Following on from our last MLOps guide - Definitions, Stucture, Leveled Processes, and Use cases content, we're going to introduce you to the main MLOps and compare their pros and cons.
Benefits of adopting MLOps
MLOps is a combination of machine learning, DevOps, and data engineering. It integrates people, processes, and technology to help deploy and manage machine learning systems in production. While it's a new field, many organizations have recently Interest is growing.
The benefits to organizations that adopt MLOps include
- Increased speed and efficiency: MLOps provide a standardized set of processes and tools that can be used across the organization. This allows you to Increase the speed and efficiency of your machine learning development and deployment process.
- Improved quality and reliability: MLOps can continuously monitor and test models, Framework and the process of deploying new models to production can be automated. This can go a long way toward improving the quality and stability of your machine learning models.
- Cost savings: Organizations can reduce the costs associated with developing and deploying machine learning. This is because streamlining the development process and reducing the need for manual intervention makes it easier to scale machine learning deployments.
- MLOps provide a common framework for everyone involved in the machine learning lifecycle. This can help improve collaboration between different teams within an organization, such as data scientists, engineers, and operations teams.
- With better visibility into the machine learning lifecycle, you can identify and resolve issues earlier. This translates into better model performance and reduced risk.
- With a framework for continuous monitoring and auditing, you can enhance the security of your machine learning systems. MLOps help you protect sensitive data and prevent unauthorized access to your machine learning system.
Why it's time to adopt MLOps
The amount of data produced per capita is growing exponentially. As the amount of data generated increases, so does the number of people who find traditional machine learning approaches overwhelming. MLOps are the best solution to this situation;
There is also a growing need for real-time decision-making. Today's organizations need to be able to make decisions in real time. To build machine learning to assist with these decisions, machine learning teams need to provide models that can be deployed quickly and easily. By providing a framework for continuous integration and deployment (CI/CD), organizations that adopt MLOps are able to react faster to real-time insights.
Which organizations could benefit the most from adopting MLOps today?
- MLOps는 Data-driven organizations by making it easier to build, deploy, and manage machine learning models so they can get more value from their data.
- MLOps provides a scalable and reliable framework for managing machine learning models. As such, it can be a great help for organizations looking to scale their machine learning deployments.
- MLOps provides a standardized set of processes and tools. If your organization's business is based on real-time decision-making, or if you need to deliver faster and more efficient machine learning models, you need to adopt MLOps.
Many of your competitors are already adopting MLOps and using it to gain a competitive advantage. If you're not already using MLOps, we encourage you to plan to adopt today. MLOps will help you get more value from your data, enable you to use machine learning models quickly and efficiently, and provide a great opportunity to scale your model deployments.
MLOps Platform Comparison
Amazon SageMaker is a managed service in the Amazon Web Service (AWS) public cloud. It provides tools to help you build, train, and deploy machine learning models for predictive analytics applications. These integrated tools have the advantage of automating labor-intensive manual processes while reducing human error and hardware costs.
AWS SageMaker simplifies machine learning modeling into three steps: prepare, train, and deploy.
- Prepare and build AI models: Amazon SageMaker comes with many machine learning algorithms to train your data sets. This includes the Unsupervised machine learning algorithms are also included. This allows you to improve the accuracy, scale, and speed of your model.
- Training and tuning: Developers performing model training specify the location of the data in the Amazon S3 bucket and the preferred instance type, and then begin the training process. SageMaker provides automatic model tuning that finds parameters and hyper-parameters to optimize the algorithm.
- Deploy and analyze: When you're ready to deploy your models, automatically operate and scale your cloud infrastructure. SageMaker can perform deployment and monitoring, security patching, AWS auto-scaling, HTTPS endpoints to connect to apps, and more. Developers can track production performance changes and trigger alarms from Amazon CloudWatch metrics.
AWS SageMaker is utilized in a variety of industry uses. Automation tools in SageMaker Studio help users automatically debug, manage, and track machine learning models. With more than 150 pre-built solutions, you can quickly implement models and improve your machine learning workflows.
When building a production-grade solution, organizations face a number of complex challenges. For example, they may spend a lot of time setting up pipelines for training and evaluation. Tracking the experimentation and versioning of machine learning models can be resource intensive, and you may struggle to track and reproduce the output of your machine learning components.
Kubeflow on Google Cloud is an open source toolkit for building machine learning systems. Kubeflow specializes in building machine learning workflows to reduce cost and time. The platform is designed for data scientists who want to build and experiment with machine learning pipelines, but it's also efficient for engineers and operations teams who need to develop and test machine learning systems in a variety of environments for production-grade service.
Below are the benefits of using the Kubeflow platform.
- Deploy and manage machine learning systems at scale: Kubeflow's core user journey (CUJ) is a software solution for end-to-end workflows, meaning you can easily build, train, deploy, and develop models, and create, run, and explore pipelines.
- Train and experiment with machine learning models: Kubeflow provides a reliable software subsystem for training models.
- End-to-end hybrid and multi-cloud machine learning workloads: Kubeflow meets the need to develop machine learning models in hybrid as well as multi-cloud portability.
- Tuning model hyperparameters during training: Hyperparameter tuning is done with the Model Performance and accuracy. Kubeflow makes it easy to do this with its automated hyperparameter tuner (Katib). This not only reduces computation time, but also saves time and resources for model refinement.
- Continuous integration and deployment (CI/CD) for machine learning: Kubeflow Pipelines allows you to create reproducible workflows.
However, Kuberflow is tasked with managing and maintaining the Kubernetes environment, which is a namespace space. This can be challenging and complicated for some organizations. In addition, when integrating Kubeflow into existing infrastructure, organizations often find that the tools they've been using are not compatible with Kubeflow.
All MLOps need to carefully assess their organization's needs and capabilities before applying a specific solution.
MLflow is an open source platform for managing the end-to-end ML lifecycle. MLflow is library and language agnostic and works equally well in any environment, including the cloud. Being open source, it is highly scalable and designed to be useful whether your organization is 1 person or 1,000+. The MLflow platform consists of the following components: MLflow Tracking, MLflow Projects, MLflow Models, and MLflow Model Registry.
Some notable features of mlflow include
- MLflow Tracking: Keep track of the history of your experiments, comparing parameters and results.
- MLflow Projects: Package ML code into a reusable, reimplementable form to share with other data scientists or move to production.
- MLflow Models: Manage models created with different ML libraries and deploy them to different model serving and inference platforms.
- MLflow Registry: By providing a central model store, it collaboratively manages the entire lifecycle of a single MLflow model. These management tasks include model versioning, model stage transitions, and annotation.
After training and reproducing the model, MLflow serves as a standard for deploying the model with various tools (Tensorflow, Scikiy-Learn..) in various environments (Docker, Spark, Kubenetes..) to deploy inference results when the model receives input in a prescribed form.
- Provides a packaging format for ML models: You can use any directory with ML model files.
- Define dependencies for reproducibility: You can provide information about dependencies such as Konda environments in the config of an ML model.
- Model creation utility: Save models from any framework in MLflow format.
- Deployment API: Provide deployment APIs such as CLI/python/R/Java.
TensorFlow Extension (TFX) is Google's production-scale machine learning platform based on TensorFlow. The TensorFlow Extension pipeline is referred to as a tool for building scalable and reproducible machine learning workflows. It includes components that perform data transformation and processing, feature extraction, model training, performance evaluation, and more. One of the basic components of the TFX pipeline is TensorFlow Data Validation (TFDV), which plays an important role in data preprocessing, exploration, and monitoring.
TensorFlow Extension features:
- The TensorFlow Extension allows you to orchestrate machine learning workflows across multiple platforms, such as Apache Airflow, Apache Beam, and Kubeflow Pipelines.
- TensorFlow Extension is a standard set of components that can be used as part of a machine learning training script. It includes features that enable easy process building. It also includes components for implementing machine learning pipelines that help you perform advanced tasks or modeling, training, and managing machine learning experiments.
TFDV provides essential capabilities for exploring, visualizing, and cleaning your data sets. It inspects your data to automatically infer data types, categories, and ranges to help you identify outliers and missing values. It also provides visualization tools to help you understand your data sets, making it easier to spot patterns, outliers, and potential issues. By continuously evaluating incoming data against its trained schema and statistics, TFDV can alert you to potential data issues or indicate the need to retrain the model on new data.
WandB - Weights and Biases
WandB, Weights and Biases is an "ML Platform that enables developers to build better models faster". It supports hyper-parameter tuning, dataset and model versioning, as well as training monitoring, and provides the convenience of managing experiments in the cloud.
WandB is an end-to-end MLOps platform that simplifies the ML lifecycle and makes life easier for ML engineers by providing five main features.
- Experiments: Dashboard for tracking machine learning model experiments.
- Artifacts: Dataset versioning and model versioning.
- Tables: Used to log data to visualize and query with W&B.
- Sweeps: Optimized by automatically tuning hyper-parameters.
- Reports: Document your experiment and share it with collaborators.
The above five features allow you to collaborate with multiple people and manage projects efficiently. It also has the advantage of being highly scalable as it can be combined with multiple frameworks. In this article, I will introduce Experiments, which tracks the model training log and visualizes it on the Dashboard as you train the model. This allows you to quickly see if the training is going well.
How to choose the right MLOps platform
Each platform has its own pros and cons, so it's worth digging into the details before choosing the right one.
- Provide your own compute resources (Instances)
- Offer a wide range of features
- ~Autopilot - A service that automatically finds and trains different models for tabular data to derive the optimal model.
- ~Canvas - Services that make it easy to learn, visualize, and share models using the models you provide
- ~Clarify - Leverage explainable AI (XAI) techniques to easily understand how each attribute of your data influenced your results.
- ~Provide many other features at the no-code, low-code level for greater user-friendliness
- Well-written documentation
- ~Detailed documentation with many examples
- Easy integration with other services in AWS
- ~AWS has a dominant korea market share of about 70% (article). Because so many services rely on the AWS ecosystem, SageMaker is readily available and preferred because it has the highest compatibility compared to other MLOps services.
- MLOps full package including dataset building
- ~SageMaker Ground Truth even provides data building within the platform
- Understanding of AWS systems including EC2, S3, and ECR required
- ~If you're new to AWS, you'll need to familiarize yourself with other services in AWS like EC2, S3, ECR, and concepts like IAM.
- Relatively expensive
- ~Provides its own instances, but can be expensive. For example, for a p3.16xlarge instance with 8 NVIDIA V100s, they charge about 28 USD per hour (price list, note: if you buy your own server, the break-even point is about 3-4 months).
Google Kubeflow, Vertex AI
- Provide your own compute resources (GCP)
- ~More options for available computing resources than SageMaker
- Offer a wide range of features
- ~Vertex AI Matching Engine - Vector search service with massive scalability and low latency
- ~Vertex Explainable AI - Like SageMaker Clarify, enables model and data analytics through XAI technology
- ~Generative AI Studio - Out-of-the-box access to the latest generation models from Google, including PaLM 2, Imagen, Codey, and more, and easy to learn and deploy
- Open source Kubeflow, formerly managed by Google, is relatively familiar to existing MLOps users through the introduction of TFX
- Lower cost than SageMaker on average (Docs)
- Well-written documentation
- Small communication packet size limits when deploying models, making it difficult to handle high-resolution images or long audio files.
- There are underlying costs that are incurred even when not in use, so very light services can cost more than other platforms
- Open source library, not a platform, easy to learn, simple to use
- Clean visualization of information, including training settings and experiment results.
- No orchestration because you're not leveraging the cloud
- ~Orchestration means automatically adjusting and allocating the resources needed for a task, and automatically allocating new resources and configuring the environment when there is a problem with a resource, such as an abnormal shutdown of a cloud computer.
- Unable to diversify security settings
- ~Can't handle cases where you need to grant different access to different members, even in one project
- Requires additional computing resources
- High usability with a little extra code in Python
- Clean visualization of information, including training settings and experiment results.
- Provide service startup choices
- ~You can use the service in the cloud where it is provided (freemium), or you can start the service on your own computer.
- Limited use: Available for Python only
- Does not provide the ability to deploy models
Things to consider when adopting an MLOps platform
Initial data quality design
If you're considering adopting MLOps, you need to think about data quality early in your design. Here's why Why does data quality matter?
- Poor data quality negatively impacts a model's ability to make decisions. Additionally, issues that go unnoticed can lead to incorrect conclusions, and you can waste money and resources fixing them. Ultimately, stakeholder confidence can be adversely affected.
- Attempting to engineer around consumer-perceived data quality issues can be an underlying problem that wastes your team's time and can slowly erode productivity and morale.
This is because poor data quality can have a negative impact on both machine learning model development and operations teams.
Model data has a huge impact on the completeness of MLOps, so it's important to make sure you're doing everything from data quality to Be prepared. DataHunt can help you build high-quality MLOps by leveraging artificial intelligence throughout the machine learning lifecycle. This process starts with a meticulous assessment of data quality, analyzing the input data, and The principle is to be thorough in the design phase.
On average, 80% of the total time spent on an AI project is spent on data preparation, and 85% of all projects never make it to production. This is because the quantity or quality of the dataset on which the project is based is often sub-standard. DataHunt is a leading provider of data labeler jobs, including Data identification, Data aggregation, Data cleaning, Data Labeling, Data Augmentation and other lengthy We recognize the importance and do our best from the start.
How did DataHunt build high-quality data with 99% accuracy?
- Korea's first AI Assisted Annotation: Launched Korea's first HITL (Human in the loop) data processing service incorporating AI
- Deliver projects 2x faster than others with AI autolabeling: Automate AI-powered labeling to reduce project time by up to 2x.
- Industry-leading accuracy: Industry-leading 99%+ data accuracy made possible by AI technology, multiple verification processes, and a differentiated processing workforce
- Top-notch development and operations staff: Development staff composed of core talents from IT companies such as Kakao, Coupang, IBM, LG, etc. and operations staff experienced in B2G/B2B projects.
DataHunt has left more than 10 million data processing and building cases in the second year of service. He also participated in four NIA data building projects for AI learning in 2022. 4년 연속 data voucher provider and was honored as a 2020 Best Practice. Supporting multiple countries Experience in doing business has made us a company that grows every year.
Summary: When adopting an MLOps platform, there are important decisions to make about your initial data design.
- MLOps make it easier to build, deploy, and manage machine learning models, and can be a big win when you need to organize a stable framework.
- When analyzing globally competitive MLOps platforms, they all have in common the ability to centralize the entire workflow/process, reproducibility of results, and ease of deployment, monitoring, and testing.
- Getting the initial data right is critical to building a complete MLOps. Poor data quality undermines stakeholder confidence and wastes money and resources during model development and monitoring.