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How to Combat the Top 3 AI Scaling Challenges

AI Scaling

As many of us indulge in Netflix a little more than usual these days, you may have come across a documentary called Free Solo. It’s about the journey of a rock climber who attempts to conquer the first free solo climb of Yosemite’s 3,200-foot vertical rock face without a rope. (Yes, that’s right, without a rope.) Much like you’d come to expect from an award-winning film, it has the right balance of excitement, suspense, and fear.

As we talk to companies about leveraging Artificial Intelligence (AI), there’s often that same mix of excitement to move forward with this type of initiative, fear of failure and suspense if it will really do what is intended for their organization. Much like the climbing expedition, when there’s careful planning around navigating those tricky spots, you too can be successful. While the film focused on scaling the world’s most famous rock, we’re going to focus on a different kind of scaling – how to scale AI at an enterprise-level successfully.

AI Scaling Challenges

To ascend to the top, we must understand why AI can be challenging to scale and learn from those mistakes along the way.

  1. Data Complexity
    Enterprise data is commonly viewed as a cost rather than an opportunity. For many, the light bulb has turned on – there’s a drive to monetize it; however, we see huge challenges around data quality, management, stewardship, lineage, and traceability. The multitude of data formats also adds further complexity. Often, AI and Machine Learning (ML) initiatives must ingest multiple combinations of data types with differing maturity according to how they are structured, making it complicated to get started.
  2. Collaboration
    Effective collaboration is required for enterprises to scale their teams. New partners may be needed as skills need adjustments. A data science team requires a mix of roles – senior and junior data scientists, data engineers, DevOps engineers, data architects, business analysts, and scrum master/project managers. This iterative nature of data science development is different from traditional SDLC, leading to further gaps in process understanding.
  3. Existing Data Science Solutions Are Lacking
    Many data science solutions in the market solve one or more aspects of data science work very well but fail to address the end-to-end problem statement at an enterprise level. These types of solutions fall into the category of a ‘workbench,’ meaning they are well-suited for experimental work, but soon start to struggle when tasked with enterprise-scale use cases.

With all these challenges in front of me, how do we navigate around these common pitfalls? The solution? A scalable, easy to implement, modular, enterprise-ready AI platform, which leverages best-in-class open source technologies. Meet NessifAI.

Why is NessifAI Different?

This solution was designed around key challenges organizations typically face and how to solve them.

  • Sustainability

AI systems need automated testing for data, infrastructure, model training, and monitoring to keep them in sync with the real-world data. We call this closed-loop AI. The inability to automate may result in the initiative being unsustainable. A true AI platform relies on advanced MLOps and automation while incorporating techniques such as ML-assisted data curation, AutoML, and automatic model retraining to make this a sustainable initiative.

  • Agility

AI workloads demand iterative and collaborative work. With multiple teams contributing code to the same pipeline, and the system changing with each check-in, the traditional sprint-based release cadences are difficult to manage. What if you could remove these headaches by automating these processes, allowing AI assets such as data, features, models, code, and pipelines to be shared and reused by different personas simultaneously. This also brings standardization to the development cycle with built-in quality checks across the AI lifecycle.

  • Explainability

With the myriad of regulations for companies to follow, auditability and traceability of AI processes are imperative. Securing PII data without compromising the model’s accuracy is another essential. NessifAI provides end-to-end lineage and auditing across the lifecycle, right from data ingestion to model serving, documenting the decision logic at all stages of the AI lifecycle.

  • Monetization

Monetizing data is a cornerstone of a digital transformation, and AI calls for a new wave of monetizing insights. NessifAI creates a platform marketplace where all AI assets can be published and monetized for downstream consumption. Allowing for an easy Google-like search for all assets, and also curation of assets.

Start with the Right Footing

Intended to give you the right footing and foundation, NessifAI comprises key components that leverage your data and allow you to deliver value rapidly.

Foundry: A powerful and unified batch and streaming data platform engineered to meet the demanding enterprise workload.

Ledger: A single pane of glass to all operations on the platform, making data easily searchable and experiments reproducible.

Studio: A hyper-scale AI pipeline composer that allows cross-collaboration among different actors on the platform; a catalyst for rapid model building.

Insight: A highly performant model serving platform that enables automated model rollouts and monitors model performance over time.

With any AI adventure, it is vital to set out with a clear objective and understand how to avoid the challenges along the way. Contact us to learn more about how Ness can help you along your AI journey.

– Ashwyn Tirkey, Associate Vice President – Solutions