Why enterprises need ML & AI platforms to accelerate their data science journey?

The state of the market

Enterprise AI is expanding at an unprecedented rate. The cost of storing data has decreased significantly, companies are collecting tons of data every second in multiple different formats than ever before. In order to stay relevant, these companies need to figure out how to best utilize the mountain of data they’re sitting on. This utilization of data will not only help these companies make better decisions but will also help them drive their business forward.

Today, organizations are more than willing to implement machine learning but they don’t know how to start. In this article, we’ll learn why machine learning tools are an essential part of any AI strategy and how they can jumpstart any organization’s AI strategy.

Sadly, many companies have yet to benefit from leveraging data. Most of the companies today still struggle with basic issues like collecting, preparing & cleaning data for analysis. For many who have successfully tackled the data collection & preparation part, they struggle with building machine learning algorithms and then deploying these algorithms into production, which in simple terms means using those algorithms to generate predictions. It’s because, sometimes, finding just one model is far more expensive & time-consuming than they actually planned for. A lot of iterations are required and a lot of time is spent on trying to find the model that performs the best on the real-world data.

But the companies who have successfully managed to make progress towards enterprise AI have realized the value of machine learning algorithms and have benefited from the results produced by the algorithms. In addition to that, many companies who’ve come so far have witnessed that it’s not just one machine learning model that makes the difference but hundreds and even thousands of them, depending on multiple different use cases. This means that the linear thinking needs to change and companies need to start using tools that will help them accelerate their AI efforts.

Hiring data science experts: crucial but not enough.

The demand for data scientists is soaring right now. Every company is looking for experts who can help them gain a data-edge over the competitors and make better predictions about the future. Unfortunately, these professionals are expensive. In addition to that, it’s also very difficult to find quality data science experts because it’s such a new field where lack of validation still exists.

In the industry, scaling machine learning efforts is not easy. Even when you have a few data scientists on your team, scaling your operations means building and maintaining hundreds of algorithms. This process is very repetitive and time-consuming. A single data science team will not be enough and you need to equip them with tools that will add to their productivity.

Not only that, but a lot of organizations also complain that there is a gap between data science knowledge and business knowledge due to which even data scientists sometimes have trouble explaining the business value to the stakeholders. This is largely because data scientists currently spend around 80% of their time preparing, cleaning data and then building models. Only 10% of their time is spent on analysis. Therefore, we need to equip these professionals with tools that can help them accelerate data science initiatives and make them focus more on generating business value rather than writing code & maintaining systems.

Open-source tools: critical but not enough

There is no question that open source technologies in the data science and machine learning fields are nothing short of state-of-the-art. In fact, it won’t be wrong to say that the bleeding edge of machine learning algorithms and architecture is only about six months ahead of what is being open-sourced.

Open-source also makes it easier to have a centralized framework that makes hiring & onboarding a team easier. Data science professionals who have been working with such technologies require less time and start using them in the organization but open-source only works if the data science professional has exceptional coding skills - data science is not just about writing code in python or R but also includes setting up cloud servers and deploying the models in production so that business value can be generated.. This is the place where open-source tools fail to scale up. As a result, the gap between the data science department and the business units continues to grow.

After the implementation of mltrons, building accurate predictive models was achieved in less than 1 day and predictions were made in real-time. The machine learning model built was 91% accurate which ultimately equated to an increase of up to 10% in unrealized investment return.

Enter data science, AI & ML platforms

Data science & ML platforms, like mltrons dp2, have the advantage of being usable right off the shelf from day one. With open-source, you need to assemble different parts together. In comparison, these data science platforms have an in-build data science pipeline that actually helps companies save money & time while accelerating data science initiatives within an organization. Using a data science platform will enable these organizations to benefit from open-source while getting expertise from data scientists working behind these platforms. These off-the-shelf tools give access to cutting edge technology, accessibility, governance and control over data project. But more importantly, they give speed and accessibility.

The main purpose of these platforms is to democratize AI within the organization. A successful AI strategy requires different business units working together so these platforms and tools enable business professionals and data science to:

  1. Use data to make predictions (build machine learning-powered use cases
  2. Provide transparency & reproducibility throughout the team and within a project
  3. Bring different stakeholders together and access the value of the data science initiative as a whole

In the end, data science and ml platforms are all about time. This means time savings in all parts of the process (from data preparing to machine learning models to making business decisions). These tools also allow for quick prototyping, making it easier for organizations to start with machine learning. Getting started on the AI journey is intimidating, but data science and ML platforms can ease that burden and provide a framework that allows companies to learn as they go.

mltrons AutoML & Explainable AI Platform

Enter mltrons do2: Mltrons dp2 is an analytical tool that is very carefully designed for data analysts & business analytics professionals to help them leverage machine learning & deep learning algorithms without writing code or worrying about managing cloud infrastructure. It uses the principles of AutoML and serverless AI implementation to bring together the latest open-source libraries and autoML libraries in one place powered by our state-of-the-art AutoDataPreparation library that brings together best-industry practices on data preparation for optimal results in just a few clicks. Mltrons is predictive & prescriptive analytics in five simple clicks.

What sets mltrons AutoML & Explainable AI apart?

Specially designed for medium sized enterprises

mltrons aims to empower the smaller-medium sized businesses with the latest machine learning technology and help them make better predictions. These SMEs are sitting on a large volume of data but they lack the resources, talent & time to build out internal analytical systems that can add value to the entire organization. Mltrons provides them with the entire data science pipeline deployed on AWS without having them bust their banks. More than that, mltrons allows these organizations to utilize “citizen data scientists” within their organization and get maximum value from AI-driven initiatives.

White Box ML

mltrons propagates the idea of transparency and explainable AI. mltrons provides complete transparency of where the data is hosted, which algorithms are used, what features were selected, what logic did the best model take in order to give a certain prediction so that the stakeholders can know how much to trust the system.

Most importantly, users can dive deep into the critical aspects of model behavior. They can get decision trees, feature importance graphs, global feature impact graphs, local feature impact graph, partial dependence plots, simulation, scanerio analysis and many more!

Intuitive UI & Dedicated Support:

mltrons uses the idea of “guided analytics”. The interactive user interface comes with pointers to guide the user on generating maximum value out of the machine learning project. More than that, mltrons team works closely with clients to ensure they get the maximum value out of the platform.

Bridges the gap between data science & business teams

Mltrons uses its own version of the AI project canvas that closely tracks the progress of an ML project and brings different stakeholders that work together to ensure a successful use case within the organization.

Customers across various industries like retail, e-commerce, marketing consulting, and more use mltrons to drive their AI strategy and move towards self-service analytics while using state of the art machine learning & deep learning algorithms to make accurate predictions in the fastest way possible. Mltrons empowers these businesses with AutoML, explainable AI & a successful AI implementation that they need to become an AI-driven enterprise.

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