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AI and Machine Learning

MLOps helps you successfully integrate AI and ML into scalable architectures

With technology changing in the blink of an eye, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as pivotal capabilities for scalable architectures. These technologies can transform enterprise systems to process vast quantities of data, all while learning, refining, and optimizing your business operations and product or service offerings.

AI and Machine Learning Enhance Scalable Architectures

AI and ML bring an unprecedented level of possibilities to scalable architectures. When built, trained, and integrated correctly, they allow for real-time decision making, predictive analytics, and automated responses as business environments change.

Once AI or ML are integrated into your technology landscape, scalable systems become more able to adapt to new challenges and opportunities. Yes, your scalable architectures will be robust and resilient – but they will also become predictive, proactive, and intelligent.

Ultimately, this digital transformation drives innovation, enhances competitive advantage, and fosters sustainable growth in a data-driven world.

For example, AI can be used to:

  • Summarize vast quantities of information from disparate sources to identify sales or market opportunities
  • Evaluate pending contracts for internal compliance and fairness
  • Pull data together and create a holistic view of a project or process
  • Replace legacy algorithms and analytics
  • Augment human productivity
  • Power a data platform for continuous learning

Some specific examples include:

  • Manage pre-qualifications, dynamic pricing, loan origination, collections, and servicing
  • Analyze new market/industry regulations for gaps from current state
  • Build infrastructure and tools for your data pipeline
  • Create a recommendation system for new products based on consumer information
  • Detect and analyze software and network vulnerabilities and threats

Scalability first, AI and ML second

If your company uses older systems built on a monolithic or legacy architecture, you may first need to address system scalability before you can use AI and ML to better leverage data. Our team of experts have deep experience helping companies improve their architecture scalability. Schedule a Discovery Call

How AI and ML Keep You Competitive

By adopting data science, not just a machine learning model, AI and ML open the door to long-term competitive advantage. These are the top factors our experts cite:

Enhanced Decision Making

AI and ML algorithms can analyze big data far more efficiently than humans, providing insights and patterns that might not be immediately apparent. With an ML-enabled, data-driven decision making process, you can respond more effectively to market changes and customer needs.

Automation and Efficiency

AI/ML-driven automation can significantly improve operational efficiency by streamlining processes, reducing manual workload, and minimizing errors. This allows your employees to focus on strategic initiatives and innovation rather than routine tasks.

Personalization and Customer Experience

AI/ML enables highly personalized customer experiences through targeted marketing, personalized content, and customized product recommendations. This personalization leads to higher customer satisfaction, loyalty, and ultimately, increased sales.

Predictive Analytics

ML models can predict trends, customer behavior, and market changes, thus helping you stay one step ahead of competitors.

Innovation and Product Development

AI/ML stimulates innovation by providing new ways to solve complex problems, optimize product design, and speed up research and development.

Risk Management

AI/ML enhances risk assessment and management by identifying potential risks and anomalies in operations, finance, cybersecurity, and other areas.

Cost Reduction

By optimizing operations and increasing efficiency, AI/ML can lead to significant cost savings. These savings can then be invested in other areas of the business, such as research and development or marketing.

Adaptability and Scalability

AI/ML systems can adapt to new data and conditions, enabling businesses to scale up or pivot their strategies quickly in response to market demands or technological changes.

Challenges of Integrating Artificial Intelligence into Scalable Architecture

Because AI is so complex, AI models are expensive to build and operate. The longer the sequence gets, the more computing power is required to train and operate the model. Costs can therefore grow exponentially.

Aside from the massive investment, the biggest concern with AI is information security. Once you provide a prompt or question, that information leaves your environment and moves to a public model. If sensitive information is accidentally shared, you may face an information leak and privacy/confidentiality breach. Whether interacting via chat or connecting to public models with an API, there is currently no perfect private/confidential solution. The implementation of these models have inherent security concerns.

Amazon Bedrock was created to help solve this problem. It is a fully managed service that allows companies to build generative AI applications without the information security challenge of leakage. However, this is not a plug-and-play service. It is critical to understand how to architect and integrate Amazon Bedrock before getting started.

What is MLOps?

Machine Learning Operations, or MLOps, is an emerging discipline at the intersection of Machine Learning, DevOps, and Data Engineering. It brings automation into the testing-training-deployment process for a more streamlined and efficient workflow.

MLOps engineers are subject matter experts in this discipline; they are not data scientists or development engineers. These engineers build the models and continually train, test, retrain, and change models before deploying them into production.

Once a model is deployed, the MLOps team monitors the model and its performance, including how much compute resources are being used, whether the environment is large enough, if model governance is being followed, and if there is bias or drift in data and/or accuracy.

Challenges of Integrating Machine Learning into Scalable Architecture

Companies that are experienced with ML already have a data scientist in-house who designs and develops machine learning algorithms. Unless your data scientist is also a software engineer, there are likely gaps when it comes to MLOps, DevOps, and software engineering. For example, your data scientist may not write efficient code/SQL queries, build queries that are repeatable, or document their work.

For full integration into your scalable architecture, you need:

  • Code that is optimized for efficient queries
  • Feature engineering to ensure your model is repeatable
  • Pipelines for data collection and processing
  • Clean, relevant sets of data to support deep learning
  • Model governance 

An experienced ML consulting partner can help you close the gaps.

What is feature engineering?

You can’t run ML algorithms on straight data; instead, it has to be converted to “features.” That conversion process is feature engineering.

Success hinges on using clean data and selecting the best features (or attributes) for training; this ensures that the models will be accurate and efficient. As models evolve, new features can be created by combining or transforming the existing ones. 

Three benefits of feature engineering:

  • Offer a better user experience: By analyzing your customers’ needs, you can add new features that increase the product’s value, make the product more intuitive, and improve overall product satisfaction and engagement.
  • Gain a competitive advantage: Feature engineering allows you to create unique features that differentiate your products. And by anticipating trends, you can build features that future-proof your product.
  • Boost revenue: ML can spot relevant patterns and relationships in the data, which can lead to better customer targeting, optimized pricing strategies, and reduced churn, thus resulting in increased revenue.

What is model governance?

If you operate in a regulated industry, like fintech or healthtech, your models must make decisions that are compliant with rules protecting personal identifiable information. 

At the center of model governance is the model artifact, which includes:

  • Files containing model parameters, architecture, or configuration settings used to make predictions. 
  • Documentation that ensures stakeholders understand how to use the model appropriately and interpret its results accurately.
  • External data from libraries, frameworks, or software packages. 
  • Testing artifacts that assess the model’s performance, robustness, and compliance with industry requirements. 
  • Deployment configurations that efficiently and effectively move the model into production.

Essentially, you need to know where the data is coming from and how it’s being used to train the model. During development, you must run processes to show the model matrix isn’t biased or using prohibited data. For example, the model can’t consider gender or race for lending decisions.

We Are a Tech Consulting Company That Solves MLOps Challenges

Since 2018, Ten Mile Square has provided AI consulting and ML consulting services to fintech, healthtech, and other SaaS technology companies, including those who already have experience using AI and ML on legacy SaaS platforms.

Our proven technology assessment process will help identify opportunities and address challenges that support your business operations. The operations experts on our team bring extensive knowledge in system architecture, deployment strategies, scalability options, and the numerous compliance, regulatory, and information security issues financial services and healthcare companies face.

When our teams work together, your ML models will be more than just accurate. They will be seamlessly integrated into your existing business processes and able to support real-world business scenarios.

If your company does not yet have ML experience, hire a data scientist first. As a consulting firm, we will work directly with the data scientist and guide them as they integrate MLOps, DevOps and software engineering into your system architecture. 

Our proven approach:

Define the Problems You’re Trying to Solve

We don’t lead the initial discussion with our AI and ML expertise. Instead, we start by conducting discovery to understand your business, what problems you’re trying to solve, specific use cases for AI and ML, and requirements. The more specific you are with the problem you want to solve, the easier it is for us to advise on the solution and actionable next steps.

Select Relevant AI/ML Models

Choosing the right model is based on several factors, including alignment with your business objectives, available data, technical capabilities, and regulatory requirements. We take these factors (and more) into consideration when proposing the best model for your company.

Integrate Efficient MLOps Practices

Our focus on MLOps involves building scalable systems capable of handling increasing data and operational demands. Your AI or ML model will be:

  • Adaptable to new trends and business needs.
  • Automated to ensure updates, testing, and deployment are faster and more reliable.
  • Capable of maintaining performance and reliability through robust monitoring.

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