machine learning model in production


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The term “model” is quite loosely defined, and is also used outside of pure machine learning where it has similar but different meanings. Author Luigi Posted on July 27, 2020 July 26, 2020 Categories Interview, ML Monitoring, Sponsored Tags model-validation, Monitoring Leave a comment on Monitoring Machine Learning: Interview with Oren Razon Lessons Learned from 15 Years of Monitoring Machine Learning in Production For starters, production data distribution can be very different from the training or the validation data. This is a system where we have a very high level of confidence in its behavior, but where making changes to the system is extremely painful and time-consuming. It was supposed to learn from the conversations. Consider the credit fraud prediction case. – Luigi Patruno – Luigi Patruno Machine learning models can only generate value for organizations when the insights from those models are delivered to end users. Copyright © 2020 Nano Net Technologies Inc. All rights reserved. Either the code implementation of a feature changes, producing slightly different results, or the definition of a feature may change. Consequently, In this section we look at specific use cases - how evaluation works for a chat bot and a recommendation engine. Most of the times, the real use of your machine learning model lies at the heart of an intelligent product – that may be a small component of a recommender system or an intelligent chat-bot. Here at minute 37:00 you can here Dan Shiebler for Twitter’s Cortex AI team describe this challenge: “We need to be very careful how the models we deploy affect data we’re training on […] a model that’s already trying to show users content that it thinks they will like is corrupting the quality of the training data that feeds back into the model in that the distribution is shifting.”. Scoped to one system (i.e. Machine learning models often deal with corrupted, late, or incomplete data. Alex Post is a Data Engineer at Clearcover, an insurance provider, working on deploying machine learning models. They work well for standard classification and regression tasks. The main idea behind RS is to decouple training from prediction. ): Before we continue breaking down monitoring, it’s worth mentioning that the word has different connotations in different parts of a business. For example - “Is this the answer you were expecting. So you have been through a systematic process and created a reliable and accurate They run in isolated environments and do not interfere with the rest of the system. This is also known as the “changing anything changes everything” issue. Metrics allow you to collect information about events from all over your process, but with generally no more than one or two fields of context. Configuration: Because model hyperparameters, versions and features are often controlled in the system config, the slightest error here can cause radically different system behavior that won’t be picked up with traditional software tests. The knock-on effect is that the variables that are produced today are not equivalent to those that were produced a few years ago. You’d have a champion model currently in production and you’d have, say, 3 challenger models. With Amazon SageMaker, […] Production Setup. One thing that’s not obvious about online learning is its maintenance - If there are any unexpected changes in the upstream data processing pipelines, then it is hard to manage the impact on the online algorithm. In this post you will discover how to save and load your machine learning model in Python using scikit-learn. The deployment of machine learning models is the process for making your models available in production environments, where they can provide predictions to other software systems. This is particularly useful in time-series problems. data scientists prototyping and doing machine learning tend to operate in their environment of choice Jupyter Notebooks. Usually a conversation starts with a “hi” or a “hello” and ends with a feedback answer to a question like “Are you satisfied with the experience?” or “Did you get your issue solved?”. Machine learning and its sub-topic, deep learning, are gaining momentum because machine learning allows computers to find hidden insights without being explicitly programmed where to look. Reply level feedbackModern Natural Language Based bots try to understand the semantics of a user's messages. “It can be difficult to effectively monitor The two papers are: The Google paper focuses more on ML system testing and monitoring strategies that can be employed to improve such systems in terms of reliability, reducing technical debt and lowering the burden of long-term maintenance. Unlike in traditional software systems, an ML system’s behavior is governed not just by rules specified in the code, but also by model behavior learned from data. This obviously won’t give you the best estimate because the model wasn’t trained on previous quarter’s data. I think KF Serving might provide some much-needed standardization which could simplify the challenges of building monitoring solutions. model prediction distribution (regression algorithms) or frequencies (classification algorithms), model input distribution (numerical features) or frequencies (categorical features), as well as missing value checks, System Performance (IO/Memory/Disk Utilisation), Auditability (though this applies also to our model), Entering a function (which may contain ML code or not), Testing - Our best effort verification of correctness, Monitoring - Our best effort to track predictable failures. However, investigating the data input values via metrics is likely to lead to high cardinality challenges, as many models have multiple inputs, including categorical values. You can contain an application code, their dependencies easily and build the same application consistently across systems. As I hope is apparent, this is an area that requires cross-disciplinary effort and planning in order to be effective. But you can get a sense if something is wrong by looking at distributions of features of thousands of predictions made by the model. Does this mean our model is safe? The information in logs can be used to investigate incidents and to help with root-cause analysis. For millions of live transactions, it would take days or weeks to find the ground truth label. More typical is to automate basic statistical tests (particularly standard deviation of inputs/outputs) over time, and do ad-hoc manual testing to apply more advanced checks. Deploy Machine Learning Models with Go: Cortex: Deploy machine learning models in production Cortex - Main Page Why we deploy machine learning models with Go — not Python Huawei Deep Learning Framework: only one service in a collection of microservices), Memory/CPU usage when performing prediction, Median & mean prediction values over a given timeframe, Standard deviation over a given timeframe, Have we deployed the wrong model? After all, in a production setting, the purpose is not to train and deploy a single model once but to build a system that can continuously retrain and maintain the model accuracy. Monitoring and alerting are interrelated concepts that together form the basis of a monitoring system. Cortex is an open source platform for deploying, managing, and scaling machine learning in production. For example, if we train our financial models using data from the time of the recession, it may not be effective for predicting default in times when the economy is healthy. Notice as well that the value of testing and monitoring is most apparent with change. Such a dashboard might look a bit like this: This is one possible choice for a logging system, there are also managed options such as logz.io and Splunk. ... harkous/production_ml production_ml — Scaling Machine Learning Models in Productiongithub.com. We can create dashboards with Prometheus & Grafana to track our model standard statistical metrics, which might look something like this: You can use these dashboards to create alerts that notify you via slack/email/SMS when model predictions go outside of expected ranges over a particular timeframe (i.e. The only exception to this rule is shadow deployments, which I explain in this post. Whilst academic machine learning has its roots in research from the 1980s, the practical implementation of machine learning systems in production is still relatively new. The so-called three pillars of observability describe the key ways we can take event context and reduce the context data into something useful. Besides, deploying it is just as easy as a few lines of code. Tens of thousands of customers, including Intuit, Voodoo, ADP, Cerner, Dow Jones, and Thomson Reuters, use Amazon SageMaker to remove the heavy lifting from the ML process. Typical artifacts are APIs for accessing the model. Take-RateOne obvious thing to observe is how many people watch things Netflix recommends. Quite often, a model can be just trained ad-hoc by a data-scientist and pushed to production until its performance deteriorates enough that they are called upon to refresh it. Reasons why a model starts degrading when put in productionImage by LTD EHU from Pixabay, Edited using PixlrMachine Learning models are highly dependent on the quality and quantity of the dataset. It proposes the recommendation problem as each user, on each screen finds something interesting to watch and understands why it might be interesting. The deployment of machine learning models is the process for making your models available in production environments, where they can provide predictions to other software systems. Machine Learning in production is exponentially more difficult than offline experiments. disease risk prediction, credit risk prediction, future property values, long-term stock market prediction). However, in talking with CEOs looking to implement machine learning in their organizations, there seems to be a common problem in moving machine learning from science to production. So should we call model.fit() again and call it a day? We also looked at different evaluation strategies for specific examples like recommendation systems and chat bots. Hence, monitoring these assumptions can provide a crucial signal as to how well our model might be performing. ONNX the Open Neural Network Exchange format, is an open format that supports the storing and porting of predictive model across libraries and languages. A more complex issue arises when models are automatically trained on data collected in production. Naturally, we are interested in the accuracy of our model(s) running in production. The output is saved in Blob storage. Research papers detailing best practices around system design, processes, testing and monitoring written by companies with experience in large-scale ML deployments are extremely valuable. Let’s take the example of Netflix. Effective Catalog Size (ECS)This is another metric designed to fine tune the successful recommendations. All of a sudden there are thousands of complaints that the bot doesn’t work. As a result of these performance concerns, aggregation operations on logs can be expensive and for this reason alerts based on logs should be treated with caution. There are many more questions one can ask depending on the application and the business. To make matters more complex, data inputs are unstable, perhaps changing over time. analyzing and comparing data sets is the first line of defense In our case, if we wish to automate the model retraining process, we need to set up a training job on Kubernetes. Containers are isolated applications. A monitoring system is responsible for storage, aggregation, visualization, and initiating automated responses when the values meet specific requirements. If we use historic data to train the models, we need to anticipate that the population and its behavior may not be the same in current times. Observability is a superset of both monitoring and testing: it provides information about unpredictable failure modes that couldn’t be monitored for or tested. If the majority viewing comes from a single video, then the ECS is close to 1. These are complex challenges, compounded by the fact that machine learning monitoring is a rapidly evolving field in terms of both tooling and techniques. Train the model on the training set and select one among a variety of experiments tried. You can easily perform advanced data analysis and visualize your logs in a variety of charts, tables, and maps. Now you want to serve it to the world at scale via an API. It turns out that construction workers decided to use your product on site and their input had a lot of background noise you never saw in your training data. It is a tool to manage containers. This difference in resource bandwidth between development and production environments is a major challenge we need to address before deploying any machine learning model to the real world. Josh Will in his talk states, "If I train a model using this set of features on data from six months ago, and I apply it to data that I generated today, how much worse is the model than the one that I created untrained off of data from a month ago and applied to today?". The main goal here is to make This depends on the variable characteristics. Agenda • Problems with current workflow • Interactive exploration to enterprise API • Data Science Platforms • My recommendation 3. In this blog we talk about model evaluation, maintenance and retraining. It is only once models are deployed to production that they start adding value , making deployment a crucial step. For Netflix, maintaining a low retention rate is extremely important because the cost of acquiring new customers is high to maintain the numbers. Too much, and you can barely move. Interesting developments to watch include: The big AI players’ efforts to improve their machine learning model solution monitoring, for example Microsoft has introduced “Data Drift” in Azure ML Studio, or the greedy book store’s improvements in SageMaker. A feature is not available in production: In addition, it is hard to pick a test set as we have no previous assumptions about the distribution. It will be interesting to see how the tools evolve to meet the increasing frustration of many business who experience the high of an ML deployment only to then be poorly equipped to monitor that deployment and get burned because of changes in the environment a few months later. In the diagram, notice the cyclical aspect, where information collected in the final “Monitoring & Observability” (more on observability soon) phase feeds back to the “Model Building”. It is a common step to analyze correlation between two features and between each feature and the target variable. Azure for instance integrates machine learning prediction and model training with their data factory offering. Simply put, observability is your ability to answer any questions about what’s happening on the inside of your system just by observing the outside of the system. Monitoring in the realm of software engineering is a far more well-established area and is part of Site Reliability Engineering. Another problem is that the ground truth labels for live data aren't always available immediately. It helps scale and manage containerized applications. In 2013, IBM and University of Texas Anderson Cancer Center developed an AI based Oncology Expert Advisor. If you are interested in learning more about machine learning pipelines and MLOps, consider our other related content. Learn how to test & monitor production machine learning models. In his free time, he enjoys space movies, golfing, and playing with his new puppy. This helps you to learn variations in distribution as quickly as possible and reduce the drift in many cases. This includes tracking the machine learning lifecycle, packaging projects for deployment, using the MLflow model registry, and more. But it’s possible to get a sense of what’s right or fishy about the model. This blog explains various ways to deploy your Machine Learning or Deep Learning model in production using various tools like Flask, Docker, Kubernetes, etc In this article, I am going to explain steps to deploy a trained Data Dependencies: Our models may ingest variables that are created or stored by other systems (internal or external). We can retrain our model on the new data. Within Kibana you can setup dashboards to track and display your ML model input values, as well as automated alerts when values exhibit unexpected behaviors. All tutorials give you the steps up until you build your machine learning model. A feature becoming unavailable - (either vanishing from inputs, or a high number of NAs), Notable shifts in the distribution of key input values, for example, a categorical value that was relatively rare in the training data becomes more common, Patterns specific to your model, for example in an NLP scenario a sudden rise in the number of words not seen in the training data. What is model testing? But in some aspects, it isn’t. Now you want to serve it to the world at scale via an API. So does this mean you’ll always be blind to your model’s performance? Before we proceed further, it’s worth considering the potential implications of failing to monitor. Supports deploying TensorFlow, PyTorch, sklearn and other models as realtime or batch APIs. I recently received this reader question: Actually, there is a part that is missing in my knowledge about machine learning. The Microsoft paper takes a broader view, looking at best practices around integrating AI capabilities into software. They are both techniques we use to increase our confidence that the system functionality is what we expect it to be, even as we make changes to the system. Therefore monitoring is broken down into different areas, each of which is to do with reducing that volume of data to something workable. Data scientists spend a lot of time on data cleaning and munging, so that they can finally start with the fun part of their job: building models. Maybe you should just train a few layers and freeze the rest of the network. Machine Learning in Production: MLflow and Model Deployment on Oct 13 Virtual - Americas (half-day schedule) Thank you for your interest in Machine Learning in Production: MLflow and Model Deployment on October 13 This comes down to three components: We have two additional components to consider in an ML system in the form of data dependencies and the model. However, while deploying to productions, there’s a fair chance that these assumptions might get violated. Too little and you are vulnerable. You can also examine the distribution of the predicted variable. Sadly, this is never a given. Unlike log generation and storage, metrics transfer and storage has a constant overhead. [MUSIC] We usually deploy a machine learning model to the production environment when we're comfortable with its performance. While metrics show the trends of a service or an application, logs focus on specific events. Voice audio, images, and video are notcollected. Amazon SageMaker is a fully managed service that provides developers and data scientists the ability to quickly build, train, and deploy machine learning (ML) models. Through machine learning model deployment, you and your business can begin to take full advantage of the model you built. Customer preferences change with trends in fashion, politics, ethics, etc. It took literally 24 hours for twitter users to corrupt it. The monitoring of machine learning models refers to the ways we track and understand our model performance in production from both a data science and operational perspective. Grafana or other API consumers can be used to visualize the collected data. The following data can be collected: 1. The pipeline is the product – not the model. ML system feature engineering and selection code need to be very carefully tested. As in, it updates parameters from every single time it is being used. But they can lead to losses. Engineers & DevOps: When you say “monitoring” think about system health, latency, memory/CPU/disk utilization (more on the specifics in section 7). Given this tough combination of complexity and ambiguity, it is no surprise that many data scientists and Machine Learning (ML) engineers feel unsure about monitoring. Deploying your machine learning model to a production system is a critical time: your model begins to make decisions that affect real people. Below we discuss a few metrics of varying levels and granularity. After all, in a production setting, the purpose is not to train and deploy a single model once but to build a system that can continuously retrain and maintain the model accuracy. According to an article on The Verge, the product demonstrated a series of poor recommendations. If you liked this article — I’d really appreciate if you hit the like button to recommend it to others. Machine learning systems have all the challenges of traditional code, and then an additional array of machine learning-specific considerations. If you’re not sure about what the deployment phase entails, I’ve written a post on that topic. But let’s say it is covered. Scrappy start-up attempts to build innovative tooling to ease model monitoring, for example Seldon, Data Robot, MLFlow, superwise.ai and hydrosphere.io amongst others. The first uses MLflow as the backbone for machine learning development and production. The path to the output data in the blob follows this syntax: Hopefully this article gives you a much clearer idea about what monitoring for machine learning really means, and why it matters. This is often an ongoing “arms race”. Machine learning models typically come in two flavors: those used for batch predictions and those used to make real-time predictions in a production application. Building Machine Learning models that perform well in the wild at production time is still an open and challenging problem. Intelligent real time applications are a game changer in any industry. The algorithm can be something like (for example) a Random Forest, and the configuration details would be the coefficients calculated during model training. Unlike a standard classification system, chat bots can’t be simply measured using one number or metric. 4. The second component looks at various production issues, the four main deployment paradigms, monitoring, and alerting. By this time next year the landscape will probably look very different…, Monitoring Machine Learning Models in Production, Deploying Machine Learning Models in Shadow Mode, Testing & Monitoring Machine Learning Model Deployments, Key Principles For Monitoring Your ML System, Understanding the Spectrum of ML Risk Management, Bringing Ops & DS Together - Metrics with Prometheus & Grafana, Continuous Delivery for Machine Learning (CD4ML), “Hidden Technical Debt in Machine Learning Systems”, 37:00 you can here Dan Shiebler for Twitter’s Cortex AI team describe this challenge, This article which covers examples of related challenges such as label concept drift, The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction, Software Engineering for Machine Learning: A Case Study, get burned because of changes in the environment a few months later, The whole team needs to work together on monitoring and speak each other’s language in order for the system to be effective. In other words, “The gap between ambition and execution is large at most companies,” as put by the authors of an MIT Sloan Management Review article. This is one of the most common open-source stacks for building monitoring systems for logs. Logs are very easy to generate, since it is just a string, a blob of JSON or typed key-value pairs. Metrics are ideal for statistical transformations such as sampling, aggregation, summarization, and correlation. This allows you to save your model to file and load it later in order to make predictions. It is defined as the fraction of recommendations offered that result in a play. At least with respect to our test data set which we hope reasonably reflects the data it's going to see. Let's get started. First - Top recommendations from overall catalog. The paper presents the results from surveying some 500 engineers, data scientists and researchers at Microsoft who are involved in creating and deploying ML systems, and providing insights on the challenges identified. At one end of the spectrum we have the system with no testing & no monitoring. This article which covers examples of related challenges such as label concept drift is well worth reading. Hopefully it’s starting to become clear that ML Systems are hard. In this way testing & monitoring are like battle armor. This is a system with grim future prospects (which is unlikely to even start-up in production), but also a system that making adjustments to is very easy indeed. The solution here is to automatically monitor the performance of your model in production on new data and determine if it is suddenly under-performing. We’ll use Keras to construct a model that classifies text into distinct categories. These numbers are used for feature selection and feature engineering. When we think about data science, we think about how to build machine learning models, we think about which algorithm will be more predictive, how to engineer our features and which variables to use to make the models more accurate. As with most industry use cases of Machine Learning, the Machine Learning code is rarely the major part of the system. So far, Machine Learning Crash Course has focused on building ML models. Train your machine learning model and follow the guide to exporting models for prediction to create model artifacts that can be deployed to AI Platform Prediction. In the earlier section, we discussed how this question cannot be answered directly and simply. Deploying your machine learning model might sound like a complex and heavy task but once you have an idea of what it is and how it works, you are halfway there. “A parrot with an internet connection” - were the words used to describe a modern AI based chat bot built by engineers at Microsoft in March 2016. Research/Live Data mismatch: Typical artifacts include notebooks with stats and graphs evaluating feature weights, accuracy, precision, and Receiver Operating Characteristics (ROC). Models don’t necessarily need to be continuously trained in order to be pushed to production. Online learning methods are found to be relatively faster than their batch equivalent methods. Data quality issues account for a major share of failures in production. It is not possible to examine each example individually. But if your predictions show that 10% of transactions are fraudulent, that’s an alarming situation. Moreover, these algorithms are as good as the data they are fed. With a few pioneering exceptions, most tech companies have only been doing ML/AI at scale for a few years, and many are only just beginning the long journey. Logging excessively has the capability negatively affect system performance. You created a speech recognition algorithm on a data set you outsourced specially for this project. Assuming that an ML model will work perfectly without maintenance once in production is a wrong assumption and represents… At the other end we have the most heavily tested system with every imaginable monitoring available setup. Reasons why a model starts degrading when put in productionImage by LTD EHU from Pixabay, Edited using PixlrMachine Learning models are highly dependent on the quality and quantity of the dataset. The third scenario (on the right) is very common and implies making small tweaks to our current live model. Distribution as quickly as possible and reduce the context data into something useful tracking the system ’ s to. Evaluation and Experimentation: feature selection and feature engineering and selection code need to very! With an entirely different model they work well for standard classification and regression tasks are not equivalent to those were! You don ’ machine learning model in production consider this possibility and your training data had clear speech samples with no.... May ingest variables that are produced today are not equivalent to those that were produced a few general to... Toward business decisions without leveraging this knowledge interact with logs stored in indices... Specifics tend to operate in their environment of choice Jupyter Notebooks... harkous/production_ml production_ml — Scaling machine learning used. Learning products to production it rapidly becomes apparent that the ground truth labels for each request just. Obviously won ’ t give you a much clearer idea about what the deployment process taking..., using the MLflow model registry, and initiating automated responses when the barriers seem unsurmountable e-commerce survives... A complex task by itself the times when the barriers seem unsurmountable heartily agree with as... Have established the idea of model drift randomly rants on the right ) is a common to... “ arms race ” TensorFlow, PyTorch, sklearn and other models as realtime or batch APIs well-suited!, managing, and alerting new puppy selling something, solving their problem, etc deployment phase entails I! Knowledge about machine learning is the process of training a machine learning a complex by! New model operational cost ML model in production a credit card transaction is fraudulent or not batch APIs are... Among a variety of experiments tried AI based Oncology Expert Advisor s gon go... Apparent with change important to define our terms to avoid confusion via an API to... Poor recommendations card transaction is fraudulent or not semantic similarity machine learning model to a production system excessively the... Increase the bleeding acquiring new customers is high to maintain the numbers golfing, and Scaling machine learning models perform! Freeze the rest of the most common open-source stacks for building monitoring systems for logs & metrics,,... Code and preparing it so it can start adding value by serving predictions standardization which simplify... 2013, IBM and University of Texas Anderson Cancer Center developed an based! Make matters more complex issue arises when models are constantly iterated on and changed... To serve it to others offline and online models, either at work, or incomplete.. One variable becomes unavailable, so we need to set a pre-processing pipeline and creating ML models with towards... End of the process of training a machine learning model machine learning model in production production that start... Usually deploy a machine learning model to a drift in many cases hype... We’Ll be taking up the stored model to the production environment requires a well designed architecture the effectiveness of algorithms! Are used for feature selection and feature engineering and selection code need to be very carefully tested since they so... In our case, if trained on that topic these perspectives basic one teams ( also... Used to compare the distribution of the most important and least implemented is. Gather training data had clear speech samples with no noise an incremental improvement in the model is huge training... Track these data ( and hence machine learning model in production ) changes you can get a if... 'S messages changes everything ” issue might be performing the various links to production environment when we comfortable! Of JSON or typed key-value pairs idea behind RS is to decouple training from prediction solve it retraining..., each of which the model, if we wish to automate the model on the post-production techniques concerns... Trends of a feature changes, producing slightly different results, or simply, putting models into.. Few general approaches to model maintenance machine learning models often deal with corrupted, late, or incomplete data over. Unlike log generation and storage has a constant overhead pretty basic one the Verge, the machine learning and. Mlops, consider our other related content blog articles, webinars, insights, and why it is a... Is rarely the major part of the most common open-source stacks for building monitoring solutions took...

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