What is Machine Learning (ML)? What does it do?
Machine learning (ML) is a subset of artificial intelligence (AI) that uses algorithms to discover common patterns in data and optimize the final results based on continuous training. ML models learn from past experience and make predictions based on existing experience. For example, today's e-commerce companies no longer use general price cuts or coupons to attract customers. Instead, they build personalized offers based on each customer's historical purchasing patterns, and combine these data with customer PII information, web searches, current Combined with real-time information such as geolocation, activity in mobile applications and more. In this way, ML models can be built to predict a customer's propensity to purchase a specific product. All marketing activities start to be driven by data and models, and by providing the right products and offers to the right customers at the right time, they can increase transaction volume and profit margins to achieve a higher return on investment.
ML enables businesses to make decisions based on data and models rather than experience or intuition. At the same time, with the continuous supply and training of massive new data, ML models will become more intelligent and accurate. For example, this is how LLM such as the now very popular ChatGPT was born.
How MLOps provide value to AI/ML projects
With the rapid growth of structured and unstructured data, all types of enterprises hope to obtain value from data to gain competitive advantages and improve service capabilities. But the reality is that many production ML applications do not live up to expectations in real-world settings. This is because any technology requires high-quality development, implementation, and maintenance. If the focus has been on building ML models rather than building production-ready ML products, complex ML system components and infrastructure will suffer from the lack of necessary coordination and Updates may result in reduced performance or even failed predictions. To be more precise, good ML requires good MLOps pipelines and practices. MLOps focuses on data model deployment, operationalization and execution. Through this set of standard practices, trusted decisions can be provided in real time. MLOps combines model development and operational techniques, which are critical for high-performance ML solutions.
MLOps covers all key stages of data science:
- Data preparation: This phase focuses on understanding the goals and requirements of the project and preparing the data required for the model.
- Model Building: Data scientists build and evaluate various models based on a variety of different modeling techniques.
- Deploying and monitoring the model: This is when the model gets into a state where it can be used for decision-making in business processes. Ops (operations) is the key to ensuring that the model provides expected business value and performance.
How to simplify MLOps with the open-source WhaleStudio
WhaleStudio is Beluga's open source DataOps solution. By using WhaleStudio, enterprises can simplify the deployment of ML models, and greatly improve the operational efficiency of MLOps through WhaleStudio's powerful data preparation capabilities and scheduling monitoring capabilities:
- Comprehensive data integration and data preparation capabilities: quickly connect all types of real-time or batch data, and improve data accuracy and usability through built-in data lineage and data quality tools
- Support the ability to schedule and execute ML tasks: support the execution of user training tasks using various frameworks
- Support the ability to schedule and execute mainstream MLOps projects: provide out-of-box mainstream MLOps projects to allow users to use the corresponding capabilities more conveniently
- Supports the ability to orchestrate each module to build a machine learning platform: According to the adaptability of MLOps project characteristics and business, the capabilities of different projects can be used in different modules.
With WhaleStudio, data scientists and ML engineers can focus on solving business problems rather than worrying about data acquisition and data preparation. At the same time, WhaleStudio can use any tool at scale in minutes instead of days and months. Frameworks (such as TensorFlow, MLFlow, etc.) build high-quality AI/ML models, and use powerful Ops capabilities to schedule, monitor, continuously deploy, and continuously launch model training.
To sum up, Beluga open source WhaleStudio can help enterprises quickly realize data value in MLOps projects:
- Data scientists and ML engineers have the flexibility to build their AI/ML models in any framework
- Enables data scientists to accelerate AI/ML training with high-quality, trusted and timely data
- Deliver trusted data in a timely manner and enhance ML model performance using integrated DataOps
- Allow users to better focus on high-value innovation tasks by accelerating and simplifying the model lifecycle
- Improve performance, reliability and scalability of ML systems
- Better collaboration between data scientists, ML engineers, data engineers, and IT operations
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