Simplify Building and Operationalizing Machine Learning Models with New MLOps Solution

This blog is co-authored by Preetam Kumar.

This is the first in a series of posts that will discuss how organizations can successfully deploy their ML models and put AI into production.

With all the benefits machine learning (ML) can bring, it’s ironic that few organizations implement or make business decisions based on their AI/ML models. Let’s review the basics of ML and walk through how a new solution can help deploy ML models so AI can get the recognition it deserves.

What is Machine Learning and What Does it Do?

ML is a subset of artificial intelligence (AI) in which an algorithm discovers patterns in data that include batch and streaming data. ML models learn from past experiences and make predictions based on their experience. So, for example, retailers can build personalized offers based on customers' historical buying patterns and combine that data with real-time activities. This includes web searches, current geographical locations, activities in the mobile app, etc. From there they can build ML models to predict a customer’s propensity to buy a specific product. This drives data-driven campaigns and increases ROI by providing the right offer to the right customer at the right time.

ML enables organizations to make decisions based on data and insights versus a gut feeling. Moreover, ML models become more intelligent and more accurate with exposure to new data. An example is how Google maps gives faster routes by adding the traffic conditions.

Machine Learning Market Drivers and Trends

With the rapid growth of structured and unstructured data, organizations want to get value out of data to gain a competitive advantage and improve the customer experience. To achieve this goal, ML has become an important method to leverage the potential of data. ML also allows businesses to be more innovative, efficient and sustainable. The success of many productive ML applications in real-world settings falls short of expectations.

According to a survey by NewVantage Partners, only 15% of leading enterprises have deployed AI capabilities into production at any scale¹. Most leading organizations have significant AI investments, but their path to real business benefits is challenging.

The reason many ML projects fail is because many proofs of concept never make it to production. The ML community has always focused on building ML models but not on building production-ready ML products. As a result, they don't provide the necessary coordination of the resulting, often complex ML system components and infrastructure. This includes the roles required to automate and operate an ML system in a real-world setting.

How MLOps Delivers Value to AI/ML Projects

Machine learning operations (MLOps) is the process of streamlining ML models. MLOps focuses on data model deployment, operationalization and execution. This standard set of practices lets you enable the full power of AI at scale. It also enables you to deliver trusted, machine-led decisions in real time. MLOps combines model development and operations technologies. This is essential to high-performing AI solutions.

Many organizations follow the process to build, test and train ML models. But how can you provide continuous feedback? This is especially important once the models are in production. Data scientists can't be responsible for managing an end-to-end ML pipeline. What's needed is a team with the right mix of technical skills to manage the orchestration. This establishes a continuous delivery cycle of models and forms the basis for AI-based systems.

MLOps covers all the key phases of data science:

• Prepare data: This stage focuses on understanding the objectives and requirements of the project and preparing the data needed for the model.
• Build model: This is where data scientists build and assess various models based on a variety of different modeling techniques.
• Deploy and monitor models: This is when the model moves into a state where it can be used within the business process for decision making. The operational aspect also ensures the model is delivering the expected business value and performance.

There are many benefits of implementing MLOps, including the ability to:

• Deliver business value for data science projects
• Improve the efficiency of the data science team
• Allow ML models to run more predictably with better results
• Help enterprises improve revenue and operational efficiency
• Speed up AI/ML initiatives with high performing models
• Advance ML for digital transformation

Simplify MLOps with Informatica ModelServe

ModelServe is Informatica's cloud-native MLOps solution that simplifies ML model deployment. It does this by streamlining and automating the building, deploying and monitoring of machine learning models. This helps enterprises scale their AI adoption in minutes to make trusted decisions in real time. It also combines model development and operations technologies that are essential to high-performing AI solutions.

With ModelServe, data scientists and ML engineers can focus on solving business problems rather than worrying about model provisioning infrastructure. ModelServe operationalizes high-quality and governed AI/ML models built using just about any tool, framework or data science platform, at scale, within minutes instead of days and months. And models can be consumed by practically any application.

Key differentiators of Informatica ModelServe include:

• Simple, easy-to-use, wizard-driven approach for data scientists and ML engineers to deploy and operationalize AI/ML models at scale
• Flexibility for data scientists and ML engineers to build their AI/ML models in just about any framework and consume them in most applications
• Ability to enable data scientists to accelerate AI/ML initiatives with high-quality, trusted and governed data
• Improved productivity of data science teams due to streamlining and automating the process of building, deploying and monitoring machine learning models
• Enhanced model performance with timely delivery of trusted data using integrated DataOps
• Better focus on high-value, innovative tasks for those using ML pipelines by speeding up and simplifying the model lifecycle
• Better collaboration between data scientists, ML engineers, data engineers, and IT operations
• Increased reliability, performance, scalability and security of ML systems
• Improved ROI of AI/ML projects

Features, Stages and Steps in Informatica ModelServe

Below are the steps in operationalizing ML models and descriptions on how it enables data scientists and ML engineers to operationalize ML models:

1. Model Registry: Data scientists can build their models using AI/ML frameworks like Python, TensorFlow, Spark ML, Keras, etc., and register the models.
2. Model Deployment: Once the model is registered, data scientists can deploy the AI/ML models in a serverless environment in minutes (instead of days or weeks) without worrying about model provisioning infrastructure.
3. Model Monitoring: With Informatica ModelServe, you can monitor the performance of the deployed model in a single pane of glass, detect anomalies and take remedial action like retraining the model. Post monitoring, the model can be consumed in practically any application.

Next Steps

Interested in learning how Informatica ModelServe can help you build and operationalize ML models? Complete this form and we will follow-up with you.
 

_{¹https://www.forbes.com/sites/gilpress/2020/01/13/ai-stats-news-only-146-of-firms-have-deployed-ai-capabilities-in-production/?sh=736e5b6e2650}