In an era where data security and privacy have become paramount, many enterprise companies are seriously reevaluating their reliance on third-party Artificial Intelligence (AI) tools and platforms. While externally sourced tools can offer convenience and rapid deployment, they often come with inherent risks, including potential data leaks, lack of control over data processing, and vulnerabilities to external threats. Therefore, we’re beginning to experience a discernible shift towards the in-house development of generative AI tools among these enterprises. Building proprietary AI solutions provides companies with a dual advantage: a tailored tool that meets specific needs and a more controlled environment ensuring data remains protected and confined within the enterprise’s own security protocols. 

Top AI Modeling Tools 

The global AI market’s limited transparency worries 75% of CEOs.   

The concept of building AI tools from scratch might seem daunting to the uninitiated. However, with the rapid advancements in AI research and open-source frameworks, developing custom AI models has become significantly more accessible. For companies that invest in attracting skilled AI practitioners and equipping them with the necessary resources, the journey from concept to deployment can be streamlined and efficient. Skilled individuals, familiar with the intricacies of deep learning, neural architectures, and data pipelines, can leverage existing libraries and platforms, adapting them to the company’s specific requirements. Thus, with the right team and strategic investment, enterprises can not only ensure data security but also harness the full potential of AI tailored to their unique needs. 

Some of the top AI Model Development tools for 2023 are starting to catch up to what ChatGPT, Bard and others can offer, while ensuring the continued protection of critical data for the business: 

 Trusted Data Sources for AI 

Your private information protection by AI is doubted by 52% of consumers. 

Private companies are at the forefront of leveraging a vast expanse of data sources to fuel their AI modeling pursuits and this critical data is much of the reason they are driven to in-house AI solutions. Renowned databases like Oracle and SQL Server serve as reservoirs of structured data, often containing years of transactional, customer, and operational information. Web data, gathered through web scraping and APIs, offers insights into user behaviors, market trends, and real-time feedback. Publicly available datasets provide benchmarks, research foundations, and often, vast volumes of information that can be combined with their proprietary data. Flat files, such as CSVs or Excel sheets, while rudimentary, often contain crucial data extracts or manual entries. Data lakes, vast repositories designed for big data and real-time analytics, encapsulate structured and unstructured data at scale. Lastly, the open-source realm offers a treasure trove of datasets, tools, and libraries, invaluable for Large Language Models (LLMs) and other AI endeavors. 

However, with this wealth of data comes an inherent responsibility to safeguard it from potential breaches and misuse. Advanced relational database security mechanisms, such as row-level security, encryption (both in-transit and at-rest), and regular vulnerability assessments, can ensure that the structured data within Oracle or SQL Server remains protected. Fine-grained access controls can be implemented to ensure that only authorized personnel can access specific datasets. Data lakes, given their vastness, necessitate robust access control layers, monitoring tools, and encryption protocols. Network security further fortifies this defense layer. Intrusion detection systems (IDS) and intrusion prevention systems (IPS) can monitor and block malicious traffic. Virtual Private Networks (VPNs) ensure encrypted connections for remote access, while firewalls regulate incoming and outgoing traffic based on predetermined security policies. Together, by integrating advanced database security with comprehensive network security measures, companies can ensure the sanctity of their data, creating a reliable foundation for their AI initiatives. 

AI Model Training Tools 

Nearly 25% of business owners are concerned about the impact of AI on website traffic. 

Once this data is pulled into a LLM from all these data sources and data sets to a developed AI Modeler tool, continual training of large data models in AI will be pivotal for several reasons. As the digital world evolves, the data landscape is in perpetual flux, marked by emerging patterns, changing behaviors, and novel scenarios of both internal, as well as additional, external datasets. By consistently training AI models on new and expansive datasets, we ensure that these models remain relevant, accurate, and effective in their predictions and analyses. Furthermore, regular training combats model stagnation and obsolescence, ensuring that the AI’s understanding of the world is not confined to internal relational data and object data stores. Moreover, as computational capabilities grow and algorithms become more sophisticated, there’s an inherent opportunity to harness this power by training larger models, leading to improved performance and generalization. In essence, continued large data model training is the linchpin that drives AI evolution, guaranteeing that systems remain adaptive, robust, and attuned to the ever-shifting nuances of the global digital landscape. 

Two of the AI Modeling products to watch this year are MosaicML and Synthesia: 

Understanding Weights and Biases 

As of 2023, 63% of individuals are concerned about the potential bias and inaccuracies in AI-generated content. 

Weights and Biases (often abbreviated as W&B) is a platform designed for machine learning experiment tracking, visualization, and collaboration. While the term “weights and biases” in the context of neural networks refers to the parameters that are learned during the training process, W&B as a tool aid in the optimization, monitoring, and understanding of these parameters and more.  

For large language models (LLMs) like enterprise GPT variants and other massive neural architectures, W&B can be particularly useful in several ways, such as experiment tracking, visualization around weight values/bias, reproducibility, collaboration and creating model artifacts if one needs to roll back to a previous state when things go wrong. 

For W&B solutions to seamlessly integrate with a multitude of machine learning frameworks, makes it a versatile choice regardless of the specific tools or libraries one might use for LLMs. 

Although W&B doesn’t directly “impact” the AI data of LLMs, it provides researchers with a comprehensive suite of tools to better train, understand, and optimize these models, ensuring that they achieve the best possible performance and that the research process is transparent and reproducible.  Making sure there is less bias involved in your LLMs is essential to the quality of the AI used by the company in the end.  If you’d like to learn more about W&B, check out their website. 

Data Labelers for AI 

Nearly half of consumers question the safety of automation in healthcare with AI. 

Once in-house AI systems, especially Large Language Models (LLMs), have undergone rigorous training and retraining cycles, and the performance metrics have been meticulously tracked and optimized using tools like Weights & Biases (W&B), a crucial subsequent step is data labeling. While initial training often relies on labeled data, the output data generated by these models as they operate can be vast and varied. Properly labeling this output data becomes paramount to ensure consistent data quality, interpretability, and trustworthiness of the model’s results. 

Labeling doesn’t simply mean classifying data; it involves assigning meaningful, accurate tags to the model’s output based on context, ensuring that the data can be easily understood, categorized, and acted upon. This is essential for multiple reasons. Firstly, labeled data allows for better monitoring of the model’s performance over time. As the AI model operates in real-world scenarios, the labeled output can be compared against expected outcomes, providing insights into areas where the model might be deviating or where it continues to excel. Secondly, governance becomes more manageable and effective. With labeled data, any anomalies, biases, or inconsistencies can be quickly identified, ensuring that the AI’s outputs align with organizational standards, ethical considerations, and regulatory requirements. Over time, as the digital landscape and company priorities evolve, these labeled datasets can serve as a foundation for further refining and retraining the LLM, ensuring its relevance and accuracy. 

In essence, while training and optimizing AI models is a substantial part of the AI lifecycle, the post-training phase, especially data labeling, is crucial. It guarantees that the models, once deployed, continue to uphold the standards of data quality and governance that enterprises require. 

  • Surge AI, (labeler and lifcycle tracker)  
  • Snorkel AI (labeler and can hinder use if there’s a breach)

The Rest of the AI Toolbox 

AI has left 52% of consumers doubting the protection of their private information.  40% believe that AI usage will force companies to be more cautious with customer data. 

In the quest for creating a bespoke enterprise AI tool, the journey often demands a suite of specialized tools rather than simply opting for pre-existing solution. This customized approach not only caters to specific needs but also ensures enhanced functionality and adherence to unique standards and policies when protecting critical business data is paramount. 

For development challenges like identifying data inconsistencies in LLMs and pinpointing data sprawl during the model’s processes, Arize AI stands out as a robust solution. Their offerings help streamline data management and maintain model integrity. On the other hand, if your operations involve collaboration with governmental entities, adherence to stringent policies and protections becomes paramount. Vannevar Labs specializes in this niche, providing tailored software solutions that ensure complete compliance with government regulations. Moreover, for those aiming to offer a personalized search experience as part of their AI product suite, Neeva provides state-of-the-art personalization capabilities, setting your product apart. 

Furthermore, as AI processes intensify, the underlying data infrastructure often faces unprecedented demands. The necessity for reliable and high-speed access to golden source copies of data, especially in relational systems, becomes inevitable. In scenarios where high Input/Output Operations Per Second (IOPs) and throughput exceed what conventional cloud databases or native storage solutions offer, Silk emerges as a game-changer. Not only does it deliver on performance metrics, but its capability to offer zero-footprint instant extracts ensures that LLMs can source data without exerting undue pressure on production relational database workloads. This holistic approach guarantees efficiency while preserving the integrity of the primary systems.  A third advantage of storing data on Silk’s solution is the enhanced early detection and protection against ransomware attacks Several reports and surveys on cybersecurity and AI highlight that organizations are increasingly aware of the potential risks and are investing in security features similar to what Silk offers as part of its platform.  

Lastly, it might be essential for you to substitute the data in your LLMs with simulated data, ensuring that even in the event of a breach, no harm is done. Utilizing Hugging Face allows for the safeguarding of crucial and confidential customer information, eliminating the need for specialized AI data risk detection tools, such as those for patient data from Bayesian Health. 

 AI statistics from: 

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