Developing AI applications comes with many challenges. A holistic MLOps platform could be the perfect solution to overcome all of them, accelerate development and achieve better results: Tredence explains why.
Despite the recent boom in interest regarding Artificial Intelligence, according to Gartner only 53% of AI projects make it from prototype to production. This is due to several big challenges enterprises still face when they try to jump on the AI wagon, and to entry barriers (economic or technical) still standing tall.
But predictions from Forrester forecast that in 2025 almost 100% of enterprises will have adopted some form of AI. So, how can companies set up AI applications in an easier, quicker and more efficient way? The solution could lie in MLOps.
What is the role of MLOps in AI application development?
There are several aspects of MLOps that can accelerate the AI application development cycle and increase the chances of success. As recognized also by McKinsley, there are excellent opportunities in operationalizing MLOps platforms in business processes and AI development, leading to shorter process times and increased model stability and efficiency thanks to workflow automation.
In particular, investing in an MLOps platform can offer companies important benefits to facilitate their digitalization process:
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Better collect, store and process data
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Boost the model creation process
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Improve collaboration between teams
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Implement real-time automated monitoring
Let’s dive a little bit deeper to understand how to use MLOps to build and deploy scalable Artificial Intelligence solutions for your company.
Setting up your AI application: using MLOps platforms to create the database
Data are the starting point of every Machine Learning and AI application; setting up a fast, robust, secure and monitored database is crucial, and should therefore be the number one priority when setting up the project.
First of all, data need to be collected and stored, then it should be secured with access rights to avoid data loss, manipulation and leaking. The data should be accessible in an efficient and quick way by all people with access rights.
MLOps provides tools to set up the desired type of database easily and quickly. This will allow you to save time and start your AI application sooner.
Save time with MLOps for model creation, testing and improving
Building an AI model involves a lot of experimentation, trial and testing. But the risk is to get stuck in the experimentation phase and never actually get the models out in the real world, one of the main reasons why so many AI projects fail before they even get to production.
Building a ML model isn’t easy: there is a high number of features, parameters, and models to take into account while testing and experimenting, and keeping track of everything is extremely hard. This causes many issues with experiments’ reliability and makes it difficult to replicate them.
Companies that are offering MLOps services are a great solution to avoid these criticalities. How?
MLOps ecosystems and platforms have integrated experiment-tracking tools. These tools help you centralize all useful information related to each experiment, and all the related metrics and visualizations. This grants a holistic view of all trials’ data, making it easier to analyze and compare all experiments in one graphical interface.
Ensure seamless collaboration between teams
AI application development is not possible without the involvement of cross-functional teams. The professional figures needed include data scientists, engineers, and operations personnel. Collaboration between different teams and departments can be challenging, especially when information should be shared in real time and with great accuracy.
MLOps platforms makes collaboration easier by establishing shared processes and tools and providing them to everyone involved. This enables data engineers to create pipelines sending clean and relevant data to data scientists, who then build and train models. Such collaboration accelerates the development cycle and results in AI applications that better align with business goals.
Achieve scalability and automation to increase the project’s profitability
Once set up and running, if feedback is positive, it is time to take your AI application to the next level by scaling it to achieve more efficiency, cut costs and bring value to stakeholders. At the same time, you want to automatize at least some tasks in order to enjoy the benefits predicted by Deloitte:
Organizations can expect an average 31% reduction in costs due to intelligent automation.
We at Tredence are well aware of the importance of achieving scalability and automation for your AI projects, and can offer the best MLOps solutions to overcome the challenges of scaling Machine Learning.
Overcoming MLOps challenges requires deep expertise and process understanding of both machine learning and all related operations. As reported in one of the latest McKinsley Global Survey, only 15% of companies worldwide have successfully scaled automation. If you want to be part of that winning 15% and gain competitive advantage, Tredence can support your company to set up MLOps at scale.
Taking the next step toward a new era for your company
As organizations are striving more and more to optimize processes and drive growth, MLOps can make a huge difference. A MLOps platform is an incredibly powerful tool to securely store data and build datasets, accelerate the development of AI applications, monitor processes, automate the workflow and achieve scalability.
Investing in MLOps and MLOps platforms could be your brand’s key to success. Contact us now if you are ready for your company’s next era.
