Businesses have started adapting MLOps as part of their operations. Startups need to quickly grow their operations and the MLOps platform guarantees accelerated growth for startups by providing ML models that impact the business sales and help make forecasts, and also gather data that help in business analysis, it also gives the startup an edge over existing competition. ML models built with MLOps platforms are reliable and accurate.
This article discusses MLOps platforms and the advantages of a startup using these MLOps platforms.
What is MLOps?
Machine learning Operations (MLOps) is a process that involves the collaboration of data scientists, machine learning engineers, and businesses for the production of machine learning models which are then deployed. MLOps covers the machine learning lifecycle from data collection to model monitoring and model management. MLOps can also be integrated with DevOps to ensure continuous integration, deployment, and monitoring.
It also provides an end-to-end pipeline for building and deploying reliable machine learning models.
Phases of MLOps life cycle
Team integration and Business requirements: The required team needs to be set up which consists of the data scientist, machine learning engineers, and operation managers.
Data collection, analysis, and preprocessing: This involves the gathering of data that is needed to build the machine learning models, most times this data is usually raw and unclean, so preprocessing techniques like handling of missing values, outlier detection, and encoding needs to be carried out So that the data can be analyzed and concept models can be built.
Model training, evaluation & deployment: This is the stage where the machine learning model is trained from the data collected, ML algorithms like regression, deep learning, Naïve Bayes, decision trees, etc. are applied to the data to analyze its pertain and learn from it. The ML model is stored In a model hub, the ML model is then evaluated and tested on the test data before deployment.
MLOps pipeline (Continuous integration, monitoring, and deployment): After this ML model is built, then a pipeline is used to ensure continuous integration, monitoring of the modelops, and deployment of the ML models. This will help ensure that it performs well in production.
What are MLOps platforms and their examples?
MLOps platforms are frameworks or tools that help manage the machine learning lifecycle and provide infrastructure and processes for the successful development of ML models at scale.
Key features of a MLOps platform
MLOps lifecycle management: MLOps platform must provide facilities for maintaining and managing the MLOps lifecycle that ensures that the end-to-end pipeline is covered. It should also include the required people and processes.
Provision of continuous integration and deployment capabilities: It is very important that an MLOps platform provides continuous integration and deployment for the machine learning models, this ensures the ML model performs efficiently.
Data management and model versioning and tuning: MLOps platform should have data storage mediums like databases in which data can be retrieved from and then preprocessed and analyzed to build the ML model. During the training of the ML models, the MLOps platform should also provide versioning, which helps to save ML models in model hubs after training. MLOps platform should also have model hyperparameters tuning capabilities to produce an accurate model.
ML model monitoring after deployment: This is a very important feature of MLOps platforms because it must provide a way to monitor the machine learning models after deployments, this helps ensure that they perform flawlessly in production.
Some examples of mlops platform
Algorithmia: It is an MLOps platform that helps to secure ML model and ML model health, and also helps organizations develop a path to scalable machine learning operations.
Datarobot: It is an MLOps mostly known for automation of end-to-end ML models and data science operations.
H20.ai: It is an MLOps platform mostly used for model monitoring.
Kubeflow: serves as an MLOps platform for automating the deployment of ML models in Kubernetes.
Valohai: it is an MLOps platform that helps manage the ML lifecycle. It helps train, evaluate, and deploy ML models.
Verta ai: it is an MLOps platform mainly used in model management, and high-velocity data science and to support machine learning teams.
Other examples of MLOps platforms are Allegro.io, polyaxon, MLflow , Metaflow, Dataiku.
Why a startup should use the MLOPs platform
MLOps provides many benefits to startups. Some of these benefits are discussed below.
1. MLOps platforms improve collaboration and communication in the startup business
MLOps platforms can help improve the communication between data science team members and the business employees which in turn helps the startup attain its desired business goals. This also helps to create an efficient and flexible working environment for different teams in the startup. MLOps platforms help to clarify the responsibilities of team members thereby driving the business forward and enabling an efficient workflow also resulting in increased performance of teams
2. It helps to drive business growth by offering state-of-the-art ML models.
MLOps platforms serve as a suitable tool to build machine learning models which are used to drive startup business growth, for example, an ML model can be developed to help predict the number of customers who are likely to buy a product making the company take accurate decisions thereby increasing business growth.
3. Can give the startup an edge by helping with business forecasts, analysis, and predictions.
MLOps help startup businesses acquire data that can be analyzed and used for a business forecast like determining the number of sales a startup will have in the next two years, also this data can be analyzed trends in data.
4. Can help accelerate the business startup operations and sales
MLOps platform can be used to build recommender engines which are then used to display likely products that a customer is interested in, which can help accelerate sales. Also, MLOps platforms facilitate the operations of the business like planning and administration.
5. It can help the startup business identify target customers
MLOps platforms also help build unsupervised machine learning models which can be used for customer segmentation. These ML models help group similar customers together which enables the business to easily identify its target customer. This then helps to predict the conversion rate of the identified customer.
MLOps platforms are a new technology trend that has rapidly been adopted by many business startups. It offers a reliable and well-structured way of using ML for business, this then helps build efficient ML models which drive business growth.
This post discussed MLOps platforms in detail and the various reasons why a startup should use MLOps platform. It is guaranteed that using MLOps platform will generate positive results for business startups.
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