Enterprise Machine Learning Platform Challenges and its Use Cases

Introduction to Enterprise ML Platform

Machine learning is increasing its capabilities. As a result, the number of businesses using it is increasing daily. It is a data-driven approach. So it is more accurate than the traditional approaches. Nowadays, IT companies are turning to ml to solve customer problems. ML engineers, data scientists, and developers work together to yield results in this field. Here comes the question, how to manage this workflow? For managing these workflows, we need platforms.

What is an Enterprise Machine Learning Platform?

It is a set of tools and technologies with some standardized practices established by an organization. It supports and automates the ml workflow to reduce the workload. It also helps in increased collaboration between team members of different profiles. It makes the entire workflow easier. The above process is done on a large scale or an enterprise level, and then it is an enterprise machine learning platform.

Automate ML Workflow and enable the sequence data to be transformed and correlated together in a model to analyzed and achieve outputs. Click to explore about our, Machine Learning Pipeline Deployment and Architecture

What are the ML Platforms Challenges?

The below highlighted are the challenges of ml platform:

Wrong use case

Sometimes enterprises use ml for wrong use cases. They will select machine learning for non-machine learning problems or everyday problems. So the entire process fails and will not yield any result, leading to a loss of money and time.

Wrong data

Data is the oxygen for it. If the data given is biased or wrong. Then the entire model building fails. As a result, the process will fail as a whole. So the ultimate result is technical debt and a waste of time.

Technical Complexity

When adopting ml at an enterprise level, there is a problem of technical complexity, as we know that algorithms are built around advanced mathematical concepts. Not all businesses have high technical expertise. So proceeds can be more complex.

Lack of generalizability

It provides the output based on the data trained on it. So we cannot generalize the results. So we cannot apply them to another case. The result will be only for that particular case.

A process that enables the developers to write code and estimate the intended behavior of the application. Click to explore about our, Unit Testing of ML with Test Driven

What are the best Enterprise Machine Learning Platforms?

The below listed are the best enterprise ml platforms:

Data Robot

It is an enterprise AI machine learning platform. Jeremy Achin and Tom de Godoy developed the platform. It makes fast and easy maintenance, building, and deployment of AI. It includes automated ml, time series, and MLOps, independent and fully integrated tools. Its features are:

It helps in building innovative deep-learning models from different types of data.
It monitors models through a centralized dashboard.

Amazon Sagemaker

Amazon Sagemaker enables the building and training of its models. Amazon web services are the contributor. It is a software-as-a-service platform. It also helps in deployments. It provides tools for each step of the ml development cycle. It has the following features:

It provides an integrated Jupyter authoring notebook instance which makes the process integrated.
It provides a distributed environment for standard ml algorithms optimized to run efficiently.
It has the option to build our-own algorithms.

Akira AI

Akira AI is an enterprise decision science platform. Xenonstack, a stack innovator, is the developer of the platform. It enables automation of the infrastructure to train and deploy deep learning models on the public cloud and on-premises. It has the following features:

It provides end-to-end ml model monitoring.
It suggests actionable insights to solve ML model issues, creating confidence in model performance.
It reduces the risk of model failure.
It builds compliant and responsible AI systems with proactive bias monitoring capabilities.

Databricks

It is a unified analytics platform powered by Apache Spark. It covers data science, ml, analytics, and data engineering. It is available on multi-cloud. The platform offers automated and scalable CPU and GPU clusters. It allows rapid experimentation. It is pre-configured with a popular ml framework. Its features are:

It is optimized for performance.
It has reliable and performant data lakes.
It provides interactive data science as well as collaboration.

The procedure of evaluating the wellness of models performance against the real data. Click to explore about our, ML Model Validation Testing

Dataiku Data Science Studio

It provides a single platform for data science and its tasks. It focuses on multidisciplinary data science teams, collaboration, and ease of use. Dataiku is the sponsor. It significantly shortens the timing of load-clean-train-test-deploy cycles. It has the following features:

It caters to customers that need performance metrics that go beyond model accuracy.
It helps in the monitoring model to identify drifts for particular business benefits.

Google Cloud vertex AI

It is designed to help developers more easily build, deploy and scale machine learning models with pre-trained and custom tooling within a unified artificial intelligence platform. Google Research team is the developer of this platform. It has the following features:

It combines AutoML and AI Platform into a unified API, client library, and user interface.
It requires almost 80 percent fewer lines of code to train a model than its competitors.
It uses MLOps tools to manage data and models easily and repeat at scale.

H2O Driverless AI

It is an enterprise machine learning platform that leverages its tasks. H2O.ai is the contributor. It is an AutoML platform that provides a full suite of data science capabilities. It has the following features:

It provides automatic feature engineering.
It helps in model validation and tuning.
It provides a deployment service as well.

Microsoft Azure ML

It is an enterprise-level machine learning platform that helps to build and deploy models quickly. Microsoft has developed this platform. Azure ML provides end-to-end MLOps for creating and deploying models, leveraging automated workflows. It has the following features:

It has the jupyter notebook open document format,drag-and-drop functionality, and automated ml capabilities. We can say that it offers solutions for “all skill levels.”
It supports open-source technologies, including MLflow, Kubeflow,
TensorFlow, PyTorch, and Python.

Anaconda Enterprise

It is an enterprise-level machine-learning platform. Anaconda has developed the platform. It is an open-source Python and R-focused platform. The tool enables data science and ml in Linux, Windows, and Mac operating systems. It has the following capabilities:

It allows users to download more than 1500 python and R data science packages and manage libraries, dependencies, and environments.
It allows users to analyze data with Numpy, pandas, Dask, and Numba.
Users can visualize results generated with Matplotlib, Bokeh, Datashader, and many more.

Machine learning targets on the advancement of computer models that can admission datasets and use it train for themselves. Click to explore about our, ML Model Testing and Tools

What are the future trends of Enterprise ML Platform?

The future scope of enterprise ML platform described below:

Accurate results for web searches

When doing web searches, we are unaware of the quality of articles. Ranking and hierarchical order are done for the same. In the future, search engines will improves user as well as host experiences with the help of it. In the future, search engines will provide far better results.

Accurate Tailor-made customization

Nowadays, corporates are using it to understand their customers. It helps them in enhancing their product and service. They can use machine learning to analyze what products are more valuable to their customers. So adoption of it will allow them to design products with advanced features needed by the customer. These trends will continue as well.

Automated Self-learning systems

In addition to standardizing how people implement its algorithms, open-source frameworks such as TensorFlow, Pytorch, and Keras have eliminated the basic requirements for the same. We can create a zero-coding ecosystem in the future.

Enterprises execute activities on a large scale while also creating new business opportunities with ML. Download to explore the potential of Machine Learning

Conclusions

Nowadays, enterprises are adopting machine learning to run their business. Adopting these techniques is not an easy task. It needs a lot of effort. Here comes the need for ml platforms at an enterprise level. So this will make collaboration as well as doing tasks easily.