Production ML 101 - MLOps/LLMOps

Are you aware that a staggering 87% of machine learning models fail to deliver results in production in 2019? In today's age of massive investments in AI/ML projects, this is an alarming statistic for businesses. However, the solution is simple - understanding machine learning engineering and MLOps.

By equipping yourself with the knowledge to deploy and manage machine learning models in production, you can position yourself for success in the AI/ML space. Join our course "Machine Learning Operations 101" to gain the skills and insights needed to take your AI/ML projects to the next level!

In our "Machine Learning Operations 101" course, we'll teach you how to address these root causes and unlock the full potential of your machine learning models.

In this course, you'll learn:

1. Fundamentals of Production ML: Learn the core principles of machine learning in production such as DevOps vs MLOps vs LLMOps, ML in Production vs ML in Research, Technical Debt In Machine Learning Systems

2. Current Tooling Ecosystem: MLOps tools

3. MLOps as a Process: Learn how to operationalize your machine learning models using the MLOps framework

4. Evaluation of Tools: Understand how to evaluate and choose the right tools for your machine learning operations, ensuring optimal results and ROI for your business.

5. Future Roadmap in Production ML: Gain insight into the different job roles in the MLOps process. Als understand the different job roles and future roadmap and relevant resources for each job role.

Are you struggling to achieve success with your machine learning models in production? You're not alone! Many businesses fail to realize the full potential of their models due to a variety of root causes:

1. Underestimating the importance of deployment: Don't let your investment in machine learning go to waste! By understanding the importance of proper deployment, you can ensure your models work seamlessly in your existing systems.

2. Need for software engineering principles: It's not just about the models - it's about the systems they run on! Ensure your models are scalable, reliable, and maintainable with proper software engineering principles.

3. Dynamic data is key: Don't rely solely on static data. To achieve optimal results, your models need to be trained on dynamic, real-world data.

4. Communication is key: Collaboration between data science and IT teams is critical for the successful deployment and maintenance of machine learning models.

5. Mastering MLOps: MLOps is a framework that operationalizes machine learning models in production, allowing you to achieve optimal results with minimal effort.

ML Code is 5-10% while ML system is 90-95%

There is a lot of difference in the mindset of ML in Research vs ML in Production

Requirements:

• ML Research: Focuses on achieving state-of-the-art (STOA) models, often with a singular goal of optimizing performance on a given model metric.

• ML Production: This must consider the requirements of different stakeholders who may have varied needs, such as product, business, engineering, data science teams

Computational Priority:

• ML Research: Prioritizes fast training and high throughput, as speed is critical to running multiple experiments and training large models.

• ML Production: Prioritizes fast inference and low latency, as models need to process data in real time and at scale.

Data:

• ML Research: Typically works with static datasets that are already collected and labelled for specific tasks.

• ML Production: Works with constantly shifting data that require constant retraining and adaptation of models to maintain accuracy and relevance.

Fairness:

• ML Research: Fairness is not always a primary concern, and models may perpetuate bias if not intentionally accounted for during training.

• ML Production: Must consider fairness and bias to ensure that models do not discriminate against certain groups and produce equitable outcomes.

Interpretability:

• ML Research: Interpretability is often not in focus, as the goal is to optimize performance rather than understand how the model arrived at a decision.

• ML Production: Must consider interpretability to ensure that models are transparent and explainable to stakeholders, such as regulators, auditors, and end-users.

Overall, ML Research and ML Production have different priorities and considerations, reflecting the different stages of the machine learning lifecycle. Understanding these differences is essential for the successful implementation of machine learning projects in production.

The probabilistic nature of the Machine Learning Models creates problems in putting models in production

LLMOps are the same as MLOps with an underlying model which is different. But it has more ambiguity involved in it.