With more data and computational needs than ever before, building a robust machine learning foundation is crucial for developing cutting-edge applications and scaling efforts over time.

These services cover data, computation, monitoring, collaboration and more. While each infrastructure service provides value on its own, together they form a complete platform that can support advanced machine learning from research and development through production deployment.

Let’s explore 10 essential infrastructure services that every AI team should have access to in order to build a powerful foundation for their work.

Service 1: Data Storage and Management

The lifeblood of any AI server is data. With petabytes, exabytes or even larger volumes now commonplace, having scalable storage and easy access to diverse datasets is non-negotiable. A few must-have capabilities include:

• Massive storage capacity that can scale elastically as needs grow over time. Both hot and cold storage options are important.
• Intelligent data organization that makes it simple to discover, access, version and share datasets across teams and projects.
• Data ingestion tools to stream live data sources or batch process large files from various sources and formats.
• Metadata tagging for richer search and fine-grained access control. As datasets become larger, metadata is key to navigating content.
• Data preparation utilities for tasks like cleaning, normalization, feature engineering and data augmentation.

With the right data management service, teams can focus on model building instead of wrangling bits and bytes. Over 2 petabytes of data are now supported, with new datasets added daily.

Service 2: Distributed Computing

Modern AI has made distributed computing a necessity, from the data-parallelism of training large neural networks to running inference at massive scale. Key capabilities include:

• Elastic compute capacity can burst to thousands of GPUs or other accelerators on demand. Both on-premise and cloud-based options exist.
• Automatic scaling of workloads as requirements change. Resources should scale up and down seamlessly based on queue depth or other signals.
• Distributed training frameworks like Horovod, TensorFlow, PyTorch and others are pre-installed for fast deployment.
• Jupyter notebooks, Dask or other tools for interactive exploration and debugging at any scale.
• Monitoring of hardware utilization and job progress with alerts on failures.

By connecting to a few lines of code, teams get instant access to virtually unlimited parallel processing power for all phases of AI model development and deployment. Over 250 petaFLOPS of training performance are now available on-demand.

Service 3: Model Registry

With tens, hundreds or thousands of models being created, an effective model registry is essential for tracking experiments, reproducing results and managing model versions. It allows rich metadata tags and descriptions for each model to document purposes, techniques, hyperparameters and more.

Moreover, by bringing transparency to the full model lifecycle, the registry helps optimize research and ensures robust governance of production assets. Over 10,000 models are now tracked, from initial concepts to retired versions.

Service 4: Model Training Orchestration

Coordinating all the pieces needed for distributed deep learning training can be a challenge, from data preparation to experiment tracking. A training orchestrator handles these tasks:

• Automatic scaling of training jobs to any number of accelerators based on simple configuration.
• End-to-end workflow definition for any data science task using a visual interface or code.

By abstracting away cluster management, the orchestrator allows data scientists and researchers to focus on modeling rather than systems. Over 100,000 training runs are now tracked monthly across various projects.

Service 5: Model Evaluation and Monitoring

Once models are trained, an effective evaluation and monitoring platform is needed. It will support the deployment of models to various environments like production, staging and canary for rigorous testing. It will also monitor key metrics like accuracy, latency, throughput, errors and more over time at the production scale. When you gain full visibility into real-world model behavior, risks are mitigated before impacts occur on a large scale.

Service 6: Collaboration Tools

Modern AI servers depend on multidisciplinary teams working seamlessly together. Collaboration tools are therefore essential:

• Code repositories with version control, pull requests and issue tracking for all machine-learning code and configurations.
• Notebooks and documentation for sharing insights, visualizations and reports.
• Discussion forums, comments and annotations to have conversations around models.
• Project and task management to coordinate work across data scientists, engineers and others.
• Identity and access management to securely grant fine-grained access to teams and resources.

By bringing structure and transparency to teamwork, productivity soars. Over 1,000 active users now collaborate daily on the platform.

Service 7: Feature Store

As models evolve, reusable features become important to manage and version. A feature store addresses this by storing both raw features like texts and engineered features like embeddings in an online serving system. It supports batch and online feature extraction and transformation from data sources and enables feature reuse across models through standard interfaces.

With centralizing features, the store helps maximize data utility over the full lifecycle of dozens of concurrent projects.

Service 8: Model Monitoring and Observability

Advanced monitoring is key to maintaining high model quality at scale. Beyond metrics, full visibility into behavior is important. It allows:

• Distributed tracing to track requests through tiers to diagnose slowdowns.
• Logging of errors, warnings and diagnostic information.
• Audit logs to understand who accessed what resources and when.
• Business application profiling to find bottlenecks.
• A/B testing and canary deployments to compare model versions safely.
• Integrations with external monitoring tools (Prometheus, Grafana, etc.).

With deep observability, even the most subtle issues can be caught early. Over 100 billion logs are now analyzed daily through integrated services.

Service 9: Model Explainability

As models impact more decisions, explainability becomes crucial for ensuring fairness and compliance. You need:

• Model interpretation tools to assess feature importance, correlations and predictions.
• Counterfactual explanations surface as to why a particular prediction occurred.
• Annotations and metadata to provide context on model limitations and appropriate use.
• Bias detection via demographic data analysis is used to flag unfair outcomes.
• Documentation of the full model development process (data, techniques, evaluations).

Through proactive explainability, risks are mitigated and stakeholder trust is built. Over 1,000 models have been interpreted to date.

Service 10: Security and Compliance

Protecting sensitive data and intellectual property is paramount. By design, the infrastructure services meet the most stringent security and privacy needs of regulated domains. Features like role-based access control to infrastructure down to a file/record level and data encryption of files in transit and at rest using industry-standard techniques are a must. Over 1 million access control policies are now actively enforced.

Final Words

Building a complete AI infrastructure foundation requires juggling many moving pieces. However, by leveraging managed services that cover data, compute, model management, security and more, teams can stay laser-focused on their core work of developing cutting-edge AI. The right platform eliminates roadblocks, so data scientists and engineers can work at peak efficiency.