Enterprise AI focuses on integrating advanced AI and ML capabilities into existing business processes to optimize and streamline workflows, make use of vast data repositories, and help enterprises solve their unique business challenges. There are many enterprise and ML platforms available on the market to help enterprises train existing machine learning models and build custom AI solutions.
While the applications of AI can vary quite significantly in the enterprise environment, in this article we’ll focus on some key use cases of enterprise AI tools and share 10 of the best solutions available on the market today, so you can begin to harness this powerful technology in 2023.
Why should enterprises consider enterprise AI applications?
AI and machine learning platforms, particularly those crafted for enterprise-scale use, deliver substantial advantages that can considerably elevate business performance and help maintain competitiveness in the future market landscape. With enterprise AI platforms, teams can:
- Accelerate digital transformation: Enterprises can use these AI and ML tools to optimize their business processes, enhance customer experiences, and drive innovation. These tools can enable teams to quickly build, deploy, and operate AI applications at scale, thus accelerating their digital transformation journey.
- Streamline ML lifecycle: With enterprise software tools, enterprises can simplify and automate the machine learning lifecycle and manage everything from data ingestion, labeling, and feature engineering to model training, tuning, deployment, and monitoring.
- Leverage pre-trained AI models: Enterprise AI solutions often come with pre-trained models and features, such as natural language processing, that can help enterprises quickly get started with AI and ML without requiring vast amounts of cleaned and pre-processed data.
- Gain insights from vast datasets: Enterprises can use AI enabled solutions to gain insights from their unstructured data, such as images, videos, text, and audio. Through techniques such as object detection, face recognition, and visual search, they can transform their unstructured data into valuable knowledge and insights that can solve business challenges.
Top 10 Enterprise AI & Machine Learning Platforms
Enterprise AI platform can be a significant investment of time and resources to gather, clean and curate appropriate training data, customize the solution, and integrate it into complex enterprise workflows. To make this investment worth it, you need to choose the right AI software solution. And to help you begin your search, we’ve put together a list of the top 10 enterprise AI applications.
H2O.ai
Open source, distributed in-memory machine learning and enterprise AI platform.
What you need to know:
- Works with customers, partners, and communities to co-innovate and democratize AI
- Can deploy models into production with Java (POJO) and binary formats (MOJO), and integrate with various frameworks and platforms.
- Can work on existing big data infrastructure, such as Hadoop, Spark, or Kubernetes, and ingest data from various sources into its in-memory distributed key-value store.
C3.ai
Leading enterprise AI software provider for accelerating digital transformation.
What you need to know:
- Offers a comprehensive platform and a large and growing family of turnkey enterprise AI applications
- Enables customers to rapidly build, deploy, and operate AI applications at scale
- Has been recognized as a leader in AI and ML platforms by Forrester and IDC
Amazon SageMaker
Fully managed machine learning service that enables data scientists and developers to build, train, and deploy custom machine learning models in a scalable and secure environment.
What you need to know:
- Supports all major ML frameworks, such as TensorFlow, PyTorch, MXNet, and XGBoost, and allows users to bring their own algorithms and frameworks
- Provides a range of tools and features to simplify and automate the machine learning lifecycle, such as data labeling, feature engineering, model tuning, debugging, monitoring, and explainability
- Integrates with other AWS services, such as S3, Glue, Athena, Redshift, Kinesis, Lambda, and SageMaker Studio
TensorFlow Enterprise
End-to-end machine learning platform to help teams build their own machine learning models, deploy ML models, and implement ML ops.
What you need to know:
- Offers multiple data tools to help prepare, load, preprocess, and validate data at scale, as well as responsible AI tools to uncover and eliminate bias in data and models
- Supports distributed training, immediate model iteration, and easy debugging with Keras
- Provides tools to track development and improvement through the model's lifecycle
- Provides collections of pre-trained models at TensorFlow Hub and Model Garden that can be fine-tuned or customized for new tasks and data
- Enables deployment of models on any environment, such as servers, edge devices, browsers, mobile, microcontrollers, CPUs, GPUs, FPGAs, and TPUs, and helps implement best practices for MLOps for production ML
Google’s Vertex AI
Cloud-based machine learning platform developed by Google Cloud that provides an end-to-end workflow for building, training, and deploying machine learning models.
What you need to know:
- Combines data engineering, data science, and ML engineering workflows, enabling teams to collaborate using a common toolset
- Provides several options for model training, including AutoML for tabular, image, text, or video data without writing code, and custom training for using preferred ML frameworks and writing own training code
- Offers end-to-end MLOps tools to automate and scale projects throughout the ML lifecycle, such as Vertex AI Pipelines, Vertex AI Feature Store, Vertex AI Vizier, and Vertex AI Experiments
- Runs on fully-managed infrastructure that can be customized based on performance and budget needs
Dataloop
Dataloop is a platform for building and managing computer vision applications.
What you need to know:
- Provides a unified interface for data annotation, model training, deployment and monitoring
- Supports various types of annotations, such as bounding boxes, polygons, keypoints and semantic segmentation
- Integrates with popular frameworks and tools, such as TensorFlow, PyTorch, OpenCV and AWS
- Offers a flexible pricing model based on usage and data volume
DataRobot
Enterprise AI platform that enables teams to build and deploy AI models with ease and efficiency.
What you need to know:
- Offers both code-first and no-code options for ML experimentation, allowing users to iterate and experiment with different problem framings and modeling strategies.
- Provides a centralized system for validating, governing, integrating, monitoring, and measuring AI models, regardless of where they are built or deployed.
- Supports a broad ecosystem of data platforms, APIs, services, business apps, and deployment infrastructure, enabling users to maximize their existing technology investments
- Has a world-class team of AI experts and service partners who can guide and assist users with their AI projects and advanced artificial intelligence techniques
Gathr
Cloud-based platform that simplifies data integration and engineering for machine learning and data science projects.
What you need to know:
- Supports various data sources and formats, such as databases, files, APIs, webhooks, etc.
- Enables ETL, ELT, CDC, streaming analytics, data preparation, machine learning, and advanced analytics workflows with a drag-and-drop interface and code-free options.
- Provides a scalable and secure cloud infrastructure that handles data ingestion, processing, storage, and delivery.
- Offers a collaborative environment for data teams to share, review, and monitor their data pipelines.
IBM Watson
Cloud-based platform that empowers data scientists, developers and analysts to build, run and manage AI models, and optimize decisions across different environments.
What you need to know:
- Supports a wide range of data sources and open source frameworks like PyTorch, TensorFlow and scikit-learn
- Provides a collaborative environment with tools for code-based and visual data science, as well as automated machine learning and model monitoring
- Offers flexible deployment options on IBM Cloud Pak for Data, IBM Cloud Pak for Data as a Service, or other public clouds
- Enables AI governance and risk management with features like explainable AI and automated validation
Clarifai
Leading artificial intelligence software that helps enterprises gain insights from their unstructured data, such as images, videos, text and audio.
What you need to know:
- Offers a range of products and services for different stages of the AI lifecycle, from data labeling to model deployment
- Has a rich repository of pre-trained AI models that can be used or fine-tuned for various tasks, such as object detection, face recognition, visual search and more
- Provides an intuitive user interface and a free API for developers and data scientists to build AI applications easily and quickly
What teams can benefit from enterprise AI software?
Enterprise AI software can enable innovation across a variety of teams in an organization due to its diverse applications and far-reaching impact. Here are some specific areas where these AI enterprise software can deliver tangible value:
- Human resources: Custom AI based systems can streamline operations like resume screening and scheduling interviews, freeing up time for HR personnel to focus on strategic tasks. It can also support more sophisticated functions such as predictive attrition and talent analytics.
- Supply chain management: AI can enhance forecasting accuracy, optimize inventory management, alert for predictive maintenance of machinery, improve logistics planning, and help in detecting and mitigating supply chain risks.
- Business intelligence: A BI tool could be built to act as a proactive AI assistant for leadership and BI teams, simplifying the process of analyzing large datasets, making data-driven decisions, and predicting future trends.
- Sales and marketing: AI can enable these teams to personalize customer interactions, predict customer behavior, automate lead generation, and optimize marketing campaigns.
- Customer service: AI can help in automating responses to common queries through chatbots, analyze customer feedback for sentiment analysis, and provide personalized recommendations to customers.
What can enterprise AI solutions be used for?
As we have seen, enterprise AI applications can be used across industries, functions, and teams, but now let’s dig in a little deeper to some specific applications:
- Predictive analytics: Leverage AI and machine learning platforms to harness your enterprise's data for the creation of predictive models. This fosters proactive decision-making and facilitates strategic future planning.
- Business process optimization: Utilize these platforms to automate repetitive tasks and processes across your organization. This enhances operational efficiency and productivity, ultimately driving cost savings.
- Customer experience enhancement: Apply AI-powered analytics to gain deep insights into customer behavior. This allows you to personalize customer experiences, thereby boosting customer satisfaction and loyalty.
- Machine learning lifecycle management: Implement comprehensive platforms to manage the entire lifecycle of machine learning projects. This ranges from the initial stages of building and training models to their deployment and maintenance in production environments.
- Data integration and engineering: Simplify your data integration and engineering processes. These platforms ease the gathering, cleaning, and preparation of data for subsequent machine learning and data science initiatives.
- Fraud detection: Financial institutions can build machine learning models to identify anomalous transaction patterns, helping to detect and prevent potential fraudulent activity.
Conclusion
Harnessing the power of enterprise AI and machine learning platforms can provide businesses with a substantial competitive edge in today's fast-paced, data-driven world. With their ability to accelerate digital transformation, streamline machine learning life cycles, and provide valuable insights from vast data repositories, these tools are indispensable for businesses aiming to innovate and grow.
Our AI development company can work with you to put together a comprehensive AI strategy and help you leverage the power of AI to build custom software solutions that fit your enterprise’s unique requirements. Don’t hesitate to reach out today.