AI/ML Trends and Challenges
It's been five years since AlphaGo, a computer program that plays the board game Go defeated the world champion using machine learning and artificial intelligence technology. And recently, we heard about AlphaFold generating proteins to solve one of the core challenges in biology. Companies like AlphaGo and AlphaFold are paving the path for the future of machine learning and artificial intelligence. But, despite increasing interest in adopting artificial intelligence and machine learning, 85% of AI projects fail to deliver on their intended promises to business. There are many gaps in the machine learning process and filling in those gaps is critical to the success of any data science project. Let's discuss some of the latest AI/ML and data science challenges affecting the implementation of AI/ML projects.
The first challenge is access to trusted and governed data for data scientists, data stewards, data analysts, data engineers, developers, and business users. Companies rely heavily on high-quality, trusted data to create trustworthy insights to make critical business decisions. Or else it will become a "garbage in, garbage out" situation. To achieve this goal, most companies opt for a cloud-native AI-powered data management platform that can democratize access to trusted data in a governed manner.
The second challenge is adopting a multi-cloud strategy for machine learning. Customers are modernizing their legacy systems in public cloud infrastructure like – AWS, Azure, and Google Cloud. Cloud gives the organization access to pre-trained models and helps them innovate, which is not possible in an on-premises environment. For example, the availability of the latest machine learning libraries and specialized hardware. But it is hard to stick to a single cloud, and more than 76% of organizations use multi-cloud. Companies often build different machine learning applications in multiple clouds. For example, customers can build an app that uses GCP’s vision APIs on Google Cloud, and the rest of their apps can run on AWS or Azure.
The third challenge is the automation of data science and machine learning to accelerate and operationalize the machine learning models. Data scientists can focus on high-value work by automating their mundane work. In addition, automation gives data scientists more bandwidth to do what they are trained to do. The most important trend is automating the entire machine learning workflow, including data acquisition, data exploration, data integration, model training, model deployment, and monitoring. As a result, you can reuse, configure and deploy repeatable patterns rapidly.
MLOps is the process of streamlining deployment, operationalization, and execution of machine learning models. It is a standard set of practices for Machine Learning Operations at scale to fully actualize the power of AI and deliver trusted, machine-led decisions in real-time. It emulates the concept of DevOps in merging machine learning and operationalization. MLOps aims to combine the model development and model operations technologies essential to high-performing AI solutions.
Many organizations follow the process of build, test, and train ML models for their data science practice. But the real challenge is providing continuous feedback once the models are in production. In addition, data scientists can't be responsible for the management of an end-to-end machine learning pipeline. It would be best to have a team with the right mix of technical skillsets that manage the orchestration. MLOps provides the framework to operationalize the ML model development process to establish a continuous delivery cycle of models that form the basis for AI-based systems.
The benefits of implementing MLOps:
MLOps can solve business issues by addressing many industry use cases. For example:
Banking and Finance: Fraud detection and prevention, customer onboarding, customer experience, portfolio management, assessment and management of credit risks, customer churn predictions, Blockchain, algorithmic stock trading, credit scoring, and loan processing.
Retail: Sales, product usage and retention forecasting, customer lifetime value, upsell and cross-sell, audience segmentation, weather forecasting, inventory management, and next-best action.
Healthcare: Preventive patient care, drug discovery, ICU monitoring and cancer diagnosis.
Manufacturing: Predictive maintenance, improve product design, smart energy consumption, supply chain management, and quality control.
Informatica can help accelerate your data science initiatives to build next-gen analytics and AI/ML platform. Informatica has purpose-built connectors for thousands of endpoints to provide native connectivity for both metadata and data for varied use cases and latency/ SLA requirements. Informatica also provides a best-in-class ETL and ELT engine to process data in the most efficient way as per the use case. Let's see how you can use Informatica's AI-powered data management to onboard new data sources into a cloud data lake and automate machine learning models' operationalization.
Step 1: Identify the business problem and acquire data. In this step, you will identify trusted data from various sources, like IoT devices, machine logs, relational databases, mainframe systems, on-premises data warehouses, and applications and load them into a cloud data lake. For example, you could use Informatica's Enterprise Data Catalog to identify the trusted data and use Cloud Mass Ingestion to ingest it into the cloud data lake. Informatica's unique AI-driven intelligent metadata discovery solution allows data engineers to quickly discover data assets and apply them to a data pipeline. For example, a data engineer can search for inventory data and add it to the mapping. Informatica Cloud Mass Ingestion helps you mass ingest continuous data from files, messaging streams, databases CDC, applications into cloud targets in a simple and intuitive 4-step wizard-based approach. Cloud Mass Ingestion also optionally provides transformation capabilities during ingestion to avoid unnecessary hops by saving cost.
Step 2: Curating, cleansing, and preparing the data. Once the data is ingested into a cloud data lake, you need to cleanse and standardize rules that ensure your data is clean and ready to consume. Informatica Cloud Data Quality provides easy-to-use drag and configuration capabilities. These let data scientists and data consumers rapidly build, test, and run data quality plans. As a result, they can analyze, cleanse, standardize, and match data and monitor data quality on an ongoing basis to ensure the correct data is used for their machine learning model. In addition, by leveraging Informatica's Cloud Data Integration's out-of-the-box pre-built data transformation templates, data engineers spend less time implementing manually coded and error-prone logic. You can also use advanced transformations like – hierarchy transformation, built-in integration for data quality, machine learning transactions for operationalizing ML models, and data masking transformation.
Step 3: Build machine learning models. In this stage, data scientists can build and test the machine learning model using their favorite development tools like the "Jupyter" notebook. And then run the model using Informatica's Spark-based data integration engine on Advanced Serverless deployment to provide a pipeline for cleansed training data for model development. Informatica provides the industry's first data management solution to run on advance serverless deployment. CLAIRE automatically provisions a Spark serverless cluster for auto-scaling and auto-tuning to run jobs at scale for better performance and with effective cost management when the job is processed. In addition, Informatica has customized and incorporated layers of innovations such as run time optimizations, advanced data management, elastic operations, and more.
Step 4: Deploying machine learning models. In this stage, data engineers can consume and deploy the machine learning model into the Informatica production environment running in Serverless mode for predictive analytics and sending recommendations like - custom SMS alerts, next best action, etc. Using Informatica, data engineers can reuse the training data pipeline for processing data for inference. In addition, the Serverless deployment frees data science and engineering teams from managing infrastructure so that these teams can focus on the model efficiency.
Step 5: Model monitoring. In this stage, DataOps teams can monitor the model's performance and ensure it continues delivering value. In addition, they can leverage Informatica's built-in monitoring, alerting capabilities to automate monitoring and management of your models.
MLOps capabilities are essential to operationalize data science use cases to drive business value and accelerate digital transformation. Informatica is the only cloud-native data management vendor that provides end-to-end MLOps capabilities on any platform, any cloud, multi-cloud, and multi-hybrid.
To learn more, watch this new demo video on Operationalize your machine learning models at scale with Informatica CDI-E.