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Making Machine Learning More Accessible

Accessibility has become quite the buzzword in almost any industry you come across, and data is no different! Why should machine learning only be accessible to large companies with the budget and technical know-how to utilize it? We want every company, no matter their size, sector, or budget, to be able to reap the benefits of machine learning. It’s all about making the technology and tools realistic for anyone to participate, should they choose.

This can be a BIG DEAL in the world of data. There is amazing potential for businesses, but up until now, we’ve seen limitations in resources and technical skills to be able to start incorporating machine learning into an organization.

Common Limitations Within Machine Learning

A large part of machine learning to date has been focused on research and development, but more recently, there has been a shift toward how to productionize models (taking models from a testing environment into a live environment where it can be accessed by end users). With this shift, many businesses are realizing the potential of machine learning to their organization. As they jump in, they quickly face a few common limitations:

  • Understanding: Machine learning can be a fun buzzword, but many don’t understand what value it really adds to their business. They may not fully understand the strategic use case for machine learning and therefore don’t know where to start laying smart foundations for doing so.

  • Resources: The most common limitation we see in organizations that use a tool like mia is a lack of resources, or budget, to go through the entire machine learning life cycle. The organization may have some trepidation to jump fully into a machine learning tool and therefore are nervous about throwing too many resources into a project.

  • Size: Smaller teams often have the desire to leverage their data but lack the size to have team members devoted to building and productionizing effective models.

  • Skill Set: Your team may not have the skill set to focus attention on all aspects of the machine learning life cycle, in particular the productionizing of machine learning models. In such cases, you may find yourself perpetually stuck in the R&D stage, building models that sit untouched by the rest of the organization.

  • Time: Let’s face it: most data teams are thrown requests from all directions within an organization, and that can mean that priorities shift all the time. Having an agile tool to help you productionize valuable models in an efficient, effective manner is a game changer for your team!

  • Expectations: Machine learning models are all about iteration. Building a good and useful machine learning tool is a very iterative process. You don't want to lose momentum in your work, so having a tool like Mia means you can rapidly iterate and keep the momentum going.

There are usually two options when it comes to machine learning: a highly-customized option, or AutoML. The highly-customized approach requires deep machine learning expertise and takes significant time which means an organization has to be fully invested in the project, including allocating a large budget to it. As noted above, data science teams' demands are almost always in flux, and so the attention spent on something that is time and resource intensive, like modeling, is not always conducive. On the other hand, AutoML models, which are off-the-shelf solutions built for a specific use case (e.g. a recommendation engine), are generic and have limited use cases and often lower accuracies.

That is why increasing access to machine learning solutions is so vital, and why low-code and no-code solutions can be such amazing resources for data teams. This middle-ground solution offers a more customized solution that is specialized and accurate, but leverages many low-code technologies and efficiencies for teams that may not have the resources to do a fully-customized model.

Low-code and no-code tools have revolutionized traditional software development and app building, but are only just starting to scratch the surface when it comes to machine learning. They are opening up resources to innovators in all ranges of expertise. There are many no-code AutoML tools and low-code frameworks (like PyCaret) for model development, but not many to help put models into production.

Mia is a low-code/no-code tool that transforms machine learning models into full-stack apps in minutes. It does not require learning or hiring for new skills to take care of complex processes of deployment, allowing data scientists to focus on what they do best instead: building models. While extremely nimble, it also has the ability to be further customized to fit specific needs. Users from novices to experts can save time with mia while producing high-quality models for their data that can be used in real-world applications.

Can your 2-year-old create a model with a no-code tool? Probably not. But neither should a data scientist need to become an advanced DevOps expert to put their model to use. It puts the power of data back in the hands of real people who are looking to leverage data to bring real solutions to the world. Think about the evolution of website design: when websites first came to the scene, they were complex and required custom code at every turn. Today, website builders and content management systems allow anyone to spin up a basic website. Accessibility has many more applications than we ever dreamed, and it’s making it realistic for so many people with great ideas to get them into action!

To find out how you can start using machine learning to get more out of your data give us a shout at


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