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AI and Machine Learning (ML) are everywhere. Amazon recommends products using machine learning algorithms. TGI Fridays made a virtual bartender using machine learning. Car manufacturers use machine learning to make cars drive themselves. This can be a long and painful process.
Use cases are limitless. The ML algorithm offers unique advantages but is challenging to develop, maintain or deploy. It would help if you had Machine Learning Operations (MLOps).
MLOps can be a tricky beast by itself. Even well-designed MLOps are prone to chaos if deployed unthinkingly. It's essential to have a plan for how to implement MLOps.
MLOps, based on DevOps for software development practices, brings together diverse teams within an organization to accelerate the deployment and machine learning development services. This article will provide a detailed guide on MLOps and how it streamlines the end-to-end ML process. We'll also share some case studies of companies that have implemented it.
What is MLOps?
MLOps is an acronym for Machine Learning Operations. It's a collection of best practices that standardizes and streamlines machine-learning systems' construction and deployment process.
Take a minute to review our article What is MLOps? Article. Both concepts are explained in plain English, showing how and why businesses use them.
Developing machine learning models requires specialized dataset preparation, evaluation and maintenance expertise. It's not easy to get ML models that provide consistent business objectives value. This requires a continuous collaborative effort between machine learning engineers, software developers, and data scientists.
The collaboration is complex and new, leading to delays, friction and errors. MLOps aims to change that. The tools, procedures and workflows that businesses use to integrate reliable machine learning into their software are the most important.
ModelOps vs MLOps
ModelOps can be used in two ways:
- Meaning 1: It includes the automation and management, for example, of rule-based AI and Machine Learning models. ModelOps can therefore be considered a subset of MLOps that only deals with ML-based models.
- Meaning 2: ModelOps is often used interchangeably with MLOps, as rule-based AI AI models have become less prevalent, and data scientists tend to focus on models based only on machine learning.
What Is The Difference Between DevOps and MLOps?
MLOps is similar to DevOps in that it aims to close the gap between machine learning development and operations within an organization. One fundamental difference: machine learning uses data and that data changes constantly. A machine-learning system must continuously adapt and learn from new inputs. MLOps is a new approach that brings with it many challenges.
- Continuous integration/Continuous delivery (CI/CD) practices in DevOps are the automation of testing and validating code changes from multiple contributors, integrating them into a single software project, and releasing them to production. CI/CD in MLOps involves testing, validating and integrating code, data and models.
- MLOps' Continuous Training (CT) feature is not part of DevOps. Continuous training allows the model to be retrained to keep up with changes to the data.
How Much Interest Is There in MLOps?
A report from Deloitte predicts that the MLOps market will grow to $4 billion in 2025, up from $350 million today. Businesses realize that as machine learning becomes a core business component, they will need a systematized and automated method to implement ML. This is reflected in the graph below.
The Key Components in MLOps Strategy
All stakeholders should have access to your MLOps Strategy. This is not a one-time process. An MLOps Strategy can and should evolve.
It should include the following:
Current Friction Points
Gather information about current pain points from the teams in your company if you already use ML models. You might devise an MLOps plan that fixes future issues before current ones.
Ideal Workflow
How would the perfect MLOps look for your company? Do not worry about it being perfect on the first try. Your section will change as your team, business functions, and work goals evolve.
Budget
Constraints are essential. It's unlikely that you have an unlimited budget to hire consultants and buy expensive software. You should know your budget and what your ideal workflow will cost.
Quick-Term Solutions
Put your pain points, ideal workflow and budget together. Find the most problematic problems and then identify solutions. You should break your problem down into short- and long-term options if it is a complex situation.
Long Term Solutions
It's unlikely that you can fix your entire problem at once. You may also have a good idea of what issues will arise as your business grows. Here's where they go. Outline the pain points that you will solve in the future and how to fix them. Also, outline any pain points for which you have a vision.
Team Ownership Structure
If nobody is in charge, nothing will be done. Each problem or solution must be assigned responsibility. Depending on your strategy's complexity, you may choose to distribute ownership between teams. Hiring an MLOps Architect can make this process more manageable if you have the budget.
Timeline
Decide when you want everything to happen. The timeline must include the dates of all processes, people, and tools. Long-term and short-term objectives may be set at specific dates.
Let's now look at three stages in an MLOps approach: automated, manual and CI/CD.
Manual MLOps Strategy
A manual MLOps structure has no automation. Each step of the MLOps process involves manual work: data gathering, data preparation and model training. Manual processes may not be the best long-term solution, but they are cheap and straightforward. Manual processes provide an excellent way to address immediate issues.
Manual MLOps are subject to human errors. You'll need to use the same manual process to deploy and train your ML models whenever you wish to update or retrain your model. Model degradation may not be detected by manual model monitoring. Manual processes are also slow. Every time they take a long time.
Manual MLOps reduce friction in the software, data and ML teams. However, it falls short of other MLOps objectives. It isn't easy to make iterations on ML models without automation. ML models, in particular, are prone to drifting data and degradation over time. Imagine an application that creates bugs by design. Please fix the software and deploy it again as quickly as possible!
It's like driving every time you want new tires just so the factory can replace them. It's excellent for making cars but terrible for maintaining them.
It will help if you automate these processes as soon as possible. Automating the procedures should be done as quickly as possible.
This will be a sweet spot for many companies. An MLOps automated strategy requires the development of an automated machine learning pipeline. Integration of multiple software tools can reduce the amount of manual work needed to refine ML models.
Start by configuring Argo workflows, or NATS. These tools manage training data flow from storage into Python libraries such as Pandas. These tools can run scripts to automatically ingest, verify, clean, and separate data into discrete groups.
These tools can also automate ML model development, tracking experiments, and evaluation if data is not your primary concern. This process can be linked to an event, like the release of new data.
Automating ML model verification can be done using similar workflows. You can, for example, choose only to show the performance results of iterations that have passed validation.
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What Are The Components Of Machine Learning Lifecycle?
The typical process for developing a machine-learning model consists of the following:
- Determine your business requirements objectives.
- Data Collection and Exploration.
- Data Processing and Feature Engineering.
- Model training.
- Validation and model testing.
- Model deployment.
- Model monitoring.
How Does MLOps Contribute To The ML Lifecycle?
MLOps is the creation of an end-to-end machine-learning pipeline that automates the retraining and deployment of models.
A small team of data scientists can deploy your machine-learning application manually if you only have one algorithm, and it does not need to be adjusted frequently for the changing environment. If you are looking to scale up your machine-learning applications, an automated system is required. A machine learning pipeline automates the following steps in the ML Lifecycle:
Data Processing and Feature Engineering
- Data Labeling: MLOps Tools can automate a part of the data labeling process, which is time-consuming and prone to error when performed manually.
- Automated Features Engineering and FeatureStores: Features can be generated from raw data. The created features can be stored and standardized in feature factories or stores for reuse. The feature engineering process requires domain expertise about the problem. Some manual work is needed, but automation and feature stores can save time.
Model Training
- Automated Hyperparameter Optimization: Search and select hyperparameters automatically that provide optimal performance to machine learning models.
- Automated Data Validation: Before model trains are placed on new data, it is validated to ensure that it has specific properties. Data scientists can stop the pipeline if it is not valid.
- Continued Training (CT): The deployed model will be automatically trained using new data after validation. The model is automatically trained if you have already deployed it.
MLOps includes tools that improve reproducibility in ML and AI training and development processes.
- Experiment Tracking: Keeping tabs on important information during model training, such as hyperparameters and different models of ML, data sets, codes etc.
- Registry of Models: This registry archives all models, their metadata and other information and keeps it in one central location for later retrieval.
- ML Metadata Store: Central repository to store the metadata for ML models. This includes the model creator, creation date, training data, parameter values, location, performance metrics, etc.
Validation and Model Testing
Automatic Model Validation: The model's performance is evaluated by measuring performance metrics and comparing it to older versions.
Model Deployment
Continuous delivery and continuous deployment (CD) of the Model: A trained and validated model is deployed as a service to predict future events automatically in production. There are two types of deployment methods:
- The online inference is when the model produces outputs, often in real-time, through an API.
- In batch inference, the model is run periodically to produce results.
Model Monitoring
Monitoring Automation: A pipeline is continuously monitored in real-time to ensure that model performance exceeds a certain threshold and avoids model or data drift. The channel is updated by triggering a trigger.
Pipeline Automation
MLOps requires:
- Automatic Transition Between Steps: The transition between steps in the ML Lifecycle is automatized. It allows rapid experimentation with different models.
- Pipeline Continuous Integration: The pipeline is continuously integrated (CI). Codes, components, and other code from different developers are automatically tested, verified, and integrated.
- CD of the Pipeline: Codes and components integrated into the pipeline are delivered to the environment.
-
Triggers:Triggers the pipeline to train the deployed model. You can trigger it:
- If new data is constantly available, it is possible to schedule the release of the information.
- New data is available.
- If the performance of the model degrades at a certain point
- in demand
What Is The Importance of MLOps Now?
MLOps are essential to every company that uses machine learning.
- Standardizes the ML process. The framework provides a unified approach to be followed. It helps facilitate communication among data scientists, subject experts, software engineers and ML engineers.
- This reduces risk by providing continuous monitoring and model adjustment. The data used to make predictions by the model changes as the business environment evolves. The model's quality may deteriorate when new data are fundamentally different from the dataset used for training. Depending on your use case, a change in model accuracy could be detrimental to your business.
- Reproducibility is improved in AI and Machine Learning. It is crucial to have reproducibility in ML as this increases predictability and reliability. MLOps offers best practices and tools for improving reproducibility.
- This increases the scalability and efficiency of ML-based projects. It is necessary to use MLOps to build, maintain and control multiple models and the relationships between them.
Examples of MLOps in Practice and Their Benefits
1. Improve Customer Experience with Banking
MLOps was implemented by a leading bank to simplify its onboarding of new customers. The bank implemented ML models to verify customer data and detect fraud instantly. The onboarding process was made faster, and the customers' experience improved. This increased the customer's trust in the bank, as it reduced fraud risk.
2. Enhancing Supply Chain Management
MLOps was used by a large retailer to optimize its supply chain. The machine learning development company utilized ML models to forecast demand and maximize warehouse resource distribution. The result was improved forecasting accuracy, decreased waste and increased efficiency within the supply chain.
3. Improve Healthcare Outcomes
MLOps were used by healthcare providers to improve the outcomes of patients. This provider used machine learning models to analyze patient data to determine which patients were most at risk for adverse events. The provider used this information to prevent harmful events. As a result, the provider observed a marked improvement in patient outcomes.
Here are a few real-world examples of MLOps being implemented to produce tangible business benefits. There are, however, many others.
MLOps are a critical component of successful ML projects. Organizations can unlock the potential of machine learning by operationalizing models. MLOps unlocks the potential of machine learning, whether it's improving the customer experience, supply chain management, or health outcomes.
Read More: How to start implementing AI & ML to your existing mobile apps
A Real-World Use Case for MLOps
Google Cloud AI Platform for Supply Chain Optimization
The demands of a rapidly expanding business were too much for a large logistics firm. Supply chain management was manual, prone to error, and led to increased costs and customer dissatisfaction.
The company used MLOps and the Google Cloud AI Platform to improve supply chain management. The company used the Google Cloud AI Platform to create and deploy ML-based models which could optimize its supply processes.
The ML models could accurately predict demand and optimize routes based on historical supply chain data. They also reduced delivery times. The overall supply chain efficiency was improved, and costs were reduced.
The company also used the Google Cloud AI Platform for MLOps to operationalize their ML models. The company was able to update its ML models quickly as the supply chain changed and monitor performance in real time.
The results were stunning. It saw significant cost reductions, increased efficiency and improved customer satisfaction. The company optimized its supply chain by leveraging MLOps, Google Cloud AI Platform and other tools. This allowed them to remain competitive and optimize their market.
This example shows the advantages that organizations can derive from using MLOps in conjunction with Google Cloud. Combining the power of Machine Learning with the reliability and scalability of Google Cloud allows organizations to deliver business value while staying ahead of their competition.
Example Case Studies: What Are They?
- Uber: Uber made machine learning scaleable for diverse applications, such as estimating the arrival of meals, predicting demand in different areas, and providing customer service. It is not just about getting the right technology. It involves efficient coordination between other teams. Uber Michelangelo is a machine-learning platform created by the company to standardize workflows across different groups.
- Booking.com: Booking.com currently has 150 machine-learning models. The company explains that an integrated, iterative process that is hypothesis-driven and integrates with other disciplines has been critical in building 150 machine-learning products.
- Cevo: Cevo has built a pipeline of automated machine learning models for their financial sector clients. They wanted multiple ML model deployments and maintenance to prevent and detect fraud. By applying MLOps to this project, they claim their client has been able to reduce training and deployment times for ML models to just a few days. In just three hours, for example, they were able to create a model that could detect new fraud types each month.
Which Tools Support MLOps and What Are Their Categories?
MLOps platform facilitates end-to-end ML management, from deploying to managing and monitoring all machine-learning models on a single system. MLOps can be categorized into the following categories:
- The tools for feature engineering. Most auto ML programs provide this.
- Tracking tools for experiments
- AI platforms for building models
- Model Risk/Performance Measurement Software
To Use MLOps in Real-time, Start-ups Must Combine The Best Practices
DevOps is the industry standard for managing an application's operations throughout its development cycle. If they are to apply MLOps in real-time, businesses must adopt a DevOps approach to the ML Lifecycle. This method is referred to as "MLOps". Machine learning and operations are abbreviated as MLOps. MLOps is a collection of best practices that standardizes and streamlines machine learning development system construction and deployment processes.
This new area calls for a mix of the best practices in software development, DevOps and data science. The optimization of model creation, deployment and administration helps to reduce conflict between IT operation teams and data scientists. Congnilytica estimates that the MLOps market will grow by more than US$4 Billion by 2025. Most of the data scientist's time is spent cleaning and preparing the data for training. Models that have been trained must be tested for accuracy and stability.
Amazon SageMaker
Amazon SageMaker offers Machine Learning Operations (MLOps) solutions that automate and standardize procedures throughout the entire ML lifecycle. The software helps ML engineers to be more productive by assisting them in developing, evaluating, deploying, and managing ML models.
Azure Machine Learning
Azure Machine Learning Services is a cloud-based platform combining data science and machine learning. The built-in compliance, governance and security allow users to run machine learning workloads from anywhere. Build precise models quickly for computer vision, classification, regression and time series forecasting.
Databricks' MLflow
Databricks has created Managed MLflow on top of its open-source technology MLflow. Users can manage the entire machine-learning lifecycle with dependability, scale, and security. MLFLOW Tracking uses Python, REST API, RAPID, and Java API to log code versions, artifacts, metrics and parameters with every run.
TensorFlow Extended
Google has created a large-scale platform for machine learning called TensorFlow Extended. The platform offers shared libraries and frameworks for integrating machine learning into the workflow. TensorFlow Extended allows users to coordinate machine-learning workflows across various platforms, such as Apache Beam and KubeFlow. TensorFlow enhances the TFX design and helps users analyze and validate machine learning data.
MLFlow
A project called MLFlow, an open-source initiative aims to create a standard machine-learning language. A platform is a tool for managing the entire machine-learning cycle. Data science teams can use it as a complete solution. Users can manage Hadoop, Spark or Spark SQL clusters running on Amazon Web Services (AWS) in production, on-premises or both.
Google Cloud ML Engine
Google Cloud ML Engine is a managed service that simplifies creating, training, and using machine learning models. The service offers a uniform user interface to build, use, and track machine learning models. With the help of cloud storage and big query, users can prepare and save their data. The data can be labeled using the built-in functionality.
Data Version Control
DVC, a Python-based data science and machine learning platform, is open-source. The platform aims to replicate and share machine learning models. It can handle large files, data, machine learning models, metrics and code. DVC links data sets, machine learning models and intermediate files. Archiving files on HDFS Aliyun OSS Amazon S3, Microsoft Azure Blob Storage, Google Cloud Storage and other cloud services.
H2O Driverless AI
The cloud-based H2O driverless AI machine learning platform allows you to create, train and deploy machine learning models with just a few clicks. Supported programming languages include R, Python and Scala. Driverless AI can access data from multiple sources, such as Hadoop HDFS and Amazon S3.
Kubeflow
Kubeflow is a cloud-native machine learning platform that combines training and deployment. Kubernetes, Prometheus and the Cloud Native Computing Foundation are all components of CNCF. This tool allows users to create their MLOps stack using cloud providers such as Google Cloud and Amazon Web Services.
Metaflow
Netflix created the Python-based Metaflow framework to help data scientists, engineers, and professionals manage practical projects. It also helps them boost their productivity. The uniform API stack is essential to complete data science projects, from prototype to production. Metaflow integrates Python-based Machine Learning with Amazon SageMaker Deep Learning and Big Data Frameworks. Users can quickly deploy and maintain ML Models.
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Conclusion
These examples are just a tiny sample of how MLOps will transform various industries by 2023. Organizations recognize the value of incorporating MLOps into their ML workflows as machine learning becomes more prevalent.
MLOps methods, including automated model deployments, model monitoring, model retraining and over-the-air updates of models, as well as automated A/B tests, can help companies ensure reliability, scalability and performance in their ML model production environments. We can anticipate even more advancements as the MLOps field evolves. This will enable organizations to unleash the full power of AI and Machine Learning.
