Scope & boundaries
This guide covers the environment where your own models are built and trained — notebooks, feature engineering, experiment tracking and the compute beneath them.
It does not cover foundation models you consume rather than train (Generative AI & LLM Platforms), the lifecycle after a model exists — deployment, monitoring, retraining (MLOps Platforms), or the human work and the workforce economics behind training data (AI Data Labeling & Annotation Services).
Executive Summary
AI/ML platforms operationalize data science experiments into governed production systems, focusing on reliable model deployment and ongoing honesty. Choosing one involves balancing unification versus best-of-breed tools like Databricks, AWS SageMaker, or Dataiku. The decision hinges on data gravity and MLOps operational readiness, rather than just model training capabilities.
An AI/ML platform is not judged by the model it can train in a notebook, but by how reliably it carries that model into production and keeps it honest once real traffic, drift, and auditors arrive.
An AI/ML platform is the operational spine between a data science experiment and a governed production system: the place where data becomes features, features become trained models, models become monitored endpoints, and every step leaves a lineage record an auditor can follow. The hard part has never been getting a model to fit; notebooks and open-source libraries solved that years ago. The hard part is the long tail after the demo — reproducible training, versioned deployment, drift detection, retraining, cost control, and the governance to prove what a model is doing and why — and that is exactly where most AI initiatives stall.
This guide evaluates 8 platforms — Databricks (Mosaic AI), AWS SageMaker, Azure Machine Learning, Google Vertex AI, Snowflake Cortex, Dataiku, DataRobot, and Weights & Biases — against the work that actually decides success: training and experiment tracking, MLOps and serving, AI governance, and the newer demand to serve, fine-tune, and observe large language models alongside classical ML. It keeps the open-source backbone (MLflow, plus H2O.ai’s AutoML lineage) in view, because no enterprise platform decision is made in isolation from it. It is written for CIOs, CTOs, CDOs, and the ML and platform engineers who own the consequences.
The single hardest trade-off in this category is unification versus best-of-breed. A cloud-native or lakehouse platform that sits where your data already lives spares you the integration tax and the data-egress bill, but couples your ML practice to one ecosystem’s pace and pricing. A specialist or open tool gives you portability and depth on one axis — experiment tracking, AutoML, governed self-service — but leaves you assembling and operating the seams yourself. The right answer depends less on a feature grid than on where your data gravity sits and how much MLOps machinery you are genuinely prepared to run.
Why the AI/ML Platform Decision Is Hard — and Consequential
An AI/ML platform decision is consequential because it sets the ceiling on velocity, governance, and cost for every model an organization ships for years. It must handle model development, MLOps and serving, and AI governance, supporting both classical ML and newer generative AI workloads. The strategic question is how much of your ML practice you are willing to anchor to a single data ecosystem to get one, with hyperscalers like Microsoft, AWS, Google, and Snowflake consolidating offerings.
The platform decision is consequential because it sets the ceiling on velocity, governance, and cost for every model your organization will ship for years. Choose well and data scientists move from idea to monitored endpoint on a paved road; choose badly and each model becomes a bespoke engineering project that ages into an unowned liability the moment its author changes teams. The decision is hard because the requirements pull against each other: data scientists want notebook freedom and the newest frameworks, platform teams want standardization and reproducibility, security and risk want lineage and controls, and finance wants GPU spend that does not quietly compound. No single tool maximizes all four, so the choice is really about which compromises you can live with — and which ecosystem’s gravity you are willing to accept.
The defining shift through 2026 is that the platform conversation has split into two overlapping waves that buyers must hold in their heads at once. The classical-ML wave — tabular models, forecasting, churn, fraud, recommendation — still runs the business and still depends on feature stores, registries, and drift monitoring. The newer generative wave — serving and fine-tuning open and proprietary LLMs, building retrieval pipelines, evaluating and observing non-deterministic agent behavior — demands GPU scheduling, a vector layer, prompt and evaluation tooling, and observability for outputs that no longer have a single right answer. Honest buyers treat these as related but distinct workloads; a vendor strong at one is not automatically strong at the other.
The second force is consolidation around data gravity. The hyperscalers and lakehouse vendors are pulling training, serving, governance, and the GenAI stack into the same plane as the data warehouse, betting that whoever owns the data owns the models. Microsoft has folded its generative tooling into Azure AI Foundry (renamed from Azure AI Studio, and rebranded again toward Microsoft Foundry), AWS has repositioned SageMaker as a unified data-and-AI environment, Google has consolidated Vertex AI under its Gemini Enterprise banner, and Snowflake has pushed AI directly into the warehouse with Cortex. The strategic question is no longer whether to adopt an AI platform, but how much of your ML practice you are willing to anchor to a single data ecosystem to get one.
Should you build or buy AI/ML Platforms?
Most enterprises should buy an AI/ML platform, reserving custom engineering for differentiating parts. While open-source components like MLflow can be assembled, the operational burden of upgrades, GPU, and on-call costs is significant. The decision hinges on existing data gravity, primary ML users, and operational readiness. Options include extending a lakehouse (Databricks), adopting a native hyperscaler platform (SageMaker, Azure ML, Vertex AI), or buying a portable platform (Dataiku, DataRobot).
Build-vs-buy in AI/ML is rarely all-or-nothing, because the open-source layer is so strong that almost every “buy” is really “buy the managed version of components you could assemble yourself.” A capable team can stitch MLflow for tracking, a feature store, KServe or a model server for serving, and Kubernetes for orchestration into a credible platform — and for organizations with deep ML engineering and unusual requirements, that control is worth the operating burden. The trap is underestimating the burden: the assembled stack has no single owner, upgrades cascade across components, and the GPU and on-call costs are permanent. Most enterprises are better served buying the paved road and reserving custom engineering for the parts that are genuinely differentiating.
The more useful framing is not whether to buy, but which camp to buy from and how tightly to couple to it. Extending the data platform you already run — lakehouse, warehouse, or hyperscaler — minimizes integration and data movement but inherits that ecosystem’s pricing and roadmap. A cloud-agnostic platform buys portability and governed self-service at the price of another vendor relationship. A specialist tool buys depth on one axis. Decide by where your data gravity already sits, who your primary ML users are, and how much operational machinery you are realistically prepared to run.
| Scenario | Recommendation | Rationale |
|---|---|---|
| Lakehouse already standard (Databricks or equivalent) and ML lives next to the data | Extend the lakehouse | Mosaic AI brings training, serving, the feature store, and governed model management into the platform that already holds your data and Unity Catalog lineage — the integration and egress you avoid usually outweigh a standalone tool’s depth. |
| Committed to one hyperscaler with platform engineers on staff | Adopt the native platform | SageMaker, Azure ML, or Vertex AI give the deepest integration with that cloud’s identity, data, and GPU supply, and the lowest data-movement cost — accept ecosystem lock-in as the deliberate price of cohesion and managed serving. |
| Multi-cloud or cloud-agnostic mandate avoiding a single ecosystem | Buy a portable platform | Dataiku and DataRobot run across clouds and on-prem, giving one governed control plane independent of where compute sits — you trade some native-cloud depth for the freedom to move workloads and not be repriced by one provider. |
| Analysts and citizen data scientists need governed self-service ML | Buy a visual / AutoML platform | Dataiku’s visual flows and DataRobot’s automated modeling let domain experts build and ship models inside guardrails, widening who can contribute without a notebook-first skill set — with governance that survives the handoff. |
| Deep ML team wants control and frameworks change fast | Assemble around open source | MLflow, a model server, and Kubernetes give maximum flexibility and zero platform license — justified only when you have the engineers to own upgrades, security, and on-call, and a real reason the managed road does not fit. |
| Serving and fine-tuning LLMs is the headline workload | Prioritize the GenAI/GPU stack | Weigh GPU availability and pricing, managed model serving, a vector layer, and LLM evaluation and observability as first-class criteria — and keep this distinct from your classical-ML platform if one tool does not credibly cover both. |
How do you evaluate AI/ML Platforms?
Weight these domains against your own workload mix before you score a single vendor. An enterprise running hundreds of tabular models in regulated production will rank operations and governance far above GenAI tooling; a team whose roadmap is dominated by LLM applications will invert that. The point of the weights is to force an explicit decision about what this platform is for — classical MLOps at scale, broad self-service, cutting-edge GenAI, or some honest blend — rather than letting a long feature list flatten every criterion into equal importance.
| Capability Domain | Weight | What to Evaluate |
|---|---|---|
| MLOps, Serving & Lifecycle | 25% | Reproducible training pipelines and CI/CD for models, a first-class model registry with versioning and stage promotion, real-time and batch/streaming serving, A/B and canary rollout, automated retraining, and rollback — the path from registered model to monitored endpoint and back |
| Model Development & Experimentation | 20% | Notebook and IDE experience, experiment tracking and run comparison, feature engineering and a feature store, AutoML for breadth, distributed training, and support for the frameworks your teams use (PyTorch, TensorFlow, scikit-learn, XGBoost, Spark ML) |
| AI Governance & Responsible AI | 20% | End-to-end lineage from data to deployed model, approval workflows and access control, bias and fairness testing, explainability, model and prompt audit trails, and the evidence trail needed for internal risk review and emerging AI regulation such as the EU AI Act |
| GenAI & LLM Operations | 15% | Managed serving and fine-tuning of open and proprietary LLMs, a vector/retrieval layer for RAG, prompt management, evaluation harnesses for non-deterministic output, agent orchestration hooks, and token and inference cost controls — weighted to how central GenAI is to you |
| Data, Compute & Cost Control | 10% | Proximity to where your data already lives and the egress it avoids, GPU availability and scheduling, spot/preemptible support, autoscaling, idle-resource reclamation, and chargeback/showback visibility so AI spend is attributable and governable rather than a surprise |
| Platform Openness & Ecosystem | 10% | Portability and exit cost (open formats, MLflow compatibility, container-based serving), multi-cloud and on-prem reach, breadth of integrations, collaboration across data science and engineering, and standards conformance so the platform extends rather than confines your stack |
Which vendors lead in AI/ML Platforms?
Consider AI/ML platforms from Databricks (Mosaic AI), AWS SageMaker, Azure Machine Learning, Google Vertex AI, and Snowflake Cortex. Other options include cloud-agnostic platforms like Dataiku and DataRobot, or specialist tooling such as Weights & Biases, MLflow, and H2O.ai. Vendors generally fall into lakehouse-and-warehouse, hyperscaler-native, or cloud-agnostic enterprise camps, with recent ownership and naming changes impacting offerings.
| Vendor | Positioning | Best for |
|---|---|---|
| Databricks (Mosaic AI) | Leader — Lakehouse + AI | Data-intensive enterprises that want training, serving, and governance to live on the same governed lakehouse as their data |
| AWS SageMaker | Leader — AWS-Native | AWS-committed organizations that want comprehensive, managed ML infrastructure integrated with their existing cloud data and identity |
| Azure Machine Learning | Strong — Microsoft Ecosystem | Microsoft-centric enterprises wanting governed pro-code MLOps that connects cleanly to Azure data, identity, and the Foundry GenAI stack |
| Google Vertex AI | Strong — Google Cloud + Gemini | Google Cloud customers wanting integrated training, AutoML, and serving with first-class access to Gemini and a broad model garden |
| Snowflake Cortex | Strong — AI in the Warehouse | Snowflake-centric teams that want LLM and ML capabilities and governed agents applied directly to warehouse data without a separate stack |
| Dataiku | Strong — Governed Self-Service | Enterprises that want one governed, cloud-agnostic platform spanning data scientists and business analysts, with multi-LLM access under guardrails |
| DataRobot | Strong — AutoML to Agents | Organizations that want automation-led modeling and fast, governed deployment for analyst-heavy teams, increasingly alongside agentic AI |
| Weights & Biases | Niche — Tracking + Eval | ML and AI teams that want best-in-class experiment tracking and model/agent evaluation layered on top of an existing training and serving stack |
The market sorts into camps that rarely compete cleanly head-to-head. Lakehouse-and-warehouse platforms — Databricks and Snowflake — pull ML toward the data, betting that whoever owns the governed data owns the models built on it. Hyperscaler-native platforms — AWS SageMaker, Azure Machine Learning, and Google Vertex AI — offer the deepest integration with one cloud’s data, identity, and GPU supply, and the lowest data-movement cost, in exchange for ecosystem gravity. Cloud-agnostic enterprise platforms — Dataiku and DataRobot — sell a portable, governed control plane and broad self-service across clouds and on-prem. And specialist tooling — Weights & Biases for experiment tracking and evaluation, MLflow as the open backbone, H2O.ai for AutoML — goes deep on one axis rather than spanning the whole lifecycle. Most shortlists end up comparing across these camps, which is why naming the camp first matters more than scoring features.
Ownership and naming have churned hard, and current state matters for a multi-year commitment. Databricks acquired MosaicML in 2023 (a deal valued around $1.3 billion) and rebuilt its generative stack into Mosaic AI. AWS launched a next-generation, unified SageMaker with SageMaker Unified Studio, generally available in 2025, and renamed the original service SageMaker AI inside it. Microsoft folded its GenAI tooling into Azure AI Foundry (renamed from Azure AI Studio in late 2024 and rebranded again toward Microsoft Foundry in late 2025), while Azure Machine Learning remains the distinct pro-code MLOps service that now integrates with Foundry. Google consolidated Vertex AI under its Gemini Enterprise banner. And in a notable change of independence, CoreWeave completed its acquisition of Weights & Biases in 2025 (a deal reported around $1.7 billion), tying a leading neutral tracking tool to a GPU-cloud provider. DataRobot and Dataiku remain independent. Verify ownership and roadmap directly with any vendor before you sign.
Databricks (Mosaic AI)
Leader — Lakehouse + AIModels train and serve next to governed data rather than a copy of it, and that is the whole argument: one lakehouse unifying data engineering, analytics, and ML, with Mosaic AI — built on the 2023 MosaicML acquisition — adding GPU training, fine-tuning, model serving, and a vector index, Unity Catalog supplying lineage and governance, and open-source MLflow, which the company stewards, as the registry and tracking backbone, multi-cloud across AWS, Azure, and GCP. Positioning is premium and the DBU consumption model is hard to forecast across compute types and serving endpoints. The platform pulls you toward the Databricks-and-Spark way of working, and smaller, notebook-light teams will find it heavier than the problem warrants.
AWS SageMaker
Leader — AWS-NativeThe broadest managed-ML catalog on AWS, and you assemble it yourself: the next-generation SageMaker and SageMaker Unified Studio, GA in 2025, fold data, analytics, and AI into one environment while the original engine is retained as SageMaker AI, with managed training on spot instances, the model registry and Pipelines for MLOps, real-time and batch endpoints, and tight ties to S3, Bedrock for foundation models, and the rest of AWS. That breadth arrives as many discrete services, with a learning curve and per-component pricing that is hard to estimate up front. Deep AWS coupling is the cost of the integration, and the recent reorganization means confirming which generation a given capability lives in.
Azure Machine Learning
Strong — Microsoft EcosystemConfirm what is Azure ML and what is Foundry before you scope anything — the rebranding is fast enough to be a real buying hazard. Underneath it, Microsoft’s distinct pro-code MLOps service is intact: managed training and compute, a model registry with cross-workspace registries, pipelines, managed online and batch endpoints, and a Responsible AI dashboard for fairness, explainability, and error analysis, now integrated with Azure AI Foundry — the renamed Azure AI Studio, moving toward Microsoft Foundry — so classical ML and the GenAI and agent stack sit on one governed Azure plane. The SDK v1 reaches end of support in mid-2026, so legacy pipelines need migration, and the value is strongest when you are already invested in Azure and Entra.
Google Vertex AI
Strong — Google Cloud + GeminiPin down exactly which surface and SKU you are buying, because the Gemini Enterprise rebranding is still in motion: Vertex AI, now consolidated under that banner, offers managed training, custom and AutoML modeling, a feature store, pipelines, and managed serving, with Model Garden providing one API surface to Gemini, Anthropic’s Claude, Llama, Gemma, and more, and strong BigQuery integration keeping ML close to warehouse data. It is a natural home for teams building on Gemini and Google-scale infrastructure. The enterprise-software footprint is smaller and third-party integrations fewer than the largest clouds, and the value concentrates for organizations already committed to Google Cloud.
Snowflake Cortex
Strong — AI in the WarehouseAI applied where the data already sits, which is both the point and the boundary: serverless LLM and ML functions in SQL, Cortex Analyst for natural-language querying over a semantic model, Cortex Search for retrieval, Cortex Agents for governed agentic workflows, and fine-tuning — all inside Snowflake’s security and governance perimeter and billed on familiar consumption credits, so teams use models without moving governed data out of the platform. It is oriented to analytics and in-warehouse AI rather than deep custom training and bespoke serving, so it complements a full ML platform more than it replaces one. Capabilities are tied to the Snowflake ecosystem, and some mature fast enough that you should confirm general availability versus preview for anything you depend on.
Dataiku
Strong — Governed Self-ServiceOne governed canvas for data scientists, analysts, and business users — and adopting its way of working is the price of admission: visual flows alongside code, AutoML, a feature store, and full MLOps for deployment and monitoring, with LLM Mesh adding a governed gateway that routes, secures, and audits access to multiple LLM providers, running cloud-agnostically across AWS, Azure, GCP, and on-prem. A consistent Gartner Magic Quadrant Leader, and still independent. It is less suited to cutting-edge ML research or the lowest-level custom-model work than a notebook-first or hyperscaler-native stack, and as an independent vendor reportedly moving toward an IPO, the roadmap is worth tracking as the company evolves.
DataRobot
Strong — AutoML to AgentsAutomation-led modeling for analyst-heavy teams, now pointed hard at agents — so classical-AutoML buyers should confirm the predictive-ML roadmap still fits them. It pioneered enterprise AutoML and extended into the full lifecycle: automated modeling and feature engineering, a model registry, deployment with monitoring and drift detection, and governance, repositioned around generative and agentic AI with the 2025 acquisition of Agnostiq and the open-source Covalent compute-orchestration project feeding that push, running across clouds and on-prem with an emphasis on getting non-specialists to production-grade models fast. Automation can obscure model internals, so test explainability against your governance bar, and as an independent, venture-backed vendor, weigh long-term strategy and pricing as it shifts.
Weights & Biases
Niche — Tracking + EvalThe specialist layer developers actually like, and it assumes you already have a stack to put it on: W&B Models for experiment tracking, run comparison, hyperparameter sweeps, artifacts, and a model registry, plus W&B Weave for LLM and agent observability and evaluation, framework- and cloud-agnostic and layering onto whatever training and serving you run — a companion to the broader platforms rather than a replacement. You still bring your own serving, pipelines, and governance. CoreWeave’s 2025 acquisition ties a previously neutral tool to a GPU-cloud provider, so weigh long-term neutrality and roadmap, and the deep value only arrives once teams instrument their workflows consistently.
How much should you budget for AI/ML Platforms?
Budgeting for AI/ML platforms is dominated by consumption, not licenses, with costs driven by compute instance-hours, especially GPU time. Expect surprise costs from data egress, GPU scarcity, inference at scale, and the permanent engineering cost of open-source stacks. Platforms like Databricks, AWS SageMaker, and Google Vertex AI meter consumption, while Dataiku and DataRobot offer subscriptions.
AI/ML platform pricing is dominated by consumption, not licenses, which makes the headline rate the least useful number in the conversation. Compute is the engine: training and serving meter on instance-hours or abstracted units (Databricks DBUs, Snowflake credits, hyperscaler instance pricing), and GPU time is the line item that compounds — idle endpoints, oversized training jobs, and always-on serving quietly dominate the bill long before any platform fee does. The platforms that bundle development, serving, and governance trade a higher unit rate for fewer seams; the specialists and open tools have a lower or zero platform fee but leave you paying for — and operating — the compute and the integration yourself.
The surprise costs hide in four places. First, data movement: pulling training data out of a warehouse or across clouds adds egress that platforms living next to your data avoid, which is much of the lakehouse and in-warehouse pitch. Second, GPUs: scarcity and premium serving instances can dwarf every other cost, so spot/preemptible support, autoscaling, and idle reclamation are pricing features, not niceties. Third, inference at scale: a model that is cheap to train can be expensive to serve once traffic is real, and LLM token costs add a separate, usage-driven meter. Fourth, the people: an assembled open-source stack carries no license but a permanent engineering and on-call cost that belongs in any honest TCO. Model the curve at production scale, not the proof-of-concept.
| Vendor | Pricing Model | Relative Tier | Key Cost Drivers |
|---|---|---|---|
| Databricks (Mosaic AI) | Consumption via DBUs, plus cloud infrastructure | Premium | DBU consumption by compute type, GPU instances for training and serving, model-serving endpoints, vector index, and underlying storage |
| AWS SageMaker | Per-instance-hour plus per-service, pay-as-you-go | Moderate–Premium | Training instance hours and type, real-time and batch endpoints, GPU selection, Studio usage, and Bedrock token consumption for foundation models |
| Azure Machine Learning | Per-compute consumption within Azure | Moderate–Premium | Compute hours and GPU availability, managed online/batch endpoints, storage, and Azure OpenAI / Foundry token usage for GenAI workloads |
| Google Vertex AI | Per-compute plus per-prediction / per-call | Moderate–Premium | Training hours, online and batch prediction volume, AutoML usage, feature store, and Gemini / Model Garden API calls |
| Snowflake Cortex | Serverless consumption on Snowflake credits | Moderate | Credit consumption for Cortex functions, tokens processed by LLM/AISQL functions, fine-tuning jobs, and the compute behind search and agents |
| Dataiku | Subscription, tiered by users / capacity | Moderate–Premium | User count and edition, deployment scale and nodes, governance and LLM Mesh features, and the underlying compute it orchestrates |
| DataRobot | Enterprise subscription (platform + consumption) | Premium | Licensed capacity and users, modeling and deployment volume, monitoring scope, generative/agent features, and underlying compute |
| Weights & Biases | Per-seat / usage subscription, free entry tier | Lower–Moderate | Tracked users and seats, tracked runs and stored artifacts/data volume, retention, and whether Weave LLM/agent observability is enabled |
How long does implementation take for AI/ML Platforms?
Implementing an AI/ML platform typically takes 12-15 months to reach full operationalization and governance. The initial 1-3 months focus on platform setup and deploying a first model. MLOps and productionization follow in months 4-7, with GenAI and scale-out occurring in months 8-11. The final phase, months 12-15, establishes portfolio-scale governance and optimization.
Sequence an AI/ML platform rollout around the path to production and the operations loop, not the notebook. The parts that run long are rarely the modeling — they are wiring the platform into your data and identity, standing up the registry-to-endpoint pipeline, and getting governance and monitoring real before models multiply. Land one model end to end early, then widen; a platform proven on a single governed deployment is worth more than a dozen experiments with no path to serving.
Stand up the platform, connect it to your data and identity, and establish the development environment, experiment tracking, and a feature store seeded with your highest-value features. Drive one real model all the way to a monitored production endpoint to prove the full path — this is where data-access, permissions, and networking surprises surface, so find them on model one.
Build the repeatable machinery: CI/CD for ML pipelines, the model registry with stage promotion and approvals, monitoring with drift and data-quality detection, automated retraining, and A/B or canary rollout. The common failure here is models reaching production with no owner or alerting; wire observability and on-call in before scaling the portfolio.
Extend to generative workloads where they fit: managed LLM serving and fine-tuning, a vector/retrieval layer for RAG, prompt management, and evaluation for non-deterministic output. Onboard more teams onto the paved road and tackle GPU cost discipline — autoscaling, idle reclamation, right-sized serving — before spend compounds quietly across endpoints.
Operationalize AI governance at portfolio scale: lineage and audit evidence, bias and explainability checks in the release path, approval workflows, and reporting aligned to internal risk and emerging AI regulation. Tune cost and reliability with chargeback visibility and SLAs — the platform is now operated as a product, not delivered as a project.
What should you ask vendors about AI/ML Platforms?
Use this checklist during evaluation to confirm each shortlisted platform covers what actually decides an AI/ML deployment — the operations loop, governance, and cost control, proven on your own data and workflows rather than promised on a slide.
Frequently asked questions about AI/ML Platforms
What are the hidden costs or common surprises when budgeting for AWS SageMaker or Databricks (Mosaic AI)?
For AWS SageMaker, the breadth of discrete services and per-component pricing can make upfront cost estimation challenging. With Databricks (Mosaic AI), the DBU consumption model, which varies by compute type and serving endpoints, can be difficult to forecast accurately, especially across different GPU instances and model-serving endpoints.
For a Snowflake-centric organization, when is Snowflake Cortex sufficient, and when do we still need a full ML platform like Azure Machine Learning or Google Vertex AI?
Snowflake Cortex is sufficient for LLM and ML capabilities applied directly to warehouse data, using serverless functions in SQL, and for governed agents. However, it complements rather than replaces a full ML platform when your needs extend to deep custom training, bespoke model serving, or require the broader MLOps capabilities of platforms like Azure Machine Learning or Google Vertex AI.
What are the common pitfalls or delays when integrating a new AI/ML platform into existing data and identity systems?
The most common delays and pitfalls arise when wiring the platform into your existing data and identity, and establishing data access, permissions, and networking. These surprises often surface during the 'Foundation & First Model' phase (Months 1-3) when driving the first real model all the way to a monitored production endpoint, highlighting the importance of proving the full path early.