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Corporate
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Machine learning opened up new ways of solving technical... | scraping/output/7371013274892921836.txt | [
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### Regression & forecasting
Predicting the future is hard, but with the right tools, we can forecast
trends in e.g. energy consumption or sales volume with precision. We do this
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### Classification & clustering
Labeling data record... | scraping/output/-4005684865848025300.txt | [
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### Operational research & optimization
Even if a process works well, it can always be improved. This is where our
expertise comes in. We specialize in tackling complex problems such as
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## Client cases
Discover how our expertise in Hardware ... | scraping/output/-4005684865848025300.txt | [
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Computer vision
## Typical challenges
With our expertise, we can help you overcome structured data challenges in AI.
## Aligning the technical problem formulation with business problem
Before starting machine learning (ML) model training, you need to understand
the business requirements and available data. This inc... | scraping/output/-4005684865848025300.txt | [
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## Validation is hard
Unsupervised learning uses tools like clustering to identify data patterns,
but the results can be difficult to interpret. That’s why domain experts
during development need to make sure that the outcome is accurate. It’s also
tough to identify causal relationships between variables and labels may... | scraping/output/-4005684865848025300.txt | [
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## Data engineering and change management
Building a successful solution for structured data requires a lot of data
engineering and change management effort. Moreover, machine learning system
development can lead to hidden technical problems such as poor data quality,
model complexity and deployment challenges. To cre... | scraping/output/-4005684865848025300.txt | [
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Data cleaning and preprocessing
Once data has been collected, it must be cleaned and preprocessed to make sure
that it is of high quality. This includes tasks like removing missing values,
handling outliers, and converting data types. Exploratory Data Science (EDA)
is essential.
Feature engineering
Feature engineeri... | scraping/output/-4005684865848025300.txt | [
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Once the model has been trained and evaluated, it must be deployed into
production. This involves integrating the model into existing systems and
workflows and monitoring its performance over time.
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In this blog, we will uncover some pressing challen... | scraping/output/-4422453358527678687.txt | [
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Pharmaceutical companies are often in a race against time. Although patents
protect companies intellectual property, most of this time is spent turning an
idea into a marketable product. Traditionally medicines are produced in the
old-fashioned way by a batch process [3]. This traditional batch process has
proven to ha... | scraping/output/-4422453358527678687.txt | [
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to packaging. Inventories including raw-material storage can last 250 days
[1]. Reducing these times is essential to recover the billions spent in drug
development given the fact that there only a few years left before the patents
are expired. | scraping/output/-4422453358527678687.txt | [
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The pharmaceutical industry is often compared to the semiconductor industry
due to the high costs and the need for high throughput, volume and yield in a
clean environment with high consistency [2]. The semiconductor industry is
already quite matured when it comes to implementing industry 4.0 and this has
resulted in m... | scraping/output/-4422453358527678687.txt | [
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Today, next to the stringent manufacturing requirements, the industry is
entering an era of smaller batches and personalized medicine. Medicine is
designed with more unique features and needs to be delivered quicker to
patients in need [6]. In other words, drug production requires very small
batches often measuring in ... | scraping/output/-4422453358527678687.txt | [
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To allow smaller batches and more cost effective production of drugs, the
industry is changing to continuous flow manufacturing as shown in Figure 1.
Small amounts of chemical ingredients flow without disruption from raw
ingredients to tablet. In 2016 the FDA encouraged manufacturers to transition
from batch to continu... | scraping/output/-4422453358527678687.txt | [
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approval to switch from batch to continuous manufacturing [10] and Novartis
entered a 10-year research collaboration program with the Massachusetts
Institute of Technology (MIT) in 2007 [11]. | scraping/output/-4422453358527678687.txt | [
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Figure 1: Conceptual continuous manufacturing process compared to a typical
batch process for the pharmaceutical industry by Lee et al. [8] left and right
is a Novartis vision of continuous manufacturing in cooperation with MIT [11]
### So how can AI help with drug manufacturing?
No matter if you have a batch pr... | scraping/output/-4422453358527678687.txt | [
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As discussed above, continuous manufacturing processes require ultimate
process and quality control. One of the advantages AI can offer is connecting
the sensor data to lab results as depicted in Figure 2. Thanks to the use of
open standards, such as OPC-UA on PLCs, incorporating machines and lines from
different brand... | scraping/output/-4422453358527678687.txt | [
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Figure 2: Use machine learning to predict the quality of your drug for every
single batch.
Next to quality control by IOT data, one can use machine learning algorithms
to automate visual inspection of medicine foil strips checking for container
and closure, information which is written on the label (such as brand... | scraping/output/-4422453358527678687.txt | [
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Figure 3: Image segmentation techniques for common objects shown in the left
by Lin et al. [16] and applied on an image of a Scanning Electron Microscope
(SEM)
The first step of improving efficiencies is to create insight in your
manufacturing machine data. An example is the Bosch Pharma i 4.0 Starter
Edition [17]... | scraping/output/-4422453358527678687.txt | [
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Countless parameters in a manufacturing process can be mapped and optimized.
Doing this manually is a tedious job and basically impossible due to the
amount of possibilities and varying conditions. Using machine learning, you
can implement a full autonomous parameter optimizer using a self-learning
system to find the o... | scraping/output/-4422453358527678687.txt | [
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Medicine often have different lead times and specific transport or storage
requirements. Specific requirements are: drugs which require temperature
control resulting in limited shelf life; flammable or explosive drugs which
need to be handled carefully; narcotics or psychotropic drugs which require
close monitoring due... | scraping/output/-4422453358527678687.txt | [
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-0.005416444502770901,
0.041316356509923935,
-0.08... |
with multiple obstacles. The transport/storage requirements act as the
obstacles in this example. Note that similar AI techniques can be used for
demand forecasting in your supply chain. | scraping/output/-4422453358527678687.txt | [
-0.007463349029421806,
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Figure 5: Route optimization using machine learning techniques can be applied
both for optimizing planning inside manufacturing facilities and for supply
chain purposes such as demand forecasting
More information on how to apply AI in drug manufacturing can be found in this
video.
### On a final note...
To... | scraping/output/-4422453358527678687.txt | [
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## Related posts
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Leveraging Artificial Intelligence for insight-driven commercial models in
life sciences
In this blogpost, we’ll explain how AI can help solve typical challenges in
the commercial model of life sciences companies.
April 26, 2021
By
Sven Rymenans
Life Sciences & Healthca... | scraping/output/-4422453358527678687.txt | [
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### Introduction
In an Accenture survey (1), more than 90% of ... | scraping/output/5022103063578893462.txt | [
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Traditionally, the lion’s share of pharma promotional strategy and investment
has been focused on the interactions between the HCP (HealthCare Practitioner)
and sales representative. Other promotional channels are meetings and events,
service team calls, inside sales, digital, educational activities, etc. For
sales org... | scraping/output/5022103063578893462.txt | [
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The AI revolution on the commercial side of the pharma business has been
slower on the uptake than on the R&D side, but we see great opportunities to
improve the commercial model through AI in many ways, of which 2 of them we’ll
detail in this blogpost.
### Segmentation & Targeting | scraping/output/5022103063578893462.txt | [
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### Segmentation & Targeting
Commercial organisations in Life Sciences often use HCP information like the
number of patients treated for a specific disease, or the % of adoption to its
product as a way to segment HCPs. A classical segmentation could be Gold-
Silver-Bronze, with Gold referring to HCPs that treat more t... | scraping/output/5022103063578893462.txt | [
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Well, in order to address this we use what is called an embedding space. For
the non-techies reading this post, please bear with me for just a few seconds.
An embedding is a relatively low-dimensional space into which you can
translate high-dimensional vectors. Embeddings make it easier to do machine
learning on large ... | scraping/output/5022103063578893462.txt | [
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‘dimensions’ the embedding space maps the values (e.g. age number) on an axes
to distinguish between HCPs. The illustration below gives 2 examples of what
this looks like for a combination of 2 dimensions (e.g. gender and royalty in
the left example). | scraping/output/5022103063578893462.txt | [
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The illustrations show this in a 3 dimensional space, as this is the maximum
number of dimensions that can be visually illustrated, but in fact the number
of dimensions used in the embedding space can grow to infinity. But let’s
stick with the 3D visualisation for simplicity. So by doing this exercise with
all data at ... | scraping/output/5022103063578893462.txt | [
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So for example, a general practitioner living in location ABC might appear to
be very similar to a pneumologist in location XYZ, because they attended the
same university together, are of young age so prefer digital channels,
practice the same hobbies and both frequently attend conferences. These two
HCPs should be tar... | scraping/output/5022103063578893462.txt | [
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-0.05269007... |
We at ML6 applied and benchmarked this technique of hyper personalisation at a
multinational company and outperformed the other techniques by 150%.
### Commercial Execution
Analysis of the responsiveness of sales to promotional activities can be done
through the smart use of data. Measuring brand sensitivity to p... | scraping/output/5022103063578893462.txt | [
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To answer these questions we make use of the unobserved components (UCM) time
series model. This model was first introduced to the econometrics and
statistics fields by A.C Harvey (1989). UCM can be considered to be a multiple
regression model with time varying coefficients. It is based on the principles
that it is use... | scraping/output/5022103063578893462.txt | [
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#### Input data:
#### Model decomposition:
Our model takes into consideration the carry-over effect (e.g. my weight this
year is impacted by my weight at the beginning of the year (starting point)
and how it evolved the years before), seasonal trends (e.g. ice cream sells
better in summer than winter) and ... | scraping/output/5022103063578893462.txt | [
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Next to that, the model also accounts for what is called the memory (ad-stock
effect). This refers to the impact that marketing activities have over time on
sales or brand health: It captures how response to advertising builds and
decays in consumer markets. This concept agrees with common sense that the
awareness leve... | scraping/output/5022103063578893462.txt | [
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### Conclusion
The fact that science and technology are converging to enable more
personalized, precise treatments for patients should also trigger sales and
marketing professionals to apply similar techniques for more precise targeting
and more effective commercial efforts. The current state of modelling
techniques a... | scraping/output/5022103063578893462.txt | [
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April 28, 2021
By
Life Sciences & Healthcare
Large Language Model
Large Language Model
Foundation Models
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Corporate
Corporate
People
People
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Structured Data
Chat GPT
Chat GPT
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Sustainability
Voice & Sound
Voice & Sound
Front-End Development
Front-End Dev... | scraping/output/5022103063578893462.txt | [
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Cu... | scraping/output/5921266941805258048.txt | [
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Join our newslet... | scraping/output/5921266941805258048.txt | [
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-0.030227685... |
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Client ca... | scraping/output/-2930323519026450185.txt | [
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Share this post
Recently, Google unveiled their latest offering in ML Tools on their Google
Cloud Platform, Vertex AI. In brief, the new platform seeks to combine the
tools offered previously by separate services on GCP, such as AI Platform and
AutoML, into a single service. Integrating these previously separate servi... | scraping/output/-2930323519026450185.txt | [
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### AI Platform Pipelines
Previously in AI Platform, Google’s former Machine Learning platform, we had
AI Platform Pipelines. This was a service aimed at making it easy to deploy
Kubeflow Pipelines, the MLOps Pipeline toolkit from Kubeflow, to Google Cloud
Platform resources. The workflow for deploying a Kubeflo... | scraping/output/-2930323519026450185.txt | [
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#### 2\. Deploy Kubeflow Client to GKE Cluster
With our cluster up and running, we could easily deploy Kubeflow Pipelines
instances to it using the AI Platform Pipelines UI in the GCP console.
Creating a new deployment was as simple as selecting your GKE Cluster from a
drop down list and filling out a few pieces... | scraping/output/-2930323519026450185.txt | [
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#### 3\. Develop Pipelines with Notebooks
With the cluster setup and the Kubeflow instance created we could use the
Notebooks of AI Platform as secure development environments for working with
the Kubeflow Pipelines SDK to develop our pipelines. In AI Platform we are
simply using vanilla Kubeflow Pipelines tools... | scraping/output/-2930323519026450185.txt | [
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This workflow made it very easy to work with Kubeflow Pipelines in Google
resources, with deployment taking 5 minutes (if you don’t include the time it
takes for GCP to spin up the resources in the background). Thanks to GKE,
Kubernetes cluster management was as easy as it had ever been, and thanks to
AI Platform Pipel... | scraping/output/-2930323519026450185.txt | [
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A big indicator of this is that users are no longer required to create a
dedicated Kubernetes cluster via GKE on which to run their Pipelines. Instead,
Vertex AI employs an apparently serverless approach to running Pipelines
written with the Kubeflow Pipelines DSL. Instead, the Kubernetes clusters and
the pods running ... | scraping/output/-2930323519026450185.txt | [
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Vertex Pipelines UI
This also hints at another key conceptual difference between the two tools;
Vertex AI isn’t running an instance of a Kubeflow Client. Instead, Vertex
Pipelines is its own version of the kind of infrastructure usually provided by
Kubeflow Pipelines (ie, Container Workflow Orchestration),... | scraping/output/-2930323519026450185.txt | [
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-0.005550457630306482,
0.0373573936522007,
-0.031... |
Another benefit that users will welcome with the new approach is the reduction
in cost that is provided by the pay-as-you-go model that this ‘pipelines-as-a-
service’ approach is able to deliver. Instead of paying for the continuous
uptime of the necessary K8s Cluster, users will now only pay $0.03USD per run,
plus wha... | scraping/output/-2930323519026450185.txt | [
0.00871786754578352,
-0.03549740090966225,
-0.03313988074660301,
-0.043238505721092224,
0.07151530683040619,
-0.011889508925378323,
0.04571564868092537,
0.04577489569783211,
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-0.025809012353420258,
0.03373749181628227,
0.015660135075449944,
0.04327064007520676,
-0.05074... |
This new version of the SDK is designed primarily to make use of the Pipeline
Metadata and Artifact tracking tools of ML Metadata, an open source Metadata
tracking tool developed by the Tensorflow Extended team. Vertex AI implements
its own version of this in Vertex ML Metadata, which makes use of the base TFX
ML Metad... | scraping/output/-2930323519026450185.txt | [
0.03236827254295349,
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0.03957395255565643,
0.018126701936125755,
0.07949776947498322,
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First, concerning building components, KFP SDK v2.0 mandates that all
component parameters be annotated with their data type. In addition, an extra
distinction is now made between Component inputs that are parameters, and
those that are artifacts. Component Parameters are those that can be passed as
string, integer, fl... | scraping/output/-2930323519026450185.txt | [
-0.006127190310508013,
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0.026271725073456764,
-0.0... |
Old style component specification component.yaml file
New style component specification component.yaml file
Inspecting these component specifications carefully, one will notice that for
input values in the ‘command’ portion of the ‘implementation’, we previously
would have used `{inputValue: variable_n... | scraping/output/-2930323519026450185.txt | [
0.0363851822912693,
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0.03687590733170509,
0.03212128207087517,
0.06169933080673218,
-0.0372... |
When building Pipelines, the new SDK version brings a couple of changes. The
first is that, as with components, pipeline parameter definitions must be
annotated with their data types. Second, pipelines must be decorated with the
`@kfp.dsl.pipeline` decorator. Within the Pipeline decorator we can specify
the pipeline na... | scraping/output/-2930323519026450185.txt | [
0.009053012356162071,
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-0.005186820402741432,
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0.0009793478529900312,
0.048849981278181076,
-0.0... |
#### Kubeflow SDK v2.0 Limitations
In addition to these SDK v2.0 considerations that users must keep in mind when
developing Kubeflow Pipelines for Vertex Pipelines, there are some additional
constraints given the practicalities of Vertex Pipelines’ implementation.
The first is caching of pipeline component exe... | scraping/output/-2930323519026450185.txt | [
-0.000052366969612194225,
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0.04021346569061279,
-0.... |
In addition to caching, recursively called components is another feature of
Kubeflow Pipelines that Vertex Pipelines does not currently support. The
Google documentation on this does use the same language of ‘Currently, Vertex
Pipelines does not support..’, which would indicate that this is something
they are potential... | scraping/output/-2930323519026450185.txt | [
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0.00854721199721098,
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### Conclusion
In summary, Vertex AI Pipelines introduces some nice changes over the previous
AI Platform Pipelines implementation that will overall make the experience of
developing and running MLOps workflows on GCP a lot easier. The move to make
the underlying resources more managed than in the previous so... | scraping/output/-2930323519026450185.txt | [
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0.016007287427783012,
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0.02324005775153637,
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0.04333416372537613,
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0.011886999011039734,
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0.048060301691293716,
-0.0238688... |
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0.030809788033366203,
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### Introduction
When designing real-world NLP applications, you are often confronted with
limited (labeled) data, latency requirements, cost restrictions, etc. that
hinder unlocking the full potentia... | scraping/output/2077654063284246761.txt | [
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-0.028399... |
Let’s consider some examples where these design patterns make a lot of sense:
Named Entity Recognition (NER): the choice for or against an ML-based approach
essentially boils down to how contextual the entities are.
For example, dates can be structured in a specific way (e.g, “DD/MM/YYYY”). If
an entity follows th... | scraping/output/2077654063284246761.txt | [
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0.0022690563928335905,
0.012861852534115314,
0.02875179797410965,
0.08863168954849243,
-0.070358... |
A simple RegEx rule can easily recognize both dates
However, say you only want to extract dates of birth and not other kinds of
dates. Now, we are dealing with a very “contextual entity”: dates of birth and
other kinds of dates look exactly the same; without any context, you wouldn’t
be able to distinguish between ... | scraping/output/2077654063284246761.txt | [
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-0.01770668663084507,
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0.03202275559306145,
0.05548521876335144,
-0.0501... |
Some tasks are just too complex for rule-based approaches to have a meaningful
impact
#### (2) RULES AFTER/BEFORE ML
Rule-based pre-processing design pattern
Rule-based post-processing design pattern
The next pattern we’ll look into has a sequential nature: the business rules
either act as a first... | scraping/output/2077654063284246761.txt | [
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-0.00553992111235857,
0.026921050623059273,
0.04558176174759865,
0.05094237998127937,
-0.048297... |
With one simple rule, we only do inference on 2 passages instead of 7 with no
impact on performance
With a few simple rules, you can often drastically reduce the amount of
processing power you use with a minimal to non-existent impact on performance.
(Semantic) search: in a very similar fashion to what’s out... | scraping/output/2077654063284246761.txt | [
0.051795318722724915,
-0.04610196501016617,
-0.04313066974282265,
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0.012836994603276253,
0.002320515923202038,
0.01840292662382126,
0.07479565590620041,
-0.02341168187558651,
0.001216141041368246,
0.03803014010190964,
0.07332513481378555,
-0.007278... |
Depending on the data, a double-digit percentage decrease in latency is often
attainable with a negligible impact on search performance.
Entity linking: let’s say we want to extract product names along with sales
prices and link the two entities together (i.e., figure out which sales price
belongs to which product ... | scraping/output/2077654063284246761.txt | [
0.05340706557035446,
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0.019852373749017715,
0.03974689543247223,
0.06957384198904037,
-0.03... |
More determinism: not all mistakes are equal. Perhaps there are some patterns
that you know to be correct and want your solution to get correct every single
time.
In this scenario, you can have a restrictive rule-based system that ensures
that these critical situations are covered and in parallel a more
generali... | scraping/output/2077654063284246761.txt | [
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0.027852483093738556,
0.0355987623333931,
0.08465919643640518,
-0.04218... |
#### (4) ML-INFORMED RULES
ML-informed rules design pattern
A more niche situation could be that your use case really requires a rule-
based system — be it for regulatory reasons (e.g., GDPR’s “Right to
explanation”) or for other reasons — but that these rules are very difficult
to determine.
In this sce... | scraping/output/2077654063284246761.txt | [
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0.06484311819076538,
-0.056... |
This pattern also looks to combine rules and ML but it does so by finding an
appropriate representation of RegEx results and truly integrating the domain
knowledge into the model architecture.
Theoretically, this is a very clean solution but in practice, we don’t see
(widespread) adoption of such architectures. ... | scraping/output/2077654063284246761.txt | [
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0.021430201828479767,
0.095877505838871,
-0.0480959... |
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Sustain... | scraping/output/2077654063284246761.txt | [
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0.009572062641382217,
-0.0171268992125988,
-0.048388510942459106,
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-0.02630762942135334,
0.041026338934898376,
0.036781102418899536,
0.04470415785908699,
-0.02857... |
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... | scraping/output/7518757031055141065.txt | [
0.05454127490520477,
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0.036326371133327484,
0.019951676949858665,
-0.0581... |
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In 2020, we have trained and open-sourced the first Dutch GPT2 model, in
various sizes. Of course we wanted to share this with the world by open-
sourcing the models, the code and a nice application th... | scraping/output/7518757031055141065.txt | [
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-0.06736791... |
The final user-facing application looks as follows:
Try it for yourself at https://gpt2.ml6.eu/nl
The current setup has some difficulties though:
The responses take some time to generate, especially with the medium-size
model, reducing the user experience.
Second, the container is quite big because of the large mod... | scraping/output/7518757031055141065.txt | [
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We’re not going to go into detail on what quantization is. If you wanna get a
great primer on this: we wrote a blogpost on this and other model efficiency
aspects here.
TDLR: by reducing the precision of the weights in the Linear and Embedding
layers from fp32 to int8 through a mapping action, the memory footprint of ... | scraping/output/7518757031055141065.txt | [
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0.03125951439142227,
-0.070157... |
If you’re just here for the code goodies, you can find all of the code for
this blogpost link !
Quantization using ORT only involves three simple steps:
#### 1\. Convert the PyTorch model to an ONNX model
All the upcoming transformations happen through the ONNXRuntime (ORT) library,
so it’s only logical that these s... | scraping/output/7518757031055141065.txt | [
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0.025405991822481155,
0.02138044685125351,
0.019722774624824524,
-0.06... |
#### Run it using ORT
To actually use the model artifact (ONNX binary file), we of course need a
runtime to host it. What better runtime for ONNX than ONNXRuntime
To do this, you can easily create an ORT session, which can be fed with the
typical inputs otherwise required in a HF model (token id’s, attention masks,
e... | scraping/output/7518757031055141065.txt | [
0.05195657163858414,
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0.05266888812184334,
-0.06964... |
We won’t go into detail on all of the code needed for each of these aspects,
but you can find them all in the notebook (link again) where they are
implemented.
### Evaluation
So we coded up all these extra aspect to get nice predictions, and our model
is running happily on a Cloud Run instance, inside a Python app th... | scraping/output/7518757031055141065.txt | [
0.02408429980278015,
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0.0399811826646328,
-0.058324873... |
So by measuring the average, median and max difference in logit values, we can
get a first idea on the quality of the potential output:
We can see that the logit values can differ quite a bit. We can also see that
the impact is less for the 345M parameter GPT2-medium than for the 117M
GPT2-small model.
Though this is... | scraping/output/7518757031055141065.txt | [
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0.029453082010149956,
0.05587177723646164,
-0.06931... |
We followed their approach, and measured the perplexity on the first 1000
documents of the Dutch Partition of the OSCAR corpus. This is a wide
collection of various crawled Dutch webpages.
Interestingly, the perplexity increase is less high for the medium GPT2 model
compared to the small GPT2 model. Meaning the GPT2-m... | scraping/output/7518757031055141065.txt | [
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From the look of it, both seem to do very okay! Well enough for the online
demo, where only a few next tokens are predicted each time.
But is it any fast… ?
### Latency
Now that we know the quantized models are usable, we can start to measure the
first annoyance with the as-is deployment: the startup time and req... | scraping/output/7518757031055141065.txt | [
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-0.03906... |
Let’s compare this “warmup time” between a service serving the non-quantized
versions and the quantized versions:
the request latency
To measure the response timing for each deployed model, we send a barrage of a
few hundred sequential requests to the deployed microservice. Meaning this
latency involves network laten... | scraping/output/7518757031055141065.txt | [
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-0.05255... |
We can easily use the Cloud Run pricing documentation to get a price estimate:
* The quantized gpt2-small + gpt2-medium model image fits on a 2GB, 1vCPU machine, totaling to 💲57.02
* The non-quantized gpt2-small + gpt2-medium model image fits on a 8GB, 2vCPU (because you can’t have a 1vCPU machine for that amount... | scraping/output/7518757031055141065.txt | [
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### So long!
Leveraging quantization and ORT clearly results in a nice speedup and cost
reduction!
Enjoy all the money you just saved! And stay tuned for upcoming blogposts
where we leverage Triton Inference Server for full transformer hosting
enlightenment, since this is a more recommended approach for mature model
... | scraping/output/7518757031055141065.txt | [
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Data Protection & Security
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Infrastructure
Infrastructure
Hardware & sensors
Hardware & sensors
MLOps
MLOps
Generative AI
Generative AI
Natural language processing
Natural language processing
Computer vision
Computer vision
Accelerating businesses with AI te... | scraping/output/7518757031055141065.txt | [
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