The AI landscape changes every week – with everything from Meta’s Code Llama release to OpenAI making ChatGPT available for enterprises. It’s almost impossible to keep up with all the new AI services. In between the time we wrote and published this article, OpenAI just announced that ChatGPT can now see, hear, and talk.
It's even harder to figure out how to integrate these technologies at scale. Some big challenges exist in adopting AI for enterprises – the biggest being data quality and hiring AI experts.
We’re currently building AI apps for our partners and advising on AI technology investments for 2024. Whether you’re just trying to solve for thousands of employees using ChatGPT securely or need to automate machine learning models in AWS, Azure, or Google Cloud – there’s an enterprise solution available.
Here’s some of the technology we’re currently using, the challenges that companies are facing, and how you can hire a team of experts to start integrating enterprise AI solutions in four to six weeks.
Top enterprise AI services to consider in 2023
ChatGPT enterprise: ChatGPT Enterprise is a substantial upgrade from the standard ChatGPT model. It offers enterprise-grade security and privacy, unlimited higher-speed GPT-4 access, longer context windows for processing more extended inputs, advanced data analysis capabilities, customization options, and much more.
Your employees are already using ChatGPT or something like it. Using an enterprise-grade version is critical for security and maximum gains in efficiency.
Key features of ChatGPT Enterprise:
- Enterprise-grade security and privacy: You own and control your business data in ChatGPT Enterprise. OpenAI does not train on your business data or conversations, and their models don’t learn from your usage. All conversations are encrypted in transit and at rest2.
- Unlimited, fast GPT-4 access: ChatGPT Enterprise removes all usage caps and performs up to two times faster.
- Longer context windows: It includes 32k context in Enterprise, allowing users to process four times longer inputs or files.
- Advanced data analysis capabilities: This feature enables technical and non-technical teams to analyze information in seconds.
- Customization options: It provides enterprise-specific features designed to meet the unique demands of businesses.
- Admin controls: The new admin console lets you manage team members easily and offers domain verification, SSO, and usage insights, allowing for large-scale deployment into an enterprise.
Amazon AWS SageMaker: Amazon SageMaker is a cloud-based platform that can be used to build, train, and deploy machine learning models. It provides various tools and resources, including pre-trained models, Jupyter notebooks, and automated machine learning features.
- Fully managed service: SageMaker is a fully managed service that enables developers and data scientists to build, train, and deploy machine learning models quickly.
- Jupyter notebooks: It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis.
- Enterprise-readiness and security: Sagemaker integrates with the AWS cloud platform to give you secure ML capabilities in the cloud.
- Easy deployment: You can deploy a model into a secure and scalable environment by launching it with a few clicks from the SageMaker console.
- Automated machine learning: Amazon SageMaker Autopilot simplifies the machine learning experience by automating machine learning tasks.
- Integration with Hugging Face: For text summarization, you can use Amazon SageMaker with Hugging Face, one of the most downloaded pre-trained models. This allows you to implement text summarization within a Jupyter notebook using Amazon SageMaker and the SageMaker Hugging Face Inference Toolkit.
Microsoft Azure Machine Learning: Microsoft Azure Machine Learning is a suite of tools that can be used to build, train, and deploy machine learning models. It provides a variety of pre-trained models, as well as tools for creating custom models.
- Fully managed service: Azure Machine Learning is a cloud service that helps manage the machine learning project lifecycle.
- Model training and deployment: You can create a model in Azure Machine Learning or use a model built from an open-source platform, such as Pytorch, TensorFlow, or scikit-learn.
- MLOps management: MLOps tools help you monitor, retrain, and redeploy models.
Enterprise-readiness and security: Azure Machine Learning integrates with the Azure cloud platform to add security to ML.
- Productivity tools: Azure Machine Learning has tools that help you develop models for fairness and explainability, tracking and auditability to fulfill lineage and audit compliance requirements.
- Deployment at scale: Deploy ML models quickly and easily, and manage and govern them efficiently with MLOps.
- Cross-compatible platform tools: Anyone on an ML team can use their preferred tools to get the job done. Whether you’re running rapid experiments, hyperparameter-tuning, building pipelines, or managing inferences, you can use familiar interfaces – including Azure Machine Learning studio Python SDK (v2), CLI (v2)), and Azure Resource Manager REST APIs.
Google Cloud AutoML: Google Cloud AutoML is a suite of tools that can be used to build and deploy machine learning models without any coding experience. It provides pre-trained models for common tasks and tools for creating custom models that power your AI capabilities.
- Fully managed service: AutoML is a fully managed service that allows developers to quickly build, train, and deploy machine learning models.
- Integrated Jupyter Notebooks: AutoML provides an integrated Jupyter notebook for easy access to data sources for exploration and analysis.
- Enterprise-readiness and security: AutoML integrates with the Google Cloud platform to provide secure ML capabilities in the cloud.
- Easy deployment: Models can be deployed into a secure and scalable environment with just a few clicks from the AutoML console.
- Automated machine learning: AutoML simplifies the machine learning experience by automating machine learning tasks.
- Integration with TensorFlow: For tasks like text summarization, you can use Google Cloud AutoML with TensorFlow – one of the most popular open-source libraries for machine learning. This allows you to implement text summarization within a Jupyter notebook using Google Cloud AutoML and TensorFlow.
Salesforce Einstein: Salesforce Einstein is a suite of AI-powered tools that can automate tasks, improve decision-making, and personalize customer experiences. It includes various features, such as predictive analytics, natural language processing, and machine learning.
- Predictive insights: Salesforce Einstein provides AI-powered analytics that can predict customers' behavior–helping you make proactive decisions.
- Automated workflows: It automates routine tasks, freeing time for more strategic activities.
Personalized customer experiences: Einstein can personalize customer interactions based on their behavior and preferences.
- Integrated AI across Salesforce clouds: Einstein is integrated into every Salesforce cloud – providing AI capabilities across sales, service, and marketing.
- Data-driven decisions: With Einstein, your business can make decisions based on data, not hunches.
- Security and compliance: As part of the Salesforce platform, Einstein adheres to the strict security and compliance standards of Salesforce.
The four biggest AI enterprise systems challenges
With all these new technologies come significant challenges – some very familiar. For starters, your data quality will determine how effective your AI capabilities can be. Companies are running into big problems with managing compute resources, and there are some new technical challenges your DevOps teams need to solve.
1. Data – Big data, DataOps, data management – whatever you call it at your company – is the biggest challenge regarding AI capabilities. If your data is mismanaged or low quality, AI capabilities will be out of reach until you work through the problem.
- Data quality: Incomplete, inconsistent, or noisy data can lead to poor AI performance.
- Data volume: Storing and processing large amounts of data require significant resources.
- Data variety: Handling different data types, from structured to unstructured, poses integration challenges.
- Security and privacy: Compliance with data protection laws and safeguarding against breaches are critical.
- Ethical considerations: Data biases can lead to ethically problematic AI outcomes.
- Data governance: Ensuring data ownership, quality checks, and proper usage is complex.
- Costs: Data collection, preparation, and maintenance can be expensive.
- Expertise: Specialized expertise is often required for effective data management.
2. Compute resources – High-powered computing environments are often needed to train, test, and deploy AI models, especially those based on complex algorithms like deep learning.
- High costs: Specialized hardware like GPUs and TPUs, as well as cloud services, are expensive.
- Scalability: As AI models and data grow, the computational power needed also scales, requiring a thoughtful approach to resource allocation.
- Energy consumption: AI computations can be energy-intensive, which has financial and environmental implications.
- Software and hardware compatibility: Ensuring that AI algorithms run efficiently on available hardware is critical, as is keeping software libraries and dependencies up-to-date.
- Latency: Low-latency computational resources are crucial for applications requiring real-time processing, like autonomous vehicles or high-frequency trading.
- Expertise: Managing and optimizing computational resources requires specialized technical skills.
3. Vector database management – AI capabilities, especially ones based on LLMs, require advanced skills in vector database management. Vector databases are specialized databases optimized for handling vector data, often used in machine learning, GIS (Geographical Information Systems), and similar applications involving spatial or high-dimensional data.
- Performance optimization: Developers need to know how to fine-tune queries and indexing strategies to get the most out of these specialized databases.
- Data ingestion and preprocessing: Handling large volumes of high-dimensional data is complex, particularly regarding data cleaning, transformation, and import.
- Scalability: As with other databases, vector databases need to be designed with scalability, which may involve knowledge of distributed systems.
- Hardware requirements: Some vector databases may be optimized for specialized hardware, which can introduce added layers of complexity and cost.
- Interoperability: Integration with existing systems can be challenging, particularly when transitioning from a different database architecture.
- Security: As with any database, ensuring data security, including access controls and encryption, is crucial.
- Expertise: Vector databases often require a different skill set compared to traditional relational databases. Understanding how to model and query data efficiently can be challenging.
4. AI systems integration – Integrating AI systems into existing company infrastructure is a multi-layered challenge, often requiring coordination across different departments and skill sets. The successful incorporation of AI isn't just about developing or acquiring an AI model – it's about making that model a seamless part of an operational process.
- System compatibility: AI models may need to interact with legacy systems, databases, or third-party software. Ensuring compatibility between enterprise systems is a huge challenge.
- Data pipelines: For AI systems to work, data must flow seamlessly from source to model to output. Creating these pipelines is often more complicated.
- Latency and throughput: AI systems can have specific performance requirements. Ensuring the system can handle the required load with low latency is crucial.
- Monitoring and maintenance: AI systems must be continuously monitored for performance and accuracy, requiring new tools and expertise.
- Governance and compliance: Integrating AI requires navigating a labyrinth of regulations, especially for industries like healthcare or finance.
- Human-in-the-loop systems: In many cases, AI systems need to work in conjunction with human experts. Creating a user interface and experience that facilitates this collaboration is challenging.
- Cost and ROI: Integration costs can be high, and it may be challenging to demonstrate a clear return on investment in the short term. Especially when you don’t have the product experts who’ve done the work before to estimate costs accurately.
The biggest common thread between these challenges is having skilled senior software engineers, data engineers, systems architects, cloud experts, and experienced AI product teams to build and integrate new enterprise AI services.
It could take 6-18 months to build an AI product team capable of integrating AI systems at your company, but you need to have these AI capabilities in that amount of time or less to remain competitive. That’s why Codingscape exists.
How do I hire an AI systems integration team?
We can assemble a senior AI systems integration team for you in 4-6 weeks. It’ll be faster to get started, more cost-efficient than internal hiring, and we’ll deliver high-quality results quickly. We’re already building AI capabilities for our partners in 2023 and advising on where to invest in their AI roadmaps for 2024.
Zappos, Twilio, and Veho are just a few companies that trust us to build their software and systems with a remote-first approach. We know enterprise AI systems at scale and love to help companies start using the latest developments in AI, solve hard problems efficiently, and get a competitive advantage.
You can schedule a time to talk with us here. No hassle, no expectations, just answers.
new content is published
Cole is Codingscape's Content Marketing Strategist & Copywriter.