Scalytics 1.2: Push AI Development | A Future Beyond Data Silos | Shift-Left Architecture for AI

November '24

Hello, and welcome to another edition of the now Scalytics newsletter. Yes it was quite a while silent here. Why? We built Scalytics 1.2 - yes, FedML at enterprise level.

Decentralizing AI to Scale Smarter With Federated Learning

As data grows exponentially, traditional machine learning faces scalability, privacy, and compliance challenges. Federated Learning (FL) offers a decentralized solution, enabling scalable, transparent, and secure AI systems.

Scalytics | Release 1.2: The Federated Learning Framework for Scalable, Secure AI

The latest release of Scalytics | Release 1.2 introduces powerful features for implementing federated learning and building auditable, traceable machine learning pipelines:

• Federated Machine LearningTrain models across platforms like Apache Spark, TensorFlow, and JDBC, without altering native code.Supports unsupervised learning techniques like k-means and optimization methods like Stochastic Gradient Descent for distributed environments.
• Auditable WorkflowsAccess Audits: Track who accessed which data, when and for what purpose, to ensure compliance.
• Audits: Log model training processes for traceability and improved accountability.
• New Data Sources: Process remote files over HTTP(S) and connect to any database using JDBC.
• Platforms: Support for Apache Kafka and TensorFlow broadens compatibility for distributed workflows.
• Enhanced Runtime: The new actor-based runtime simplifies the development of federated applications, improving performance and scalability.

Read the release notes here.

A Future Beyond Data Silos

Despite the promise of data lakes to eliminate data silos, they have proven to be ineffective in this promise. In fact, a data lake is essentially a larger version of a data silo, with a deeper customer lock-in strategy.

Scalytics | Solving the AI Data Dilemma: How to Overcome Data Silos and Centralization Challenges

Scalytics Connect offers a solution by bringing algorithms to data, ensuring data sovereignty and enabling AI-readiness without data movement, avoids the complexities of ETL pipelines and data lakes.

The key points are:

• Data Management Challenges: Persistent data silos in modern data infrastructures complicate data governance, foster AI bias, and limit flexibility.
• Data Governance and Compliance: Hybrid data infrastructures combining on-premise and cloud environments pose challenges for data governance and regulatory compliance.
• Scalytics Connect’s Solution: Scalytics Connect offers a solution for training AI systems and developing digital twins with real company data, addressing data governance and regulatory compliance challenges.

• Data Sovereignty Solution: Scalytics Connect brings algorithms to data instead of moving data to algorithms, ensuring compliance with regulations like GDPR.
• AI-Readiness: Scalytics Connect enables real-time data processing and AI model training while maintaining data governance protocols.
• Data Mobility Challenges: Traditional ETL systems can lead to data lock-in and require significant work to support compliance with regulatory restrictions.

• Data Sovereignty: Scalytics Connect ensures data remains within its original environment, maintaining data sovereignty and compliance with regulations like GDPR.
• Decentralized Algorithm Execution: Scalytics Connect positions and executes algorithms decentrally, avoiding data movement and ensuring data security.
• Real-time Analytics and AI: Scalytics Connect enables real-time data analytics and AI capabilities without the risks associated with data movement.

Shift-Left Architecture for AI

Organizations often face challenges with data silos, especially when sensitive data needs to remain within secure networks. The “Shift-Left Paradigm” addresses this by bringing algorithms to the data, reducing data movement and enhancing control. Scalytics Connect, a data firewall solution, enables secure data collaboration while maintaining data sovereignty and compliance with regulations like GDPR.

Scalytics | Transforming Data Management: The Shift-Left Architecture for Enhanced Data Collaboration

• Data Privacy Concerns: Organizations are reluctant to move sensitive data outside their secure network environments.
• Shift-Left Paradigm: Instead of moving data, bring analysis and training algorithms to the data, reducing the need to share sensitive raw data.
• Scalytics Connect’s Role: Enables secure data collaboration by creating connected areas (data products) within an organization’s network, acting as a bridge between these zones.

• Data Security: Scalytics Connect acts as a data firewall, keeping sensitive data within the secure network and only allowing collaborative data to be shared.
• Data Access Control: The system allows controlled access to data, permitting specific requests and information flows while blocking others.
• Data Processing: Processing is done at the data source, avoiding unnecessary data movement and enabling transparent monitoring through an open-source API.

• Data Ownership and Sovereignty: Customers maintain complete control over their data, with unnecessary data movements and copies eliminated.
• Compliance-First Approach: Scalytics Connect ensures an audit-ready solution from the outset, with data owners defining usage rules within the data firewall for immediate auditing and visibility of compliance levels.
• Decentralized Data-Centered Collaboration (DDZ): Scalytics Connect enables data-sharing capabilities directly at the business level through a scalable, robust API and an intuitive UI, connecting to the customer’s data plane within their infrastructure.

Besides the news from Scalytics - what else kept us all up that month? Significant developments, particularly concerning the scaling of Large Language Models (LLMs) and the challenges associated with their growth. Here are the top picks from us.

A Open-Source Standard for Collaborative AI Agents

Anthropic has introduced the Model Context Protocol (MCP), an open-source standard designed to seamlessly connect AI assistants with various data sources, including content repositories, business tools, and development environments. This initiative aims to enhance AI performance by providing a universal protocol that eliminates the need for custom integrations for each dataset. => Anthropic

Scaling Challenges of LLMs

The industry is confronting limitations in scaling LLMs beyond one trillion parameters. Constraints in training techniques and data availability are prompting a shift towards smaller, specialized models. This transition emphasizes enhancing models' memory, planning, and reasoning abilities over mere size expansion. => Barron's

Data Limitations

Researchers have identified a potential shortage of high-quality data for training expansive LLMs. Projections indicate that existing high-quality English language data could be exhausted imminently, with lower-quality data following soon after. This scarcity necessitates innovative data collection and utilization strategies to sustain AI advancement. => Cornell Tech

Legal Challenges

OpenAI faces legal scrutiny from Canadian news publishers alleging unauthorized use of their content to train models like ChatGPT. This lawsuit underscores the growing concerns about copyright infringement in AI training processes and highlights the need for clear legal frameworks. => AP News

AI in the Workforce

A recent survey reveals that 88% of Gen Z employees utilize AI tools to perform job tasks, aiming to overcome "task paralysis" and boost efficiency. This trend indicates a significant shift in workplace dynamics, with AI becoming integral to daily operations and productivity enhancement. => New York Post

About Scalytics

Modern AI demands more than legacy data systems can deliver. Data silos, scalability bottlenecks, and outdated infrastructure hold organizations back, limiting the speed and potential of artificial intelligence initiatives.

Scalytics Connect is a next-generation Federated Learning Framework built for enterprises. It bridges the gap between decentralized data and scalable AI, enabling seamless integration across diverse sources while prioritizing compliance, data privacy, and transparency.

Our mission is to empower developers and decision-makers with a framework that removes the barriers of traditional infrastructure. With Scalytics Connect, you can build scalable, explainable AI systems that keep your organization ahead of the curve. Break free from limitations and unlock the full potential of your AI projects.

Apache Wayang: The Java Federated Data Framework

Scalytics is powered by Apache Wayang, and we're proud to support the project. You can check out their public GitHub repo right here. If you're enjoying our software, show your love and support - a star ? would mean a lot! If you need professional support from our team of industry leading experts, you can always reach out to us via Slack or Email.