously mentioned, data engineering, which involves preparing and gathering data, is a fundamental feature of data science projects. Therefore, businesses must set as their top priority getting their data in order, ensuring that there are no duplicate records or misaligned fields. Although this sounds basic, it is anything but. Faulty data as an input will result in faulty data as an output.
Data Science and Machine Learning Platforms Trends
AutoML
AutoML helps automate many tasks needed to develop AI and machine learning applications. Uses include automatic data preparation, automated feature engineering, providing explainability for models, and more.
Embedded AI
Machine and deep learning functionality is getting increasingly embedded in nearly all types of software, irrespective of whether the user is aware of it or not. The use of embedded AI inside software like CRM, marketing automation, and analytics solutions is allowing to streamline processes, automate certain tasks, and gain a competitive edge with predictive capabilities. Embedded AI may gradually pick up in the coming years and it may do so in the way cloud deployment and mobile capabilities have over the past decade or so. Eventually, vendors may not need to highlight their product benefits from machine learning as it may just be assumed and expected.
Machine learning as a service (MLaaS)
The software environment has moved to a more granular, microservices structure, particularly for development operations needs. Additionally, the boom of public cloud infrastructure services has allowed large companies to offer development and infrastructure services to other businesses with a pay-as-you-use model. AI software is no different, as the same companies are offering MLaaS to other businesses.
Developers easily take advantage of these prebuilt algorithms and solutions by feeding them their own data to gain insights from. Using systems built by enterprise companies helps small businesses save time, resources, and money by eliminating the need to hire skilled machine learning developers. MLaaS will grow further as businesses continue to rely on these microservices and as the need for AI increases.
Explainability
When it comes to machine learning algorithms, especially deep learning, it may be particularly difficult to explain how they arrived at certain conclusions. Explainable AI, also known as XAI, is the process whereby the decision-making process of algorithms is made transparent and understandable to humans. Transparency is the most prevalent principle in the current AI ethics literature, and hence explainability, a subset of transparency becomes crucial. Data science and machine learning platforms are increasingly including tools for explainability, which helps users build explainability into their models and help them meet data explainability requirements in legislation such as the European Union's privacy law, the GDPR.
