But you can also exploit the benefits of parallelism and multiprocessing (having multiple processes running in parallel) for **CPU bound** workloads like those in Machine Learning systems.

That, plus the simple fact that Python is the main language for **Data Science**, Machine Learning and especially Deep Learning, make FastAPI a very good match for Data Science / Machine Learning web APIs and applications (among many others).

.pace .pace-activity {\n  border-color: #007bff transparent transparent;\n}\n\n.pace-center-radar-primary .pace .pace-activity::before {\n  border-color: #007bff transparent transparent;\n}\n\n.pace-center-simple-primary .pace {\n  background: #fff;\n  border-color: #007bff;\n}\n\n.pace-center-simple-primary .pace .pace-progress {\n  background: #007bff;\n}\n\n.pace-material-primary .pace {\n  color: #007bff;\n}\n\n.pace-corner-indicator-primary .pace .pace-activity {\n  background: #007bff;\n}\n\n....

she would keep, through all her riper years, the simple and
loving heart of her childhood:  and how she would gather about
her other little children, and make THEIR eyes bright and eager
with many a strange tale, perhaps even with the dream of
Wonderland of long ago:  and how she would feel with all their
simple sorrows, and find a pleasure in all their simple joys,
remembering her own child-life, and the happy summer days.

    }

    @Test
    @DisplayName("Should handle concurrent encryption operations safely")
    void testConcurrentEncryption() throws Exception {
        // Given - Test a simpler concurrent scenario first
        Smb2EncryptionContext context = new Smb2EncryptionContext(EncryptionNegotiateContext.CIPHER_AES128_GCM, DialectVersion.SMB311,
                testEncryptionKey, testEncryptionKey);

 * <strong>Performance</strong>).
 *
 * <p><strong>Cycle Detection</strong>
 *
 * <p>Deadlocks can arise when locks are acquired in an order that forms a cycle. In a simple
 * example involving two locks and two threads, deadlock occurs when one thread acquires Lock A, and
 * then Lock B, while another thread acquires Lock B, and then Lock A:
 *
 * <pre>

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        * [Microservice in Python using FastAPI](https://dev.to/paurakhsharma/microservice-in-python-using-fastapi-24cc)  by [Paurakh Sharma Humagain](https://x.com/PaurakhSharma).
        * [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).

Pero también puedes explotar los beneficios del paralelismo y la multiprocesamiento (tener múltiples procesos ejecutándose en paralelo) para cargas de trabajo **CPU bound** como las de los sistemas de Machine Learning.

Eso, más el simple hecho de que Python es el lenguaje principal para **Data Science**, Machine Learning y especialmente Deep Learning, hacen de FastAPI una muy buena opción para APIs web de Data Science / Machine Learning y aplicaciones (entre muchas otras).

   * serialized with XML 1.0.
   *
   * <p>For SOAP 1.1 messages, see {@code XML_UTF_8} per <a
   * href="http://www.w3.org/TR/2000/NOTE-SOAP-20000508/">W3C Note on Simple Object Access Protocol
   * (SOAP) 1.1</a>
   *
   * @since 20.0
   */
  public static final MediaType SOAP_XML_UTF_8 = createConstantUtf8(APPLICATION_TYPE, "soap+xml");

		p.errorf("TEXT %q: ABIInternal requires NOSPLIT", name)
	}

	// Next operand is the frame and arg size.
	// Bizarre syntax: $frameSize-argSize is two words, not subtraction.
	// Both frameSize and argSize must be simple integers; only frameSize
	// can be negative.
	// The "-argSize" may be missing; if so, set it to objabi.ArgsSizeUnknown.
	// Parse left to right.
	op := operands[next]
	if len(op) < 2 || op[0].ScanToken != '$' {