Although according to a 2020 McKinsey study¹, 50% of the companies surveyed had already adopted AI in at least one business function, the state of AI in 2023 according to a similar McKinsey study suggests that adoption rates have effectively plateaued over the last 3 years².On the other hand, in the same survey, 2/3rds of the respondents expected their respective organizations to increase AI adoption in the next 3 years.

For example, Generative AI has garnered widespread interest since OpenAI’s ChatGPT launch in early 2023, with several studies, including McKinsey’s 2023 state of AI report², suggesting that this may well be Generative AI’s breakout year. Organizations around the world are already seeing immense value in Gen AI and have now started to explore other areas of adoption as well.

With this context, conversations around the importance of AI/ML platforms to get AI applications up and running seem inevitable. AI/ML platforms can potentially accelerate the entire lifecycle from model training and preparation, to deployment and integration.

A critical decision each organization must make is the choice of an AI/ML platform. This decision can significantly influence the success that companies have in rolling out their AI initiatives.

Organizations look at AI/ML platforms as a means to take care of the non-differentiated heavy lifting involved with AI applications. The use of an AI/ML platform eases the process of developing ML models and AI applications. As the focus for each enterprise should be on the machine learning models, insights and applications within their problem domain, AI/ML platforms can be seen as enabling infrastructure.

AI/ML platforms enable the efficient development and deployment of AI applications in collaborative environments using the latest advancements in machine learning.

The importance of using an AI/ML platform

When AI/ML platforms are implemented effectively, they can reduce operational costs, improve productivity and help grow revenues. Because AI/ML platforms can solve for the infrastructure problem, the data-to-decisions journey is shorter with patterns and trends identified more quickly and more intuitively from within the data. Business leaders must select the best platform to create and operationalize ML models and AI applications at speed and scale to stay competitive in the market.

Management of the entire ML model lifecycle

Machine learning platforms support each step of the model lifecycle, starting with data provisioning. Platforms often have data discovery mechanisms and connectors that make it easy to feed data into the machine models. A few years ago, data sets were used to train models, and then later, data feeds were established for use in production. Now, data pipelines can be created that include any necessary pre-processing steps. Data enrichment is still needed, coupled with any essential translation, formatting or quality control measures defined as part of the pipeline.

Typically, AI solutions require many iterations before the final version is ready for production. Many training runs may be necessary, and testing on real-world data is critical. As multiple roles working together is critical for AI success, collaboration is also supported by AI/ML platforms. Data scientists and business analysts are heavily involved in the training and preparation phases, as well as post-deployment analysis. The platforms also handle the deployment of models, which requires coordination with application developers and IT operations. There are numerous handoffs needed throughout the process and these well-crafted platforms are designed to facilitate seamless transitions that speed up the lifecycle.

As multiple stakeholders are involved in this process, including engineers, analysts, and data scientists, the ability to rapidly gather feedback and incorporate it into the development process is vital for feature velocity. This is where AI/ML platforms shine and show their worth, as they rapidly facilitate team development and model deployment.

Another key advantage of AI/ML platforms is that organizations are not tied to a single framework or model implementation. Platform vendors make it easy to leverage multiple frameworks and introduce new ones over time quickly. Different models can run on various frameworks simultaneously. They all can easily co-exist using the platform. The latest updates can be applied as well so that organizations can quickly take advantage of the latest innovations. Platforms often include tooling that sits on top of the frameworks, making it easier to build models and leverage new features for customers.

Deploy AI applications, not just models

Use cases often involve integration with other enterprise applications and the delivery of data, intelligence, and insights to stakeholders. The knowledge and insights produced by AI/ML technology are only useful once the appropriate stakeholders can leverage them. Thus, AI/ML platforms have evolved in scope to the point where they can be used to build entire applications with minimal coding efforts.

Custom software development is no longer a requirement to make the capabilities available to all stakeholders. Many platforms can now be used to create user interfaces, applications and workflows on top of their machine learning capabilities. The tools offered by these platforms provide the ability to orchestrate multiple models into overarching workflows. The result is no longer just machine learning components but low-code/no-code AI solutions ready to be deployed.

Governance and monitoring capabilities are also built into the platforms to ensure applications perform as expected. ML models can be adjusted as needed, and continuous deployment mechanisms make it easy to roll out updates. Decision-making has become a continuous process for many organizations;therefore, real-time insights are critical to the success of the business. Data streams are constantly being analyzed and processed, and AI/ML platforms enable teams to act on this feedback loop and iterate quickly. Sharing analysis and insights is enabled using dashboards, charts and other integration mechanisms.

Choosing the right AI/ML platform

Now that we understand the importance of AI/ML platforms, we can appreciate the need for choosing the right AI/ML platform for your business. Machine learning models are at the heart of AI applications and AI/ML platforms provide the tooling to build, deploy and manage these ML models.

Forrester published in their 2022 report “Now Tech: AI/ML Platforms” that vendors provide tooling in three main product/service designs. These include:

1. Multimodal vendors

Multimodal vendors provide various user interface mechanisms, including machine learning tools such as visual data pipeline builders. Data visualization and analysis capabilities are also offered using visual mechanisms. A benefit of this approach is that team members do not require coding skills to use the tools.

2. Code-first vendors

Code-first vendors believe that programming languages are the preferred mechanism used to build and manage machine learning models. These platforms often focus heavily on using open-source notebooks such as Jupyter. Visual tooling in these products is typically oriented around the coding environment used to implement the capabilities.

3. AI-as-a-service

AI-as-a-service vendors offer AI models that are ready to use. Data Scientists can use these artificial intelligence services individually or in combination to add AI functionality directly to their applications.

Improve productivity with the Refract AI/ML platform

Now that you know why and how to choose an appropriate AI/ML platform for your business, here’s your answer to “which platform?”

Fosfor is an integrated/multimodal suite of products that helps businesses monetize data at speed and scale out AI capabilities across the enterprise. A foundational component of this suite is Refract, an enterprise AI/ML platform that leverages the best frameworks and templates to prepare, build, train, and deploy high-quality Machine Learning (ML) models. It easily integrates with Continuous Integration/Continuous Deployment (CI/CD) solutions enabling touchless ML deployment on any cloud, on-premises, or hybrid environment.

Furthermore, LTIMindtree, Fosfor’s (Product: Refract) parent company, was recently mentioned in the Forrester report “Now Tech: AI/ML Platforms, Q1 20221, an overview of 35 AI/ML platform providers.”³

Here’s a breakdown of the various business advantages Refract offers:

1. Develop with ease

Refract’s multimodal product design reduces the effort needed to develop and deploy AI applications by up to 70%. It uses many innovative features, including no-code user interfaces and automated capabilities that significantly reduce time and effort. Data pipelines are easy to create using the integrated data catalog. Discover and connect to various data sources and enrich data before processing using the intuitive and straightforward no-code interface. Take advantage of an extensive library of connectors that supports structured, semi-structured, and unstructured data formats.

2. Mediate project lifecycles

Use Refract to manage all your ML models in one place. The entire lifecycle is taken care of, so that you can focus on your models and insights rather than infrastructure. Refract facilitates the rapid preparation and training of machine learning models. It automates model deployment, governance, and monitoring. Alerts are automatically issued when changes are detected in the data, or if thresholds are breached.

3. Innovate with top-tier tools

Refract supports multiple development environments, including Jupyter, Apache Spark, R Studio, and VSCode. Use custom libraries and code or leverage pre-built notebooks and ML libraries.

End-user applications based on the ML models can be built rapidly using Refract. Choose from numerous pre-built integrations to quickly leverage the AI capabilities in your applications and business processes. The “Build-to-Run” feature allows users to customize their workflows and run different variations.

4. Extract more from your insights

With Refract’s multimodal design, users can also seamlessly add Explainable AI (XAI) capabilities to their workflows using Refract’s model interpretability feature. This capability builds trust in the solutions by adding transparency to decisions made by the models. Furthermore, Refract provides feature importance and partial dependency graphs that give insight into the data and the model. These tools provide background on the relative importance of each feature or input parameter when making a decision or prediction.

Conclusion

Ultimately, AI/ML platforms accelerate creating and deploying applications based on machine learning technology. As such, choosing a well-crafted AI/ML platform is essential for any organization looking to build and deploy AI solutions. Platform tools typically take either a code-first, AI-as-a-service, or a multimodal approach, as in Refract’s case.

Increasingly, platforms are providing the ability to create AI applications instead of just the underlying ML components. The Refract AI/ML platform offers a complete end-to-end solution enabling teams to develop and deploy models and AI solutions rapidly.

Refract’s multimodal approach leverages both coding environments and visual tooling. This approach offers the most advantage by providing interfaces that do not require coding skills. It is usable by various team members with various skill sets. The Refract AI/ML multimodal platform offers a complete end-to-end solution with visual tooling that enables teams to create and deploy ML models, applications and AI solutions rapidly. It saves organizations up to 70% of the effort to build and deploy AI applications and delivers significantly faster time-to-value.

References:

1. The state of AI in 2020: McKinsey Survey

2. The state of AI in 2023: McKinsey Survey

3. Now Tech: AI/ML Platforms, Q1 2022: Forrester