Why team-centric collaboration in a machine learning platform is vital to successful data science projects. Making team-based collaboration work for your data science project. Collaborative leadership: Creating a team-centric mindset.
Team-centric collaboration and collaborative leadership are key to successful data science projects. Let’s find out how.
As Machine Learning (M) gains traction in all industries, many organizations are contemplating ML strategies of their own. But, before undertaking your ML, Deep Learning, or Artificial Intelligence (AI) project, you’ll need to make an important technological decision: choosing the right ML Platform that will seamlessly guide you through the design and deployment of your ML application in the cloud.
An essential characteristic to look for in your ML Platform – Team-Centric Collaboration
An essential characteristic to look for in the ML Platform you’ll choose, is if it offers team-centric collaboration, and if the user-experience is team-centric. Building ML applications requires specialists, mainly data scientists and data engineers. Other collaborators might include a data analyst, a UI designer, a project manager, and even one of your business operations experts.
Why it should be easily to enable new team members to join
Your ML platform, if it offers team-centric collaboration, should make it very easy to set-up new users. Enabling all your team members to closely work together is important to the success of your project: It not only improves creativity and productivity but also allows your team to solve problems faster. With the right ML platform, remote workers will easily be invited to join your team, creating a nicer sense of community.
Why all users should access the same workbench
The platform you choose should allow individuals access to the same workbench and enjoy the same user-experience, team-centric collaboration and yet separately work on their own section of the application. Your team will have a common view of the project and the data associated with this project. They should be able to easily share components and check other dependencies to better complete their own tasks. This will allow them to avoid replicating tasks, and enable them to share the same dataset, AI/ML models, variables, scoring functions and assumptions. The knowledge-base they’ll build and share should also include all the data extraction and transformation rules, update frequency rules, business rules, and security settings.
What are the advantages of sharing a common knowledge-base
By sharing a common knowledge-base, your team will be able to build several different applications, based on the same dataset, or sub-sets. Several different projects, such as BI analytics, management dashboards and reports, and AI/ML applications should be able to be set-up and run on the same platform with team-centric collaboration.
Without a team-centric collaborative ML platform, it will be very difficult to pin-point the source of potential problems. The lack of collective ownership and the ample amount of finger-pointing will undermine your project quality, unnecessarily lengthen your project timeline, and corrode team morale.
How useful is a tools marketplace for team-centric collaboration
Your team-centric collaboration ML platform should offer a toolshed and a marketplace, where your team can choose the different tools and applications they’ll need to design your ML applications. This includes choosing the Cloud infrastructure you’ll run your ML application on, the data connectors, the data store and ML models, and the different visual bricks to assemble the final end-user application. The toolshed should also include popular third-party tools, templates and functions. Your teammates should also be able to add their own preferred tools, create new ones and share them. Choosing an ML platform where all the tools and applications you need are in one place, will allow you to focus on the business requirements and execute your ideas faster. Using separate tools and applications will only add more complexity to your ML project.
How valuable is a team-centric collaboration machine learning platform
As the volume of data needed to adequately train and test your ML models grows, you’ll need automated and collaborative features to scale your infrastructure. Otherwise, your data engineers will continuously be late and struggle with for example, setting-up larger data sets, and increasing computation speeds. Without a collaborative ML platform, it can take months to set-up your data infrastructure and design your ML application. It will take another few months to put your models into production. It’s been estimated that without a proper collaborative ML platform, up to 90% of a project time and resources can be spent on deployment alone!
There is one more aspect to be weary of, if your ML platform is not team-centric and collaborative. As your different team members work with their own tools and applications, write scripts to glue everything together, if one of them leaves the organization, you could find yourself loosing some crucial project knowledge.
How to choose a team-centric collaboration machine learning platform
Choosing an ML platform that offers team-centric collaboration is vital. It will offer higher transparency, provide a unified user experience for your team, make it easier and quicker to develop your ML application and deploy it. It will render your ML projects more agile and provide you with the ability to easily add new use cases within the same platform. The right collaborative ML platform will allow you to deliver your ML applications more efficiently while offering better long-term cost control. Try the ForePaaS Platform for free, and let us know how you like its team-centric collaborative features.
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