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- Introducing MLflow: Infrastructure for a Complete Machine Learning Lifecycle
Introducing MLflow: Infrastructure for a Complete Machine Learning Lifecycle
About this webinar
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.In our webinar, we will present MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
We will show how to:
– Keep track of experiments runs and results across popular frameworks, including TensorFlow, with MLflow Tracking
– Execute a MLflow Project published on GitHub from the command line or Databricks notebook as well as remotely execute your project on to a Databricks cluster
– Quickly deploy MLflow Models on-prem or in the cloud and expose them via REST APIsGet started now at https://www.mlflow.org/
- python – Sliding window of a batch in Tensorflow using Dataset API – Stack Overflow
Can be achieved using sliding window batch operation for tf.data.Dataset:
Example:
from tensorflow.contrib.data.python.ops import sliding
imgs = tf.constant([‘img0′,’img1’, ‘img2′,’img3’, ‘img4′,’img5’, ‘img6’, ‘img7’])
labels = tf.constant([0, 0, 0, 1, 1, 1, 0, 0])# create TensorFlow Dataset object
data = tf.data.Dataset.from_tensor_slices((imgs, labels))# sliding window batch
window = 4
stride = 1
data = data.apply(sliding.sliding_window_batch(window, stride))# create TensorFlow Iterator object
iterator = tf.data.Iterator.from_structure(data.output_types,data.output_shapes)
next_element = iterator.get_next()# create initialization ops
init_op = iterator.make_initializer(data)with tf.Session() as sess:
# initialize the iterator on the data
sess.run(init_op)
while True:
try:
elem = sess.run(next_element)
print(elem)
except tf.errors.OutOfRangeError:
print("End of dataset.")
break - Twitter
RT @peterdaou: Ok, then. Cheese it is tonight.
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