Tag: AI architecture

  • How AI Architecture, Not Just Models, Drives Enterprise Success: Insights from NewStreetTech’s Shrish Anand Lal

    How AI Architecture, Not Just Models, Drives Enterprise Success: Insights from NewStreetTech’s Shrish Anand Lal

    Artificial intelligence has moved beyond the experimental phase. CIOs and technology leaders are now making strategic decisions about deploying AI in real-world, production environments where operational constraints and real consequences are the norm. As organizations shift from AI pilots to full-scale adoption, the spotlight is turning from the models themselves to the infrastructure that supports them.

    In a recent episode of the Analytics Insight Podcast, host Priya Diyalani spoke with Shrish Anand Lal, Executive Director and Chief Business Officer at NewStreetTech, about why enterprises need to focus on AI architecture and infrastructure rather than relying solely on AI models. The conversation covered the limitations of model-first strategies, core building blocks of enterprise AI infrastructure, cloud and hybrid deployment evolution, AI ROI, autonomous agents, platform strategy, and the importance of deterministic execution and governance. Below are the key takeaways.

    Why AI Models Alone Are Not Enough for Enterprise Adoption

    According to Lal, the AI model represents only about 20% of the challenge when it comes to enterprise adoption. The remaining 80% involves everything else — what NewStreetTech calls the FITS framework: Fear, Inertia, Trust, and Surprise. None of these issues can be solved by simply adopting a better model.

    Referencing research from NASSCOM, Lal noted that 65% of companies in India have launched AI pilots, but only 15% have reached production. He emphasized that models must be applied at the design phase, while execution must remain deterministic. “The AI model is becoming a commodity very quickly, but the AI architecture will be a competitive advantage,” he said.

    Core Building Blocks of Enterprise AI Infrastructure

    Lal outlined four essential building blocks:

    1. Separation — AI operates at the design and configuration stage, while execution remains deterministic. The two layers must not interact.
    2. Governance by design — Maker-checker workflows, audit trails, version control, and role-based access should be foundational, not afterthoughts.
    3. Orchestration — Using a single AI platform for everything is the wrong approach. NewStreetTech’s MyFix.ai, for instance, uses over 36 configuration agents and 14 execution engines, all deterministic.
    4. Deployment flexibility — Organizations must have the option to deploy on-premises, in the cloud, or via a hybrid model, especially for regulated industries.

    Cloud and Modern Architectures Evolving for Enterprise AI

    AI computing costs are dropping rapidly, which is good for adoption. But the real revolution, Lal argued, is not just about computing — it’s about architecture. The shift is from “AI as a service” to “AI as an infrastructure.” Organizations need resilience through multiple providers to avoid lock-in. Some workloads must remain on-premise; others can go to the cloud. This architecture transformation is critical for regulated industries, where leaving everything to AI creates a black box with unanswered questions.

    How the Conversation Around AI ROI Has Changed

    Two years ago, boardrooms asked whether they should invest in AI. Today, the question is why AI hasn’t yet gone into production. The ROI debate has shifted from the cost of implementing AI to the cost of not implementing AI. Enterprises now worry about falling behind competitors. Companies that are seeing real ROI started small.

    AI Agents and Autonomous Workflows: Reshaping Enterprise Infrastructure

    Lal clarified that AI agents are not chatbots. They are specialized workers handling specific tasks. At MyFix.ai, there are more than 37 specialized configuration agents and 14 deterministic execution engines. Agents build the system, humans approve it, and engines run it. This is the fundamental difference between building enterprise-grade AI infrastructure and simply adding more computing power.

    “The future will not be one AI system doing everything. It will be specialized agents working through orchestration, trust levels, cross-validation, and human oversight,” Lal concluded.

    Listen to the full discussion on the Analytics Insight Podcast.

  • Best AI Architecture Certifications for 2026: From Beginner to Expert

    Best AI Architecture Certifications for 2026: From Beginner to Expert

    The demand for AI architects is skyrocketing as enterprises scale generative AI, agentic AI, and automation across industries. Whether you’re just starting out or an experienced professional, earning the right AI architecture certification can validate your expertise and unlock higher-paying roles. This guide covers the top certifications for 2026, from entry-level to advanced, and offers tips on choosing the best path for your career.

    Why AI Architecture Certifications Matter Now

    AI architecture goes beyond traditional software development. It involves building end-to-end systems that integrate data pipelines, cloud services, machine learning models, APIs, security, governance, and monitoring. Employers increasingly seek professionals who not only understand how AI models work but also how to deploy and scale them responsibly. Modern certifications cover real-world scenarios like retrieval-augmented generation (RAG), AI agents, MLOps, prompt engineering, and responsible AI, making them highly relevant for hands-on roles.

    Best Certifications for Beginners

    If you’re new to AI, these foundational certifications require no prior programming experience:

    • Microsoft Azure AI Fundamentals (AI-900): Introduces machine learning, computer vision, natural language processing, and responsible AI concepts with a focus on Microsoft’s AI services.
    • AWS Certified AI Practitioner: Designed for both technical and business professionals, covering generative AI concepts, AWS AI services, and real-world use cases.
    • Google Generative AI Leader: A newer option gaining traction for professionals interested in understanding how generative AI can be applied in business contexts.

    Intermediate Certifications for AI Engineers

    After building a foundation, these certifications focus on creating and deploying practical AI applications:

    • Microsoft Azure AI Engineer Associate (AI-102): Concentrates on building AI applications using Azure AI services, cognitive services, and responsible AI practices.
    • Google Cloud Professional Machine Learning Engineer: Ideal for those working with ML pipelines, model deployment, and AI infrastructure on Google Cloud.
    • AWS Certified Machine Learning Engineer – Associate: Covers building, deploying, and monitoring models with cloud-native ML workflows.

    Advanced Certifications for AI Architects

    Experienced professionals designing enterprise-scale AI platforms should consider these advanced programs:

    • Databricks Certified Generative AI Engineer Associate: Focuses on production-grade generative AI applications using vector databases, retrieval systems, MLflow, and large language models. Ideal for organizations deploying AI copilots and enterprise search.
    • NVIDIA Deep Learning Institute (DLI) Certifications: Increasingly valuable as enterprises adopt GPU-accelerated AI. Programs cover deep learning optimization, LLM deployment, and high-performance AI computing.

    How to Choose the Right Certification Path

    Your choice should align with your career goals and experience level:

    • Non-technical professionals: Start with AI literacy certifications, then move to cloud AI engineering certifications.
    • Software developers: Choose associate-level cloud AI certifications to build on existing coding skills.
    • Data scientists: Combine cloud certifications with MLOps and generative AI credentials.

    Also consider the cloud ecosystem of your target employer. Azure is common in large enterprises, AWS dominates the infrastructure market, and Google Cloud is strong in ML and data engineering.

    Beyond Certifications: Building Real-World Skills

    Certifications validate knowledge, but employers want proof of practical experience. Work on AI systems, explore open-source LLMs, build RAG applications, and contribute to GitHub projects. Combining recognized certifications with hands-on projects will make you far more competitive. The goal is to create secure, scalable, production-ready AI systems.

    Why This Matters

    AI architecture certifications provide structured learning, validate industry-relevant skills, and keep you current with emerging technologies like LLMs, RAG, MLOps, AI governance, and cloud AI services. When paired with real-world experience, they significantly boost employability and prepare you for AI leadership roles.