Humans invented computers to solve their data intensive problems in an efficient manner. To communicate with them, programming languages were created. This blog post discusses why Accern coded for no-code AI.
Over the past few decades, programming languages have evolved from labor intensive and error prone Assembly Languages to high level programming languages, with support for concurrency and distributed computing. Despite these advances, writing code has continued to be a highly specialized skill that requires years of practical experience before one could program a computer to solve a complex problem at scale.
Why We Coded for No-Code AI
With the recent advances made in the fields of AI and NLP, the list of problems that require massive computation has increased substantially. These challenges aren’t just limited to financial use cases like credit risk analytics and anti money laundering but also include areas in health care and climate change. It is more necessary than ever to help the scientific community communicate their problems to the computer and instruct it to solve their use-cases without requiring them to spend years learning the syntax of Kotlin, R, Python, Javascript etc. According to an IDC Survey, global spending on artificial intelligence (AI) is forecast to double over the next four years, growing from $50.1 billion in 2020 to more than $110 billion in 2024. Another NLP Industry Survey found out that NLP budgets in 2020 grew between 10% to 30% compared to 2019.
Our mission at Accern is to lower the barrier to entry for utilizing AI to solve real-world problems. After spending the first few years interacting with business users at various financial firms, we decided to build reusable components that would be generic enough to solve a large array of problems, yet have the potential to be customized for specific requirements. Accern’s No-Code AI platform provides a solid foundation for business users and data scientists alike to immediately build and deploy NLP use cases. While a traditional AI platform requires months (and occasionally years) of effort with substantial investment in capital expenditures, Accern enables users to harness the power of AI at a fraction of the cost with massive gains in performance, accuracy and productivity.
Common Issues with No-Code AI
To build a no-code solution, we identified the most common issues that NLP use cases encounter:
- Functional issues:
- Business use case not supported
- Lack of customization in the models
- Accuracy of the models
- Technical issues:
- Difficulty integrating with existing tools
- Scalability issues
- Security in the cloud
- Rising infrastructure cost
While there are several “no-code” products that have simplified the process of building web applications, building such a tool for AI still requires highly specialized skills. Accern has had the privilege of solving real world problems faced by the business users and we have been able to capitalize on this knowledge to build our “no-code” AI platform. We had to “code” to enable “no-code”.
With our deep expertise in building AI-driven use cases while processing hundreds of millions of unstructured documents, we were able to identify the common components that substantially reduced both the cost and time taken for running NLP models on a large dataset.
Solving the Functional Issues of No-Code AI
Use-case driven pipelines
Accern has adopted a use-case driven approach to build and deploy the NLP models. Accern’s AI Marketplace empowers data scientists and business analysts with over 400+ ready-made AI use cases to automate manual workflows and enhance decisions.
Use cases include, but are not limited to ESG Behaviors, Covid-19 Impact on Credit Risk, Anti-Money Laundering Analytics, Mergers and Acquisitions, and more. These ready-made use cases are backed by AI and adaptive NLP (natural language processing) to allow financial services teams to quickly research, summarise, and extract data, and gain investment insights and manage risk.
Pre-Built and Custom Use-Cases
Clients can choose either one of these pre-built use cases or deploy use cases specific to their own requirements. The workflow is secure and available as a self
-service tool in Accern platform giving the users the ability to deploy use cases within a few minutes vs. what would take months (or years) to build and deploy.
Benchmarked for Accuracy
We offer an industry leading NLP platform with both pre-built and custom ML models for finance. Our adaptive NLP models have undergone rigorous evaluations. Customers have access to our benchmark datasets a to compare and evaluate our performance with third-party models. For instance, Accern’s Entity Extraction and Sentiment Analysis models have 99.7% and 93.5% accuracy rates respectively.
Solving the Technical Issues of No-Code AI
Source: Accern: Accern's No-Code AI Platform architecture
Connectors for Integrations
Accern supports a wide range of connectors for sourcing data as well as for sending the output of a pipeline. Clients not only have the ability to choose the connector they prefer but also choose the format of the data they want to send. The platform provides out-of-box support for connections to Snowflake, Elastic, Microsoft Azure, Amazon S3, File Upload etc.
The connectors are deployed in a highly resilient infrastructure available across multiple data centers for high availability.
Scalable Adaptive Infrastructure
Due to the scale of the data we had to process, it was important to build our solution as a Cloud native application. The platform needed to support both multi-tenancy and on-premise deployment to accommodate different clients’ needs. We solved this problem by using Kubernetes for managing containers.The auto scaling groups allowed us to scale the appropriate number of resources to run a pipeline depending on the workload.
We also made a conscious decision to manage the core technology stack including the databases, interprocess messaging and data pipeline ourselves. This gives us the ability to deploy the platform on any SaaS and Cloud provider thereby avoiding vendor lock-in.
Using Grafana and Prometheus, system telemetry is monitored in real-time for monitoring and alerting of the services in the cloud.
Cost Optimization
On average, 80% of the cloud infrastructure costs are due to compute resources. Accern’s no-code AI platform reduces the costs of deploying NLP use cases by categorizing the computing resources into different categories (Stateless, Serverless, Stateful etc).
When a use case is deployed, Accern’s platform determines the expected compute resource requirements by utilizing proprietary metrics called “Units”. This also helps in ensuring that the resource acquisition is performed in a cost-effective manner. Shared components are made accessible via APIs to further reduce the costs for running complete pipelines.
Isolated Resources for Security
The services are deployed in a Secure VPC with automated monitoring and alerting enabled for each pipeline. The services are distributed across multiple data centers for high availability. With regular backups and best practices for data recovery, we are able to assure our clients that the data will be available even in case of a Disaster Recovery (DR) scenario.
For each pipeline, client-specific components are deployed in their own isolated containers to guarantee both the availability as well as security in a multi-tenancy environment.
Access to Production data is highly restricted via RBAC thereby guaranteeing the integrity and security of the data.
What’s Next?
With a fully automated platform to build and deploy NLP use cases, we are building the ability to perform Predictive Analytics in a no-code manner from the platform. This will support model explainability and “what-if” scenarios.
It is unlikely that no-code will completely eliminate the need for writing code. There will always be developers working at companies like Accern writing the necessary code to push the no-code movement further. What is certain, though, is that no-code platforms will continue to become more widely accepted as we uncover new challenges across humanity. As technologists, we will strive to make it easier for business users to utilize the power of AI.
“Those who have the privilege to know have the duty to act, and in that action are the seeds of new knowledge.”- Albert Einstein
About Accern
Accern enhances artificial intelligence (AI) workflows for financial services enterprises with a no-code AI platform. Researchers, business analysts, data science teams, and developers use Accern to build and deploy AI use cases powered by adaptive natural language processing (NLP) and forecasting features. The results are that companies cut costs, generate better risk and investment insights, and experience a 24x productivity gain with our smart insights. Allianz, IBM, and Jefferies are utilizing Accern to accelerate innovation. For more information on how we can accelerate artificial intelligence adoption for your organization, visit accern.com