What are the best ways to speed up your Machine Learning deployment?

Imagine you're a data scientist at an e-commerce company, using TensorFlow Serving to develop a product recommendation model. TensorFlow Serving offers utilities for data preparation and model training. It has built-in testing tools to validate your model's performance, then helps with packaging and versioning for easy management. It enables smooth deployment of new models without disruption. Thus, TensorFlow Serving streamlines your machine learning pipeline, reduces errors, and speeds up deployment.

The article emphasizes speeding up machine learning deployment for production use. It recommends using standardized frameworks like TensorFlow Serving, MLflow, Kubeflow, and Seldon Core to automate workflows, ensuring consistency and integration. To enhance efficiency, optimize models, leverage hardware acceleration, utilize cloud-based services, implement CI/CD pipelines, and monitor performance. A/B testing, data pipeline optimization, and model caching are also suggested for improved deployment speed and effectiveness.

The article emphasizes speeding up machine learning deployment for production use. It recommends using standardized frameworks like TensorFlow Serving, MLflow, Kubeflow, and Seldon Core to automate workflows, ensuring consistency and integration. To enhance efficiency, optimize models, leverage hardware acceleration, utilize cloud-based services, implement CI/CD pipelines, and monitor performance. A/B testing, data pipeline optimization, and model caching are also suggested for improved deployment speed and effectiveness.

In majority of the startups, AWS is a preferred cloud platform and AWS Sagemaker has been a workhorse to automate / standardize machine learning deployment However, cloud based deployment of machine learning models also come with a significant cost. Structured usage, guidelines have to be in place with the help of DevOps / MLOps team

Consider the case of PayPal, a large online payment company. They use machine learning models for real-time fraud detection. Due to the critical nature of this task, they chose on-premise servers for deployment to ensure lower latency, thus making decisions more quickly. On the other hand, a firm like Tesla uses edge devices in its vehicles to run machine learning models for features like Autopilot. This allows models to function independently, crucial for real-time decision-making even when network connectivity is weak or non-existent.

Choose the right deployment platform: The choice of deployment platform impacts speed. Cloud services offer rapid, scalable deployment with convenience. Edge devices allow low-latency or offline scenarios. Hybrid solutions balance cloud and edge benefits for optimal performance and cost efficiency. Consider specific use case, budget, and requirements to make an informed decision.

Some non technical aspects around this is that: 1. The person building the model should well understand the business use case and it's varying patterns. Understanding business requirements helps you answer the above with ease and confidence. Given the excitement about launching models, teams and individuals tend to productionalize everything that responds with low latency which necessarily need not be the business case 2. It is not imperative on the data scientist to carry out this solely. Significant help from DevOps / MLOps should aalo be considered

Consider Google's BERT, a state-of-the-art NLP model. It's powerful but also resource-intensive, making deployment challenging. To tackle this, Google developed a technique called "DistilBERT." DistilBERT is a smaller version of BERT that has been trained to mimic BERT's behaviour (this process is called distillation). Even though it's about half the size and faster, DistilBERT manages to retain 95% of BERT's performance, making it significantly more efficient for deployment. Snapchat, on the other hand, uses model pruning and quantization for its facial recognition models. This allows the app to run these models directly on users' phones (edge devices), delivering real-time filters and effects without excessive battery drain or latency.

Different technique for different model. Like for GenAI Lambda AWS is best for deployment. because you do not face the issue of speed on multiple request when you are using large model

Twitter uses machine learning to curate content. It monitors model performance with logging and dashboards, allowing quick insights into model health. If an anomaly occurs, an alert system notifies the team. Furthermore, Twitter regularly updates its models by retraining on new data, adapting to user behavior changes. This process ensures the models stay relevant and accurate, improving user experience.

One common metric to track is the drift in the underlying data. Methods to track drift are Population Stability Index (PSI), KS Test etc., These can be configured as a part of the ML pipeline to provide alerts. Drift carries significance in real world scenarios as there can be significant market shifts, behavioral changes of the customer, change in the target segment etc., In short & in practical sense we live in a world of constant change say starting from customer to regulatory to market Retraining of the models basis drift and determining the frequency of retraining is also important.

Consider Spotify's Discover Weekly feature, which uses ML models to recommend songs based on user habits. Maintaining and updating these models is crucial to stay relevant with new songs and changing tastes. In agriculture, ML models predicting crop yields need regular updates to account for changing weather or farming practices, ensuring accuracy. Both examples highlight the importance of model upkeep in diverse real-world applications.

For LLM's deployments,Lambda AWS is best way to deploy model that save you from a lot of issue like speed, delay in response.