Another big feature needed by APIs is data validation, making sure that the data is valid, given certain parameters. For example, that some field is an `int`, and not some random string. This is especially useful for incoming data.

Without a data validation system, you would have to do all the checks by hand, in code.

   */
  public static <E extends Comparable<?>> Builder<E> naturalOrder() {
    return new Builder<>(Ordering.natural());
  }

  /**
   * A builder for creating immutable multiset instances, especially {@code public static final}
   * multisets ("constant multisets"). Example:
   *
   * {@snippet :
   * public static final ImmutableSortedMultiset<Bean> BEANS =

  When a "work that uses the Library" uses material from a header file
that is part of the Library, the object code for the work may be a
derivative work of the Library even though the source code is not.
Whether this is true is especially significant if the work can be
linked without the Library, or if the work is itself a library.  The
threshold for this to be true is not precisely defined by law.

   * delete the file and create a directory in its place, but this leads a race condition which can
   * be exploited to create security vulnerabilities, especially when executable files are to be
   * written into the directory.
   *
   * <p>This method assumes that the temporary volume is writable, has free inodes and free blocks,

    checkNonnegative(expectedSize, "expectedSize");
    return new ImmutableList.Builder<>(expectedSize);
  }

  /**
   * A builder for creating immutable list instances, especially {@code public static final} lists
   * ("constant lists"). Example:
   *
   * {@snippet :
   * public static final ImmutableList<Color> GOOGLE_COLORS =
   *     new ImmutableList.Builder<Color>()

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).

   * delete the file and create a directory in its place, but this leads a race condition which can
   * be exploited to create security vulnerabilities, especially when executable files are to be
   * written into the directory.
   *
   * <p>This method assumes that the temporary volume is writable, has free inodes and free blocks,

* **Deep Learning**: este es un subcampo de Machine Learning, por lo tanto, se aplica lo mismo. Es solo que no hay una sola hoja de cálculo de números para multiplicar, sino un enorme conjunto de ellas, y en muchos casos, usas un procesador especial para construir y / o usar esos modelos.

### Concurrencia + Paralelismo: Web + Machine Learning { #concurrency-parallelism-web-machine-learning }

storage class. ## If the PV uses a different storage class, specify that here. storageClass: "" VolumeName: "" accessMode: ReadWriteOnce size: 500Gi ## If subPath is set mount a sub folder of a volume instead of the root of the volume. ## This is especially handy for volume plugins that don't natively support sub mounting (like glusterfs). ## subPath: "" ## Expose the MinIO service to be accessed from outside the cluster (LoadBalancer service). ## or access it from within the cluster (ClusterIP service)....

storage class. ## If the PV uses a different storage class, specify that here. storageClass: "" VolumeName: "" accessMode: ReadWriteOnce size: 500Gi ## If subPath is set mount a sub folder of a volume instead of the root of the volume. ## This is especially handy for volume plugins that don't natively support sub mounting (like glusterfs). ## subPath: "" ## Expose the MinIO service to be accessed from outside the cluster (LoadBalancer service). ## or access it from within the cluster (ClusterIP service)....