MLOps for Better ML Pipelines in a Fragmented System

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MLOps for Better ML Pipelines in a Fragmented System Team
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Fragmentation is a major concern in this industry. Data scientists spend about 80% of their time managing data and preparing it for analysis. This is according to a Forbes survey. This time could be better spent actually creating better models and integrating them into production-ready applications. If this problem were to be solved, it would result in a massive boost for the industry. There aren’t enough MLOps tools out there that data scientists can rely on. Moreover, there is a huge problem with the data they are working with. This data is located all over the place. It is both structured and unstructured, and this can be a massive problem because of the time it takes to custom-tailor solutions to ingest all that data. You also have the fact that there are many laws governing what you can do with certain types of data. For example, in the financial industry, there are tight regulations concerning this. It is also the case with medical data. Building an ML pipeline this way can be difficult, time-consuming, or even outright impossible. Data scientists need a holistic platform that does everything they require in one place.

Problems with Our Current System

As mentioned above, there are many problems with the current way that data science and machine learning works. Data scientists spend too much time just gathering the data to make use of it. They need to get it from many sources, clean it, and then prepare it for analysis. In most cases, they even need to bring it into their own environments. There is also the huge problem of many different data handling tools and their formants. For example, storing data in Amazon S3 is a lot different than using Microsoft Azure. You need specialized tools just to move data from each source. Then, you would also need tools to export your Excel spreadsheets into a format that the data scientist can understand. This fragmentation is a major concern for the industry, as it means that data scientists are not as productive as they could be. The industry is also suffering from regulation. Data privacy is now a big push, and you can see that with the strict privacy laws that the European Union and California have both enacted. It is going to be more difficult for data scientists in this regulatory environment.

Creating an ML Pipeline That Works for Your Needs

The perfect MLOps pipeline is the solution to all of these problems. This will make the job of data scientists easier by putting everything they need in one place. There will be solutions for ingesting and preparing data, which is what Data scientists currently spend 80% of their time on. There is also be the ability to perform computations on the cloud, edge computing, or on an organization’s own infrastructure. This means there isn’t any worry about data privacy and security. If the data does need to travel, you can bring the compute to it, which will simplify the process. You also have to be the ability to perform data versioning and keep different types of data in the same place. All of this has to be done in real-time and accessible from all over the Internet. This is what MLOps platforms like promise for the industry.

How Can Help

The framework made available by leverages the latest ML and DL tools. It also helps with preparing models and includes Pachyderm-based data versioning, deployment using Kubernetes, Kubeflow and Spark-based ML, and DL build and deployment, Istio-based service mesh enabled microservice architecture, and ELK based monitoring capability; contributing to reduction in latency time.

About the Author Team
Enterprise AI/ML Application Lifecycle Management Platform

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