The real-time applications of MLOps that start-ups can use require a blend of the best practices
The industry standard for managing operations during an application’s development cycle is DevOps. Businesses must approach the ML lifecycle in a DevOps-like manner if they want to use real-time applications of MLOps to solve these problems. The term “MLOps” refers to this method. MLOps, or machine learning + operations, is an abbreviation.
It is a new field that requires a blend of best practices from software development, DevOps, data science, and machine learning. Optimizing model creation, deployment, and administration help to lessen conflict between IT operations teams and data scientists. Congnilytica predicts that by 2025, the market for applications of MLOps solutions would increase by over US$4 billion. Data preparation and cleaning for training purposes take up the majority of data scientists’ effort. The trained models must also undergo stability and accuracy tests. In this article, we’ll discuss the top real-time applications of MLOps.
1. Amazon SageMaker
Machine learning operations (MLOps) solutions are offered by Amazon SageMaker to assist users in automating and standardizing procedures across the ML lifecycle. It helps ML engineers and data scientists to work more efficiently by developing, evaluating, deploying, and managing ML models.
2. Azure Machine Learning
A cloud-based platform for data science and machine learning is called Azure Machine Learning Services. Users can operate machine learning workloads anywhere thanks to built-in governance, security, and compliance. Create precise models for classification, regression, time-series forecasting, computer vision, and natural language processing quickly.
3. Databricks MLflow
On top of Databricks’ open-source MLflow technology, Managed MLflow is created. The full machine learning lifecycle is managed by the users with enterprise dependability, security, and scale. Python, REST, R API, and Java API are all used by MLFLOW tracking to automatically log parameters, code versions, metrics, and artifacts with each run.
4. TensorFlow Extended
Google created the large-scale machine learning platform called TensorFlow Extended. It offers frameworks and common libraries for incorporating machine learning into the workflow. Users may coordinate machine learning workflows on a variety of platforms, such as Apache, Beam, and KubeFlow, using TensorFlow extended. TensorFlow is a sophisticated design for enhancing the TFX process, and it aids users in the analysis and validation of machine learning data.
An open-source initiative called MLFlow seeks to establish a standard language for machine learning. It serves as a management platform for the whole machine-learning lifecycle. It offers data science teams a whole solution. Users may simply manage models utilizing Hadoop, Spark, or Spark SQL clusters operating on Amazon Web Services in production or on-premises (AWS).
6. Google Cloud ML Engine
A managed service called Google Cloud ML Engine makes it simple to create, train, and use machine learning models. It offers a uniform interface for building, using, and keeping track of machine learning models. Users may prepare and save their datasets with the use of bigquery and cloud storage. The data may then be labeled using a built-in capability.
7. Data Version Control
Python-based DVC is an open-source data science and machine learning platform. It aims to make machine learning models replicable and shared. Large files, data sets, machine learning models, metrics, and code are all handled by it. DVC manages and links machine learning models, data sets, and intermediate files. archiving file contents on HDFS, Aliyun OSS, Amazon S3, Microsoft Azure Blob Storage, Google Cloud Storage, and other cloud storage services.
8. H2O Driverless AI
With just few clicks, you can create, train, and deploy machine learning models using the cloud-based machine learning platform H2O Driverless AI. The programming languages R, Python, and Scala are supported. Data from several sources, such as Hadoop HDFS, Amazon S3, and others, may be accessed by driverless AI.
The cloud-native platform for machine learning pipelines, training, and deployment is called Kubeflow. Kubernetes and Prometheus are a component of the Cloud Native Computing Foundation (CNCF), which it is a member of. By utilizing this tool, users may create their own MLOps stack using a variety of cloud service providers, such as Google Cloud or Amazon Web Services (AWS).
Netflix developed the Python-based framework Metaflow to aid data scientists and engineers in managing practical projects and boosting productivity. It offers a uniform API stack, which is necessary to carry out data science projects from the prototype stage to the production stage. Metaflow unifies Python-based Machine Learning, Amazon SageMaker, Deep Learning, and Big Data frameworks, enabling users to easily train, deploy, and maintain ML models.
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