Accelerating Production Machine Learning with MLflow with Matei Zaharia

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Successfully building and deploying a machine learning model can be difficult to do once. Enabling other data scientists (or yourself, one month later) to reproduce your pipeline, to compare the results of different versions, to track what’s running where, and to redeploy and rollback updated models is much harder. In this talk, I’ll introduce MLflow, a new open source project from Databricks that simplifies the machine learning lifecycle. MLflow provides APIs for tracking experiment runs between multiple users within a reproducible environment, and for managing the deployment of models to production. MLflow is designed to be an open, modular platform, in the sense that you can use it with any existing ML library and development process. MLflow was launched in June 2018 and has already seen significant community contributions, with 45 contributors and new features new multiple language APIs, integrations with popular ML libraries, and storage backends. I’ll go through some of the newly released features and explain how to get started with MLflow.

Daten & Analysen

: Accelerating Production
Machine Learning
Matei Zaharia
@matei_zaharia

ML Lifecycle
3
Delta
Data Prep
Training
Deploy
Raw Data
μ
λ θ Tuning
Scale
μ
λ θ Tuning
Scale
Scale
Scale
Model
Exchange
Governance

Custom ML Platforms
Facebook FBLearner, Uber Michelangelo, Google TFX
Standardize the data prep / training / deploy cycle:
if you work within the platform, you get these!
Limited to a few algorithms or frameworks
Tied to one company’s infrastructure
Can we provide similar benefits in an open manner?

www.mlflow.org
Introducing
Open source machine learning platform
• Works with any ML library & language
• Runs the same way everywhere & cross-cloud
• Scales to big data with Apache Spark
Launched this June
• Already 48 contributors and many new features!
This Talk: MLflow Overview + New Announcements

How Does MLflow Work?
8
Tracking
Record and query
experiments: code,
data, config, results
Projects
Package workflows
into reproducible
and reusable steps
Models
Model packaging
format for diverse
deployment tools

Model Development without MLflow
data = load_text(file)
ngrams = extract_ngrams(data, N=n)
model = train_model(ngrams,
learning_rate=lr)
score = compute_accuracy(model)
print(“For n=%d, lr=%f: accuracy=%f”
% (n, lr, score))
pickle.dump(model, open(“model.pkl”))
For n=2, lr=0.1: accuracy=0.71
For n=2, lr=0.2: accuracy=0.79
For n=2, lr=0.5: accuracy=0.83
For n=2, lr=0.9: accuracy=0.79
For n=3, lr=0.1: accuracy=0.83
For n=3, lr=0.2: accuracy=0.82
For n=4, lr=0.5: accuracy=0.75
...
What if I expand
the input data?
What if I tune this
other parameter?
What if I upgrade
my ML library?
What version of
my code was this
result from?

Model Deployment without MLflow
Code & Models
DATA
SCIENTIST
PRODUCTION
ENGINEER
Please deploy this
SciKit model!
Please deploy this
Spark model!
Please deploy this
R model!
Please deploy this
TensorFlow model!
Please deploy this
ArXiv paper!
…

Model Development with MLflow
data = load_text(file)
ngrams = extract_ngrams(data, N=n)
model = train_model(ngrams,
learning_rate=lr)
score = compute_accuracy(model)
print(“For n=%d, lr=%f: accuracy=%f”
% (n, lr, score))
pickle.dump(model, open(“model.pkl”))

$ mlflow ui
Model Development with MLflow
data = load_text(file)
ngrams = extract_ngrams(data, N=n)
model = train_model(ngrams,
learning_rate=lr)
score = compute_accuracy(model)
mlflow.log_param(“data_file”, file)
mlflow.log_param(“n”, n)
mlflow.log_param(“learning_rate”, lr)
mlflow.log_metric(“score”, score)
mlflow.sklearn.log_model(model)
Track parameters, metrics,
output files & code version
Search using UI or API

Project Spec
Code ConfigDeps
Local Execution
Remote Cluster
Packaging Code: MLflow Projects
$ mlflow run git://...

Model Format
ONNX Flavor
Python Flavor
Training Apps
Batch & Stream Scoring
REST Serving Tools
Packaging Models: MLflow Models
Packaging Format
. . .
Inference Code
MLlib

Model Deployment with MLflow
DATA
SCIENTIST
PRODUCTION
ENGINEER
Please deploy this
MLflow Model!
OK, it’s up in our REST
server & Spark!
Please run this
MLflow Project
nightly for updates!
Don’t even tell me
what ArXiv paper
that’s from...

MLflow Development Status
Many new features since our release in June
• Model packaging for MLlib, H2O, TensorFlow, PyTorch, Keras
• Storage on Azure, AWS, Google, SFTP
• Java and Scala API
• New examples and UI features
Just released MLflow 0.7.0 today!

Major Announcement in MLflow 0.7.0
+
RStudio partnered with Databricks to add an MLflow R API
See Kevin Kuo’s talk on this at 14:40 today!

Conclusion
Workflow platforms can greatly simplify ML development
• Improve usability for both data scientists and engineers
Get started at mlflow.org, and come see our other talks:
• MLflow R API: 14:40 today
• ML Factories: 11:40 Thursday
• 1h Deep Dive: 14:00 Thursday

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In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks. Imagine you have a large dataset, say 20 GBs, and you want to use it to train a TensorFlow model. Before feeding the data to the model, you need to clean and preprocess your data using Spark. Now you have your dataset in a Spark DataFrame. When it comes to the training part, you may have the problem: How can I convert my Spark DataFrame to some format recognized by my TensorFlow model? The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity. The Databricks Machine Learning team contributed a new Spark Dataset Converter API to Petastorm to simplify these tedious data conversion process steps. With the new API, it takes a few lines of code to convert a Spark DataFrame to a TensorFlow Dataset or a PyTorch DataLoader with default parameters. In the talk, I will use an example to show how to use the Spark Dataset Converter to train a Tensorflow model and how simple it is to go from single-node training to distributed training on Databricks.

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Databricks

There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal. In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.

Scaling your Data Pipelines with Apache Spark on Kubernetes

Databricks

Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark. Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations. Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Databricks

In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.

Sawtooth Windows for Feature Aggregations

Databricks

We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark. Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue · Why? o Custom queries on top a table; We load the data once and query N times · Why not Structured Streaming · Working Solution using Redis Niche 2 : Distributed Counters · Problems with Spark Accumulators · Utilize Redis Hashes as distributed counters · Precautions for retries and speculative execution · Pipelining to improve performance

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Databricks

In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data. In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.

Re-imagine Data Monitoring with whylogs and Spark

Databricks

Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components. We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure. This allows us to introduce optimization rules that (i) reduce unnecessary computations by passing information between the data processing and ML operators (ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and (iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator. We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.

Raven: End-to-end Optimization of ML Prediction Queries

Databricks

Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis. Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them. Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy. This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.

Processing Large Datasets for ADAS Applications using Apache Spark

Databricks

At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences. What are we storing? Multi Source – Multi Channel Problem Data Representation and Nested Schema Evolution Performance Trade Offs with Various formats Go over anti-patterns used (String FTW) Data Manipulation using UDFs Writer Worries and How to Wipe them Away Staging Tables FTW Datalake Replication Lag Tracking Performance Time!

Massive Data Processing in Adobe Using Delta Lake

Databricks

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