* L'apprentissage automatique (ou **Machine Learning**) : cela nécessite de nombreuses multiplications de matrices et vecteurs. Imaginez une énorme feuille de calcul remplie de nombres que vous multiplierez entre eux tous au même moment.

* **Machine Learning**: normalmente requiere muchas multiplicaciones de "matrices" y "vectores". Piensa en una enorme hoja de cálculo con números y multiplicando todos juntos al mismo tiempo.

### Major Features and Improvements

*   Added F-Score metrics `tf.keras.metrics.FBetaScore`, `tf.keras.metrics.F1Score`, and `tf.keras.metrics.R2Score`.
*   Added activation function `tf.keras.activations.mish`.

				MetricV2{
					Description: getMinioProcessIOReadBytesMD(),
					Value:       float64(io.ReadBytes),
				})
		}

		if io.WriteBytes > 0 {
			metrics = append(metrics,
				MetricV2{
					Description: getMinioProcessIOWriteBytesMD(),
					Value:       float64(io.WriteBytes),
				})
		}

		if io.RChar > 0 {
			metrics = append(metrics,
				MetricV2{

// server lists all metrics that could be returned for the requested path.
//
// The (repeatable) `buckets` query parameter is a list of bucket names (or it
// could be a comma separated value) to return metrics with a bucket label.
// Bucket metrics will be returned only for the provided buckets. If no buckets
// parameter is provided, no bucket metrics are returned.

	// Queued operations
	Queued InQueueMetric `json:"queued"`
	// Proxy stats
	Proxied ProxyMetric `json:"proxied"`
	// replication metrics summary for each site replication peer
	Metrics map[string]SRMetric `json:"replMetrics"`
	// uptime of node being queried for site replication metrics
	Uptime int64 `json:"uptime"`

		"Number of failures in DELETE tagging requests proxied to replication target",
		bucketL, targetArnL)
)

// loadBucketReplicationMetrics - `BucketMetricsLoaderFn` for bucket replication metrics
// such as latency and sent bytes.
func loadBucketReplicationMetrics(ctx context.Context, m MetricValues, c *metricsCache, buckets []string) error {
	if globalSiteReplicationSys.isEnabled() {
		return nil
	}

Harshavardhana <******@****.***> 1745441778 -0700

	deletes := make([]uint64, len(infos))
	writes := make([]uint64, len(infos))
	for index, di := range infos {
		deletes[index] = di.Metrics.TotalDeletes
		writes[index] = di.Metrics.TotalWrites
	}

	filter := func(list []uint64) (commonCount uint64) {
		maxCnt := 0
		signatureMap := map[uint64]int{}
		for _, v := range list {
			signatureMap[v]++
		}

After Prometheus is configured, you can use Grafana to visualize MinIO metrics. Refer the [document here to setup Grafana with MinIO prometheus metrics](https://github.com/minio/minio/blob/master/docs/metrics/prometheus/grafana/README.md).

## List of metrics exposed by MinIO

- MinIO exports Prometheus compatible data by default as an authorized endpoint at `/minio/v2/metrics/cluster`.