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Understanding Model Deployment
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Let's discuss model deployment. Can anyone tell me what deployment means in the context of machine learning?
Isn't it about getting the model ready to make predictions in real-world scenarios?
Exactly! Deployment is the process of integrating a model into a production environment where it can make predictions on live data. It's not just about the model but also packaging, exposing via APIs, and monitoring its performance. Remember, I like to use the mnemonic **'PEM'** - Package, Expose, Monitor!
What are some deployment scenarios?
Great question! There are several deployment scenarios: batch inference, where predictions are made on large data sets at intervals; online inference for real-time predictions; and edge deployment on devices with limited computing power. Each has its own use cases. Does that make sense?
Yeah! But how do you choose which scenario to use?
It depends on your application needs, the volume of data, and the infrastructure you have. Always align your deployment strategy with your business objectives.
Can you summarize what we just learned?
Of course! Model deployment is about integrating ML models into production, encompassing packaging, API exposure, and performance monitoring. Remember PEM - Package, Expose, Monitor, and identify the right deployment scenario for your needs.
Infrastructure and Tools for Deployment
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Now, letβs talk about the infrastructure needed for deployment. Who can name some model serialization formats?
I know about Pickle and Joblib!
Correct! Pickle is Python-specific, while Joblib is optimized for NumPy arrays. Itβs vital to choose a serialization format that fits your framework, such as ONNX for compatibility across frameworks. Remember the acronym **'POS'** - Pickle, ONNX, SavedModel!
What about serving frameworks?
Good point! Serving frameworks are essential for deploying your models. TensorFlow Serving and TorchServe are popular for TensorFlow and PyTorch models, respectively. Flask and FastAPI can wrap any model as well. Who knows why we might choose these options?
I think itβs about ease of integration and management!
Exactly! Each framework has its strengths. Now, think about containerization. What advantages does Docker provide?
It helps in packaging the model with dependencies.
Right! Docker creates isolated environments, making deployments consistent. For orchestration, Kubernetes is widely used to manage these containers. And don't forget about serverless options like AWS Lambda for scalable solutions!
Can you summarize the key points from this session?
Absolutely! Key tools for deployment include various serialization formats like Pickle and ONNX, serving frameworks such as TensorFlow Serving and Flask, and containerization with Docker followed by orchestration using Kubernetes. Remember to consider your model's requirements while choosing the right tools.
Monitoring Models in Production
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Next, we need to discuss why monitoring is crucial after deployment. Student_2, could you share your thoughts?
I think itβs to check if the model is still performing well over time.
Exactly! Models can degrade due to data drift, concept drift, or even model staleness. Now, what metrics do we need to monitor?
Things like accuracy, precision, recall, and even data distributions!
Great answers. Monitoring input data, predictions, performance metrics, and latency is essential for ensuring the modelβs health. Remember the acronym **'5 P's'**: Predictions, Performance, Pattern, Parameters, and Processing speed!
What tools can help with monitoring?
Some popular ones are Prometheus and Grafana for metrics and alerts. Tools like Evidently AI specifically monitor data drift. MLflow also provides tracking capabilities. It's crucial to set up alerts for performance degradation!
Can you recap what we learned about monitoring?
Absolutely! Monitoring is crucial for assessing model performance and detecting issues like data drift. Key metrics include predictions, performance metrics, and processing speed. Utilize tools like Prometheus and MLflow for effective monitoring. Don't forget the **'5 P's'** to remember what aspects to track!
Model Retraining and Feedback Loops
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Letβs dive into model retraining. Why do we need to retrain our models periodically, Student_3?
So that they stay accurate and relevant with new data.
Exactly! Retraining is essential for maintaining performance. When should we consider triggering a retrain?
When there are signs of performance degradation or after a set time interval.
Correct! Automated retraining pipelines can help streamline this process. Itβs also important to incorporate user feedback. What do you think active learning means in this context?
It's when the model asks for labels on uncertain predictions?
Precisely! And human-in-the-loop approaches can generate valuable insights from domain experts. This feedback improves future versions of the model. Can anyone summarize what we learned today about retraining?
We learned retraining maintains model accuracy and should be triggered by performance drops or regularly, and feedback is important for improvements.
Spot on! Keep in mind the need for retraining, using automated pipelines and incorporating feedback to enhance models continually. Excellent discussion today!
Introduction & Overview
Read a summary of the section's main ideas. Choose from Basic, Medium, or Detailed.
Quick Overview
Standard
The section outlines the steps involved in deploying machine learning models into production environments, focusing on the architecture, tools, and techniques necessary for effective monitoring and maintenance of models over time.
Detailed
Deployment and Monitoring of Machine Learning Models
Deployment of machine learning models is critical to transforming them from development to practical usage. This section delves into the various stages of deployment, including model packaging and exposing it through APIs, followed by continuous monitoring. It highlights the importance of tracking various performance metrics, detecting data and concept drift, and employing robust infrastructure to support these processes. Effective deployment ensures that models provide accurate predictions, while ongoing monitoring maintains model health against evolving data patterns, thereby maximizing the value of machine learning solutions. Additionally, it emphasizes the necessity of model retraining based on feedback loops and performance evaluations to adapt to changes in data over time.
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Introduction to Deployment and Monitoring
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Building a robust machine learning model is only half the battle. To generate real-world value, models must be deployed into production environments where they can serve predictions to users or other systems. However, the journey doesn't end at deployment. Continuous monitoring, performance evaluation, and maintenance are critical to ensure the model remains reliable, accurate, and aligned with evolving data patterns.
Detailed Explanation
In this introduction, we learn that simply creating a machine learning model isn't enough. The model needs to be put into use in a real-world environment, where it can make predictions based on live data. Deployment is the process of integrating the model into this setting. But even after deployment, it's crucial to keep an eye on the model's performance. This involves regularly checking if the model is performing well, updating it as necessary, and ensuring it continues to respond accurately to changes in the data it processes.
Examples & Analogies
Think of machine learning deployment like baking a cake (the model). Baking the cake successfully (building the model) is just the beginning; you also need to decorate it (deployment) and keep it fresh and appealing (monitoring). Just like a cake can go stale or lose its flavor over time, models need regular checkups to stay relevant and accurate within a changing environment.
Model Deployment Defined
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Model deployment is the process of integrating a machine learning model into an existing production environment where it can make predictions on live data. It typically involves:
- Packaging the model and its dependencies
- Exposing it via an API or application
- Monitoring its performance over time
Detailed Explanation
Model deployment means taking a machine learning model and ensuring it can make predictions based on current data in a live environment. This process has three main steps: first, you package the model and all the tools it needs to work correctly. Next, you provide a way for users or other systems to access the model's predictionsβoften through an API, which acts like a waiter that takes orders and delivers food (the predictions). Finally, once the model is live, it's essential to monitor how well it's performing over time to catch any issues early.
Examples & Analogies
Imagine a restaurant using a new recipe (the model), which needs to be included in their menu (deployment). The chefs (developers) need to package the recipe (mean preparing all ingredients) and use it in the kitchen (production environment). Customers order food (API requests), and the restaurant needs to watch how popular the dish becomes and if the customers enjoy it (monitor performance).
Types of Deployment Scenarios
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- Batch inference: Predictions are made on a large dataset at regular intervals.
- Online inference: Predictions are made in real time as new data arrives.
- Edge deployment: Models run on devices (e.g., mobile phones, IoT) with limited computing power.
Detailed Explanation
There are different methods for deploying machine learning models, classified based on how and when predictions are made. In batch inference, predictions are generated for a group of data points at set times, similar to checking everyone's grades at the end of a semester. Online inference means the model provides immediate answers to new dataβlike a restaurant staff quickly noting down orders. Edge deployment focuses on running models on local devices with limited resources, such as smartphones or IoT devices, ensuring that predictions can still be made without needing constant internet access.
Examples & Analogies
Think of batch inference like a school that evaluates student grades all at once at the end of the week. Online inference is like a cashier at a store who immediately gives you your receipt after your purchase. Edge deployment is like having a calculator on your phone that can perform calculations without needing to connect to the internet.
Infrastructure and Tools for Deployment
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Model Serialization Formats
- Pickle: Python-specific, not secure for untrusted input
- Joblib: Efficient for NumPy arrays
- ONNX: Open Neural Network Exchange, supports multiple frameworks
- SavedModel (TensorFlow) and TorchScript (PyTorch): Framework-specific formats
Detailed Explanation
To deploy a machine learning model, it needs to be saved in a format that preserves its structure and capabilities. Model serialization formats are used for this purpose. For instance, 'Pickle' is a common format for Python but it's not the safest for models used with uncertain data. 'Joblib' works well for models that depend heavily on NumPy arrays. 'ONNX' is a versatile format allowing different frameworks to communicate. Finally, 'SavedModel' and 'TorchScript' are tailored formats for TensorFlow and PyTorch, respectively, ensuring that these frameworks can read and execute the models effectively.
Examples & Analogies
Think of model serialization formats like different languages used for translating a book (the model). Just as some translations are better suited for certain styles or audiences, some formats are better for specific machine learning frameworks. Using the right format ensures the book can be read correctly by people who speak different languages.
Serving Frameworks
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- TensorFlow Serving: For TensorFlow models with REST/gRPC APIs
- TorchServe: For PyTorch models
- Flask/FastAPI: Lightweight Python web frameworks to wrap any model
- MLflow: Offers model registry, tracking, and deployment tools
Detailed Explanation
Serving frameworks are tools that help expose machine learning models so that they can be accessed and used easily. For TensorFlow models, TensorFlow Serving specifically provides built-in ways to handle requests via APIs. TorchServe does the same for PyTorch models. If you're using various types of models, lightweight frameworks like Flask and FastAPI can be created to wrap your model in an easy-to-use interface. MLflow adds extra features like model tracking and management to help oversee multiple models at once.
Examples & Analogies
Using a serving framework is like hiring a waiter at a restaurant (the framework) who takes your orders (the API) for different dishes (the models). The waiter knows how to serve each dish and can accommodate specific requests, just as these frameworks can handle different models effectively.
Containers and Orchestration
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- Docker: Package model, code, and dependencies into isolated containers
- Kubernetes: Manage and scale containers in production
- Kubeflow: Kubernetes-native ML platform for end-to-end workflows
Detailed Explanation
Containers are a way to bundle everything necessary for a modelβits code and the environment it runs inβinto a single package that can be easily moved between systems. Docker is the tool used to create these containers. Kubernetes then manages these containers, helping them scale up or down based on demand. Kubeflow provides a specialized platform designed specifically for machine learning workflows, building on the capabilities of Kubernetes to streamline the entire process from deployment to scaling.
Examples & Analogies
Containers are like shipping containers that hold all the parts needed for a product. You can easily load and unload these containers onto trucks (Docker). When demand for the product increases or decreases, the warehouse manager adjusts the number of containers being shipped (Kubernetes). Kubeflow is like a logistics manager who specializes in ensuring the right product gets to the right place efficiently.
Serverless Deployments
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- AWS Lambda, Google Cloud Functions, Azure Functions: Auto-scaled and cost-efficient, but with limits on execution time and memory
Detailed Explanation
Serverless deployments allow developers to run model predictions without worrying about the underlying infrastructure. Services like AWS Lambda, Google Cloud Functions, and Azure Functions automatically manage the scaling of resources needed for the model, helping control costs. However, each of these services has limitations, such as the maximum execution time and memory capacity, which can restrict the complexity of the models that can be deployed.
Examples & Analogies
Using serverless functions is like ordering takeout from a restaurant that handles all the food preparation for you without needing to worry about how the kitchen operates. You just place your order (invoke the function) and get your meal delivered (execute prediction) without having to maintain any equipment yourself.
Continuous Integration and Deployment for ML (MLOps)
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CI/CD automates building, testing, and deploying models, ensuring consistency and reliability.
- CI (Continuous Integration): Code is automatically tested and validated
- CD (Continuous Deployment): Validated code is deployed to production
- Popular Tools: Jenkins, GitHub Actions, GitLab CI, CircleCI
Detailed Explanation
Continuous Integration and Continuous Deployment (CI/CD) are practices that help developers manage the lifecycle of machine learning models more efficiently. CI focuses on automatically testing and approving code changes to ensure they don't break existing functionality, allowing for a smoother integration of new features. CD takes this a step further by automatically deploying the validated code into a real-world environment once it's approved. Tools like Jenkins, GitHub Actions, and CircleCI support these practices, streamlining the process for data scientists and developers.
Examples & Analogies
Think of CI/CD like a car assembly line. Continuous Integration is the step where each car component (code) is tested before being installed to make sure everything works together. Continuous Deployment is when the finished car is delivered to customers without unnecessary delays. Just like a well-timed assembly line requires careful coordination, CI/CD ensures smooth and consistent model updates and deployments.
Model Registry
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A centralized store for managing:
- Model versions
- Metadata (accuracy, data used, hyperparameters)
- Staging vs production environment transitions
Examples: MLflow Model Registry, SageMaker Model Registry
Detailed Explanation
A model registry is a centralized location where data scientists can manage the different versions of their models and track important information about each version, such as accuracy and the data used for its training. It also helps manage which versions of a model are in development (staging) versus those that are fully deployed (production). Tools like MLflow and SageMaker provide features to streamline this process, ensuring that teams can easily access the right model at any point in time.
Examples & Analogies
Imagine a library that keeps track of all the books (models) and their editions (versions). Each book has a record (metadata) indicating how well it sold (accuracy) and the topics it covered (data used). This organization allows readers (data scientists) to quickly find the book they need at any time without confusion.
Importance of Monitoring Models
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Once deployed, models can degrade due to:
- Data drift: Distribution of incoming data changes over time
- Concept drift: Relationship between features and labels changes
- Model staleness: Model trained on outdated data
Detailed Explanation
Monitoring is vital for deployed models because they can lose effectiveness over time due to several factors. Data drift occurs when the incoming data begins to differ from the data the model was trained on, leading to inaccurate predictions. Concept drift happens when the relationships between the input features and the outcomes change, which can render a model's predictions unreliable. Finally, model staleness refers to the problem of using an outdated model that hasn't kept up with new data trends, making it important to ensure regular updates.
Examples & Analogies
Monitoring models is like a health checkup for patients. If a person's condition changes (data drift), their treatment might need adjustment (retraining the model). Similarly, if the symptoms they showed (concept drift) start to represent a different illness, the doctor must reassess their prescription. Staleness is like using an old vaccine that's no longer effective; the medical team must stay informed to ensure treatment is relevant and up-to-date.
What to Monitor in Models
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- Input Data: Track feature distributions and missing values
- Predictions: Distribution, confidence, outliers
- Performance Metrics: Accuracy, precision, recall, RMSE, etc.
- Latency and Throughput: Time per prediction, requests per second
- Model Usage: Number of predictions made, error rates
Detailed Explanation
Monitoring a deployed model involves looking at several aspects to ensure it operates effectively. First, input data needs to be tracked for changes in its distributions and any missing values that might cause issues. Predictions should be monitored for their overall distribution, confidence levels, and any outliers that might indicate problems. Performance metrics, such as accuracy and recall, provide quantitative measures of how well the model is doing. Additionally, latency (how long it takes to generate a prediction) and throughput (how many predictions can be made in a second) are essential for understanding the model's efficiency. Finally, keeping track of model usage helps identify patterns and spot potential issues in error rates.
Examples & Analogies
Monitoring a model is similar to running a quality control department in a factory. They check raw materials (input data) for defects, ensure that products meet safety standards (predictions), and track whether the machinery is running smoothly (performance metrics). By analyzing production rate (throughput) and speed (latency), they optimize efficiency and address any problems promptly.
Tools for Monitoring Models
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- Prometheus + Grafana: For system metrics and alerts
- Evidently AI: Monitors data drift and model performance
- Fiddler AI, WhyLabs, Arize AI: Commercial platforms for ML monitoring
- MLflow Tracking: Logs parameters, metrics, and artifacts
Detailed Explanation
Various tools are available to assist in monitoring machine learning models effectively. Prometheus and Grafana are often used together to capture and visualize system metrics and create alerts for any significant changes in performance. Evidently AI specifically focuses on monitoring data drift and model performance over time. For companies looking for commercial solutions, platforms like Fiddler AI, WhyLabs, and Arize AI provide robust monitoring capabilities tailored for machine learning applications. Finally, MLflow Tracking is valuable for logging important model parameters, numerous performance metrics, and artifacts useful for audit trails.
Examples & Analogies
Using monitoring tools is like equipping a car with a dashboard that shows critical information such as speed, fuel level, and engine temperature. Prometheus and Grafana allow drivers (data scientists) to see performance at a glance, while specialized tools like Evidently AI offer alerts when something is off, similar to warning lights indicating potential engine problems.
Model Retraining and Feedback Loops
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Model Lifecycle Management
- Triggering retraining: Based on performance degradation or time intervals
- Automated retraining pipelines: Combine data ingestion, model retraining, evaluation, and redeployment
- A/B testing: Compare performance of old vs new models before full rollout
Detailed Explanation
Managing the lifecycle of a model involves determining when it needs retrainingβthis could occur when its performance drops or at designated timeframes. To facilitate this, automated pipelines can be set up that integrate the processes of gathering new data, retraining the model, evaluating its performance, and redeploying it. A/B testing allows teams to compare an updated model to the existing one in real-time, ensuring that pushes to production enhance overall performance before concluding the full transition.
Examples & Analogies
Think of a model like a smart assistant that needs a software update occasionally. When it stops performing tasks correctly (performance degradation), it needs a refresh (retraining). Automated pipelines are like the app store automatically downloading updates, while A/B testing is akin to letting users test a beta version of the assistant and deciding if it works better before making the upgrade permanent.
Incorporating Feedback and Active Learning
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- Active learning: Model requests labels for uncertain predictions
- Human-in-the-loop: Feedback from domain experts improves future versions
Detailed Explanation
Incorporating feedback into machine learning models is vital for their continuous improvement. Active learning involves the model identifying predictions it isnβt confident about and requesting human input for correct labels, enhancing its learning process. The human-in-the-loop approach ensures that domain experts can provide qualitative feedback, which helps improve subsequent versions of the model, refining its accuracy and performance.
Examples & Analogies
Imagine a student learning a new language. When the student encounters a difficult word (uncertain prediction), asking the teacher (feedback) improves their understanding. The ongoing learning process becomes more powerful with expert input, ensuring the student gets better in the language, just like how models evolve using feedback from specialists in the field.
Best Practices in Deployment and Monitoring
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- Use version control for models and datasets
- Build reproducible pipelines using tools like DVC or MLflow
- Secure your APIs to prevent unauthorized access
- Validate models with staging environments before live deployment
- Monitor continuously and set up alerting systems
Detailed Explanation
Best practices ensure that deployment and monitoring processes are efficient and secure. Using version control helps keep track of updates and changes made to models and datasets. Reliable pipelines can be built using specialized tools to ensure consistency. Securing APIs is important to prevent unauthorized access to the model. Validating models in staging environments means testing them in a safe space to identify any issues before they go live. Lastly, continuous monitoring and alerting systems help quickly catch any deviations from expected performance.
Examples & Analogies
Establishing best practices is like setting safety protocols in a factory. With version control, you always know the latest machine designs (models) and tools (datasets). Reproducible pipelines are like standardized production methods that ensure every item meets quality standards. Regular checks and barriers (like secured APIs and staging environments) help avoid mishaps and maintain optimal operations.
Challenges in Deployment and Monitoring
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- Ensuring reproducibility across environments
- Scaling inference to meet high demand
- Maintaining model performance as data evolves
- Managing model dependencies and environment mismatches
- Handling bias, fairness, and interpretability in production models
Detailed Explanation
Challenges in deploying and monitoring machine learning models stem from various factors. Ensuring that a model behaves consistently across different environments can be tricky, as differences in software or hardware can lead to discrepancies. Scaling inference is crucial for handling increased demand without lagging response times. Models must adapt to new data trends to maintain performance over time, necessitating effective management. Additionally, models can have dependencies that differ from the production environment or can introduce biases, which raises concerns about fairness and interpretability in how outcomes are derived.
Examples & Analogies
Consider deploying and monitoring a model like running a complex restaurant. Ensuring that dishes taste the same in different locations (reproducibility) can be challenging due to varying ingredients or cooking methods. When customer demand spikes (scaling inference), the chefs need to summon all hands on deck without sacrificing food quality (model performance). Handling dietary preferences and allergies (bias and fairness) also requires careful consideration in how meals are prepared and served.
Definitions & Key Concepts
Learn essential terms and foundational ideas that form the basis of the topic.
Key Concepts
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Model Deployment: Integration of ML models into production for real-time predictions.
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Monitoring: Continuous tracking of model performance and input data to ensure reliability.
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Retraining: Process of updating models with new data to maintain their accuracy.
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Data Drift: Changes in data distribution that can affect model performance.
Examples & Real-Life Applications
See how the concepts apply in real-world scenarios to understand their practical implications.
Examples
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Example of batch inference: A financial institution running credit scoring models every night on batches of customer data.
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Example of online inference: An e-commerce website providing personalized product recommendations based on real-time user behavior.
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Example of tool selection: Using TensorFlow Serving for deploying TensorFlow models with efficient REST APIs.
Memory Aids
Use mnemonics, acronyms, or visual cues to help remember key information more easily.
π΅ Rhymes Time
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When deploying your model, don't forget the aim, Package, Expose, Monitor, are key to the game.
π Fascinating Stories
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Imagine you're a gardener nurturing a plant. You must plant the seed (Deployment), water it regularly (Monitoring), and prune it for growth (Retraining) to ensure it blooms into a beautiful flower.
π§ Other Memory Gems
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Remember 'PEM' for deployment: Package, Expose, Monitor; and '5 P's': Predictions, Performance, Pattern, Parameters, Processing speed for monitoring.
π― Super Acronyms
Use **DRIP** to remember the kinds of drift
- Data Drift
- Relationship Drift
- Input Drift
- Performance Drift.
Flash Cards
Review key concepts with flashcards.
Glossary of Terms
Review the Definitions for terms.
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Term: Model Deployment
Definition:
The process of integrating a machine learning model into a production environment for live data predictions.
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Term: Batch Inference
Definition:
A deployment scenario where predictions are made on a large dataset at regular intervals.
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Term: Online Inference
Definition:
A deployment scenario that allows real-time predictions as new data arrives.
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Term: Containerization
Definition:
The process of packaging software code and its dependencies together in a virtual container.
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Term: Data Drift
Definition:
A change in the distribution of input data that can degrade model performance.
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Term: Concept Drift
Definition:
A change in the relationship between input features and the target label over time.
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Term: Model Staleness
Definition:
A state when a model is outdated due to being trained on old data.
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Term: CI/CD
Definition:
A set of practices for Continuous Integration and Continuous Deployment to improve software development efficiency.