Machine Learning Model Operationalization (ML Ops): Part-1

During the industrial revolution the rise of the physical machines required organizations to systematize form factories manufacturing lines. We know about automated manufacturing and operationalized the product lifestyle by clearing waste as an automated processes and introduced a new element that is big data now, with more businesses now turning towards machine learning insights and implementations of this melting pot of a technology

ML Ops is the communication between data scientists and the production operations. It is deeply collaborative in nature designed to eliminate waste automate as much as possible and produce richer more consistent insights through machine learning. The machine learning can be a game changer for businesses and data scientists work through the lens of an organizational interest with clear direction and measurable benchmarks. ML OPS allows your data scientists freedom to do what they do best find answers it takes business decisions completely off there plates so they can build and deploy models that get you insights more quickly.

Your overall operations process can also transform how your organization handles big data just like DevOps shortens production life cycles by creating better products with each iteration and ML Ops drives insights you can trust and easily put them into play more quickly.

ML Ops comes in three phases:

·????????Designing the ML powered application

·????????ML experimentation and development

·????????ML operations

it is an iterative incremental process with continuous deployment, versioning, testing and monitoring much like your DevOps processes.

The complete ML OPS process includes 3 broad phases:

1.?Designing the ML powered application: The first phase here is devoted to business understanding, data understanding and designing the ML power software. In this stage we identify our potential user design the machine learning solution to solve its problem and assess the further development. We should act within two categories of problems either increasing the productivity of the user or increasing the interactivity of our application pretty simple right initially we define ML use cases and then prioritize them the best practice for ML projects is to work on one ML use case and one time. Furthermore the design phase aims to inspect the available data that will be necessary to train our model and to specify the functional and nonfunctional requirements of our model we should use these requirements then to design the architecture of our application established the serving strategy and create a test suite for the future ML models

2.?ML Experimentation and development phases: It is the experimentation and development phase is devoted to verifying the applicability of machine learning for our problem. We basically need to make sure if everything that we thought of in theory that's actually doable in real time and more importantly, is it a tangible goal to achieve So what you do is you implement proof of concept for your model so you run iteratively different steps such as identifying or polishing the suitable ML algorithm for your problem data engineering and model engineering and your primary goal here is to deliver a stable quality ML model that will run production.

3. Operation: The developed model introduction by using established DevOps practices the once you are so familiar with you have your testing version in continuous delivery monitoring.

All of these three phases are interconnected and influence each other for example the design decision during the design stage will propagate into experimentation phase and finally influence the deployment options during the final operations space.

But the execution unfortunately is not so simple at all, so adopting an ML OPS approach will help your organization in three major forms

1. Your operations team has business knowledge and your data science team understands data, your ML experts understand ML algorithms, your researchers understand the core concept of the project, but what lies in between all of these teams there's a wide gulf of mismatched expertise what ML ops does is that it combines the expertise of all of these champs for more efficient machine learning that leverages all sets of skills.
2. ML ops becomes more common the regulatory side of operations and it is a critical function run of our of regulatory bodies and it won't matter how much inside you've gained. ML Ops ?puts your operations team at the forefront of new regulations and best practices because ultimately as we all know all of these dev Ops, sec ops, data ops, ML ops and any X OPS methodologies are all about the culture they can take ownership of regulatory processes while your data team and ML team concentrates on deploying its creative models.
3. Finally the bottlenecks that result from complicated nonintuitive algorithms eases with a better division of expertise the bigger collaboration from operations and data teams so even here ML ops will tighten the loop there is a great chance that machine learning is optimizing a particular business application but that particular application requires multiple programs and dependencies. ML ops streamlines deploying those programs and knowing which one to put into production when deploying machine learning effectively means way more than running numbers or leaving your data scientists on their own to figure out compliance and business insight. It's crucial to take responsibility for production level ML so your operations team will know how to approach this new era of data that your data team is fully supported to do what they do best looking ahead to operations ensures that you're not only ahead of the machine learning curve but your adoption is smooth and immediately fruitful for your company, what your company is doing to be actually termed jobs there are certain principles that need to be taken care.

ML development pipeline includes 3 levels where changes can occur

·????????you have data

·????????your machine learning model

·????????your code

this means that machine learning based systems the trigger for a bill might be the combination of a code change, data change, model change so all of these principles are based on these three changes these are the most important principles to meet the development pipeline includes 3 levels.

1. Automation: Automation of your data ML model and code pipelines determines the maturity of your ML processes with increased maturity, publicity for the training of new ML models has also increased. The objective of an ML OPS team is to automate the deployment of ML models into the course software system or as a service component this means to automate the complete ML workflow without any manual intervention now this is ideal but attainable triggers for automated model training and deployment can be calendar events, messaging, monitoring, events as well as changes on data for the training code and application code automated testing helps discover problems quickly and in early stages as in DevOps this enables fast fixing of errors and learning from your mistakes to adopt ML ops we see three levels of automation the initial level manual model training and deployment going through finally up to running both ML and CI CD pipelines automatically in the final stage you have CICD system to perform fast and reliable ML model deployments in production. From the previous steps is that now you automatically build test and deploy the data model and the mill training pipeline components
2. Continuous everything or continuous X to understand model development we must first specify the mill assets ML model it's parameters and hyperparameters tuning. Scripts training and testing data we are interested in the identity components versioning and dependencies of these artifacts. The target destination for an ML artifact may be a microservice or some infrastructure component a deployment service provides orchestration logging monitoring and notification to ensure that ML models code and data artifacts that includes continuous integration, continuous delivery, continuous training, as well as continuous monitoring.
3. Versioning: Versioning now the goal of versioning is to treat ML training scripts ML models and datasets for model training as first class citizens in DevOps processes by tracking ML models and datasets with version control systems now there are more than a few reasons for when your model all your data could change it could be when your models are retrained based upon new training data or maybe they are self learning or something like your data residing across multiple platforms so to develop reliable software systems every ML model specification which is the training code that creates an should go through a code review phase and every model specification should be versioned in a version control system to make the training of these models more auditable and reproduce.
4. Experiments tracking:?ML development is a highly iterative and research centric process in contrast to traditional software development processes. In ML development multiple experiments on model training can be executed in parallel before making a decision of which model will be promoted to production. Now one way to track multiple experiments is to use different branches each dedicated to a separate experiment the output of each branch is a trained model and depending on the selected metric that trained ML models are compared with each other and the appropriate model is selected. Such can be implemented through the means of a system like git which is a version control system, you could also use a bais and biases library which automatically tracks the hyperparameters and metrics of these experiments. Now the complete development pipeline includes 3 essential components-?Data pipeline, Machine learning model pipeline and ?Application pipeline. In accordance with this separation we distinguish three different scopes for testing in comment systems so you have your tests for features and data test for model development and test for ML infrastructure. so as it is in your regular DevOps pipelines even in MLOps testing is a crucial part this makes sure that testing your ML training models should include routines which verify that algorithms making decisions are aligned with the business objectives and correlate with business impact metrics. It also makes sure that you're trained model includes up-to-date data or satisfies the business impact requirements as stale models can affect the quality of prediction in an intelligent software apart from that you obviously have your conventional unit testing for any feature creation ML model specification code and testing and model governance testing.?
5. Monitoring: Once the ML model has been deployed it needs to be monitored to assure that your model performs as expected now you have a checklist called the mildest score for monitoring activities in production which includes monitoring the dependency changes throughout the complete pipeline data version change changes in the source system and upgrade in these dependencies this picture here shows that the model monitoring can be implemented by tracking the precision recall and F1 score of a model. Prediction along with time the degrees of precision recall and airborne score triggers the model to retrain which leads to model recovery in the end the ML test score basically measures the overall readiness of the ML system for production.
6. Reproducibility: The reproducibility in a machine learning workflow means that every phase of either data processing ML model training an ML model deployment should produce identical results given the same input this basically ensures uniformity in delivery and setting a production standards now that you know all about the principles of ML OPS.