* [Build simple API service with Python FastAPI — Part 1](https://dev.to/cuongld2/build-simple-api-service-with-python-fastapi-part-1-581o) by [cuongld2](https://dev.to/cuongld2).
        * [FastAPI + Zeit.co = 🚀](https://paulsec.github.io/posts/fastapi_plus_zeit_serverless_fu/) by [Paul Sec](https://x.com/PaulWebSec).

* **Deep Learning**: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.

### Concurrency + Parallelism: Web + Machine Learning { #concurrency-parallelism-web-machine-learning }

		}
	})

	// Always load ENV variables from files first.
	loadEnvVarsFromFiles()

	// Handle early server environment vars
	serverHandleEarlyEnvVars()

	// Handle all server command args and build the disks layout
	bootstrapTrace("serverHandleCmdArgs", func() {
		err := buildServerCtxt(ctx, &globalServerCtxt)
		logger.FatalIf(err, "Unable to prepare the list of endpoints")

		serverHandleCmdArgs(globalServerCtxt)

</p>

<p>
Any implementation can, upon detecting a data race,
report the race and halt execution of the program.
Implementations using ThreadSanitizer
(accessed with “<code>go</code> <code>build</code> <code>-race</code>”)
do exactly this.
</p>

<p>
A read of an array, struct, or complex number
may be implemented as a read of each individual sub-value

		word, _, operands1, ok := p.line(operands)
		if !ok {
			break
		}
		operands = operands1

		p.symDefRef(w, word, operands)
	}
	return p.errorCount == 0
}

// nextToken returns the next non-build-comment token from the lexer.
// It reports misplaced //go:build comments but otherwise discards them.
func (p *Parser) nextToken() lex.ScanToken {
	for {
		tok := p.lex.Next()
		if tok == lex.BuildComment {

      const tensorflow::FunctionDef& fdef,
      const std::vector<EdgeSpec>& e_edges,  // expected edges
      const std::vector<EdgeSpec>& c_edges,  // expected ctrl edges
      bool is_exact_edges = true) {
    // Build a set of edges from fdef
    std::set<EdgeSpec> a_edges;  // actual edges
    // Get edges from inputs to body nodes and between body nodes
    for (const NodeDef& node_def : fdef.node_def()) {

license-AGPL%20V3-blue)](https://github.com/minio/minio/blob/master/LICENSE) MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads. For more detailed documentation please visit [here](https://docs.minio.io/) Introduction ---------- This chart bootstraps MinIO Cluster on [Kubernetes]...

license-AGPL%20V3-blue)](https://github.com/minio/minio/blob/master/LICENSE) MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads. For more detailed documentation please visit [here](https://docs.minio.io/) Introduction ---------- This chart bootstraps MinIO Cluster on [Kubernetes]...

license-AGPL%20V3-blue)](https://github.com/minio/minio/blob/master/LICENSE) MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads. For more detailed documentation please visit [here](https://docs.minio.io/) Introduction ---------- This chart bootstraps MinIO Cluster on [Kubernetes]...

license-AGPL%20V3-blue)](https://github.com/minio/minio/blob/master/LICENSE) MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads. For more detailed documentation please visit [here](https://docs.minio.io/) Introduction ---------- This chart bootstraps MinIO Cluster on [Kubernetes]...