Machine Learning is undoubtedly the most widely used technology. It is presently used in almost every thinkable field which has pushed its significance infinitely.
Machine learning, as we all know, is the process of using past data to make the system learn, and it makes future predictions of its own. So where does AutoML have this picture in it?
AutoML essentially involves the automation of the edge process of obtaining machine learning to real-world problems that are actually relevant to the sector. In recent times, it has been realized and demonstrated over and over again that the future of energy is ML or machine learning.
It’s indeed obvious that it’s a near technology that enables research, analysis, and implementation in various directions.
The idea came into effect because. it is a time-consuming task to apply traditional machine learning methods to real-world solutions. We need expertise and experience from experts and from distinct areas and make this task work. Well, AutoML comes into play, making it very easy for experts to build and implement real-world machine learning models.
How AutoML simplifies work for ML engineers or data scientists?
“AutoML is not intend to replace Data Scientist. but AutoML is intend to release data scientists from the burden of repetitive and time-consuming tasks such as model selection and parameter tuning as well as the creation of data set intuition.”
- This AI and AutoML technology can be in use easily by many companies from all sectors such as banking, finance, marketing, and healthcare of any size.
- To help select the best deep neural network architecture and hyper-parameter tuning, AutoML can be generalized.
- With the help of AutoML, as data scientists, experts in this field can now focus more on issues that matter most. rather than cleaning the data, training the models, or any other tasks.
List of tasks AutoML can perform :
- Pass the information to AutoML and it should tell us the relationship between each variable and the target variable.
- Feature engineering and Feature selection
- Model selection: Choosing the model that works best for your problem, which is already there in all the AutoML platforms.
- Data formatting is very difficult, but it also converts data frames to a sparse matrix like one encoding
Some AutoML tools that are being use in industries are H2O, TPOT, Google’s AutoML, DataRobot, Amazon SageMaker, AutoKeras, and AutoSklearn.
What is the overhype behind having MLOps in place?
MLOps is communication among data scientists and the production team or operations. It is deeply collaborative in nature, design to eliminate waste, automate and generate machine learning with smarter, more consistent insights. For an organization, ML can become a game-changer.
but it can develop into a science experiment without some form of universal applicability.
MLOps brings company interest back to the forefront of your activities in ML. With clear direction and measurable benchmarks, data scientists work through the lens of organizational interest. It’s the best in the two worlds. This gives you a single place.
regardless of how they were created or when and where they were deployed, to deploy, track, manage, and regulate all your models in production. Using advanced automated machine learning health monitoring, MLOps enhances the overall quality of your models.
What is the value addition of having an MLOps :
- Build and Run Your Models Anywhere: You can deploy any model to your manufacturing environment of choice with MLOps. Any cloud platform, on-premise data center, or hybrid environment can be this. You can add in-place monitoring to any existing production model already deployed through the instrumentation of MLOps monitoring agents.
- Automated Model Health Monitoring and Lifecycle Management: To enhance the performance of your existing models, MLOps provides constant monitoring and production diagnostics.
In order to explain why your model is degrading, automated best practices allow you to track service health, accuracy and data drift.
- Embedded administration, humility, and fairness: MLOps creates a framework in which you can maintain discipline and control over your AI in your organization.
Conclusion:
Just like the way ML has evolved from research to applied business solutions.
we must enhance the maturity of its processes of operation. Hence, That’s why we have AutoML in place to reduce the workloads of doing menial or tedious tasks.
by data scientists which is undoubtedly time-consuming as the size of the data increases. just like that we have MLOps in place to monitor the life cycle of ML models.
Written by: Deepti Sardar
Reviewed By: Vikas Bhardwaj
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