Choosing the best AI/ML platform from a multimodel vendor

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Artificial intelligence (AI) and machine learning (ML) technologies are expanding rapidly as organizations seek to capitalize on the value of their data. Half of the companies surveyed in a 2020 Mckinsey study have already adopted AI in at least one business function. At this stage, 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 the technology. AI/ML platforms accelerate the entire lifecycle from model training and preparation to deployment and integration.

Organizations look to AI/ML platforms to take care of the non-differentiated heavy lifting involved with AI applications. This concept is analogous to the migration from on-premise hardware to cloud infrastructure. There is no value in tackling that complexity in-house or managing it yourself in most cases. The focus for each enterprise should be on the machine learning models, insights, and applications within their problem domain, not on the infrastructure. AI/ML platforms enable the efficient development and deployment of AI applications in collaborative team environments using the latest innovations in machine learning.

The benefits of an AI/ML platform

When ML platforms are implemented effectively, they can reduce operational costs, improve productivity, and help grow revenues. Future decisions can be driven using patterns and trends identified within the data. Countless use cases extend from the value unlocked within the data. Because of these benefits, business leaders need to select the best platform for their needs to create and operationalize ML models and AI applications. Offerings from vendors in this space vary in several dimensions, including size, capabilities, and vertical market focus. 

Machine learning models are at the heart of AI applications. AI/ML platforms provide the tooling to build, deploy, and manage ML models. Forrester, a top technology analyst company, published in their 2022 report Now Tech: AI/ML Platforms, Q1 2022 that vendors provide tooling using 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

Forrester also explains that 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.”

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 production use. 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.

As collaboration across multiple roles is critical for success, team development 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 artificial intelligence platforms handle the deployment of models, which requires coordination with application developers and IT operations. There are numerous handoffs needed through the process, and these well-crafted platforms are designed to facilitate seamless transitions that speed up the lifecycle.

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

A 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 where machine learning is at the core.

Custom software development is no longer a requirement to make the capabilities available to the enterprise or customers. Many platforms can now be used to create user interfaces, applications, and workflows on top of the machine learning capabilities. The tools 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 put to use.

Governance and monitoring capabilities are also built into platforms to ensure applications are performing 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 continual process for many organizations. Thus, real-time insights are critical to 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.

Improve productivity with the Refract AI/ML 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.

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.

Mediate project lifecycles

Use Refract to manage all of 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 thresholds are breached.

Innovate DevOps 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.

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. 

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 multimodal approach.

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 with visual tooling 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 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, aplications, and AI solutions rapidly. It saves organizations up to 70% of the effort to build and deploy AI applications significantly faster. 

Furthermore, LTI (Product: Refract) was recently mentioned in the Forrester report Now Tech: AI/ML Platforms, Q1 20221, an overview of 35 AI/ML platform providers.

Forrester, “Now Tech: AI/ML Platforms, Q1 2022: Forrester’s Overview Of 35 AI/ML Platform Providers” Mike Gualtieri, Srividya Sridharan, Gabrielle Raymond, Jen Barton, March 28, 2022.


Sourabh Sharanathe

Product Marketing Manager, Aspect by Fosfor

Sourabh is an ROI-driven product marketer with over 8 years of experience in Digital marketing, GTM, branding, and business development across AI & ML products and services. He loves to work on challenging tasks and believes that learning and exploring new topics is the key to growth. He is currently responsible for building the global Go-To-Market (GTM) strategy for Fosfor’s intelligent document processing platform, Aspect. In his free time Sourabh enjoys exploring new travel destinations and meeting new people.

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