How to deliver on Machine Learning projects
- The ML Engineering Loop In this article, we’ll describe our conception of the “OODA Loop” of ML: the ML Engineering Loop, where ML Engineers iteratively Analyze Select an approach Implement ...
- Getting started To bootstrap the loop described below, you should start with a minimal implementation that has very little uncertainty involved. ...
- Analyze Identify the performance bottleneck ...
- Select Approach ...
- Implement ...
Full Answer
How to start a machine learning project?
The initial stage in starting a project is knowing what problems we will solve. This applies to any project, including the machine learning project. Of course, everything starts with a problem, because if there are no problems, nothing needs to be solved. We must determine the problem that we will solve.
Why aren’t machine learning projects getting real-world business use cases?
The disconnect between ML projects and real-world business use case is often traced back to a lack of proper project scoping. For many practitioners, a machine learning project starts with obtaining data, processing, model training/evaluation and ends in model deployment.
How do you evaluate a machine learning project?
To evaluate your machine learning project, you’ll need to use metrics to measure the performance of your model. The metrics you use will depend on your problem type. The performance of a classification model can be measured using metrics like accuracy, precision, and more.
How to refine your machine learning models?
Refining your models is a usual occurrence when you organize your machine learning project. Once you have a rough notion of effective model architectures and methodologies for your problem, you should now focus your efforts on extracting performance advantages from the model. There are 4 general aspects to think about when refining your models.
How do you present machine learning results?
Methodology 1First sort your model scores from high to low and decile them. ... Next calculate the minimum, median, and maximum score value for each decile.Calculate the number of true positives by decile and then take the count of true positives divided by total true positives in your scoring population.
How would you describe a machine learning project?
Machine learning is the process of making systems that learn and improve by themselves, by being specifically programmed. The ultimate goal of machine learning is to design algorithms that automatically help a system gather data and use that data to learn more.
How do you introduce machine learning?
At its most simplistic, the machine learning process involves three steps:Feed a machine learning model training input data. In our case, this could be customer comments from social media or customer service data.Tag training data with a desired output. ... Test your model by feeding it testing (or unseen) data.
How do you explain machine learning to someone?
Machine learning is an application of AI that enables systems to learn and improve from experience without being explicitly programmed. Machine learning focuses on developing computer programs that can access data and use it to learn for themselves.
How do you structure an ML project?
Machine Learning Project Structure: Stages, Roles, and ToolsStrategy: matching the problem with the solution.Dataset preparation and preprocessing. Data collection. Data visualization. Labeling. ... Dataset splitting.Modeling. Model training. Model evaluation and testing. ... Model deployment.
How do you document a ML project?
OverviewPlanning and project setup. Define the task and scope out requirements. ... Data collection and labeling. Define ground truth (create labeling documentation) ... Model exploration. Establish baselines for model performance. ... Model refinement. ... Testing and evaluation. ... Model deployment. ... Ongoing model maintenance.
What are the three stages of building a model in machine learning?
We can define the machine learning workflow in 3 stages.Gathering data.Data pre-processing.Researching the model that will be best for the type of data.Training and testing the model.Evaluation.
What are the six steps of machine learning cycle?
In this book, we break down how machine learning models are built into six steps: data access and collection, data preparation and exploration, model build and train, model evaluation, model deployment, and model monitoring.
What are the three types of machine learning?
The three machine learning types are supervised, unsupervised, and reinforcement learning.
What is machine learning with real time example?
Image recognition is a well-known and widespread example of machine learning in the real world. It can identify an object as a digital image, based on the intensity of the pixels in black and white images or colour images. Real-world examples of image recognition: Label an x-ray as cancerous or not.
What is ML in simple words?
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
What are key tasks of machine learning?
A machine learning task is the type of prediction or inference being made, based on the problem or question that is being asked, and the available data. For example, the classification task assigns data to categories, and the clustering task groups data according to similarity.
What are the 3 key steps in machine learning project?
Data preparation. Exploratory data analysis(EDA), learning about the data you're working with. ... Train model on data( 3 steps: Choose an algorithm, overfit the model, reduce overfitting with regularization) Choosing an algorithms. ... Analysis/Evaluation. ... Serve model (deploying a model) ... Retrain model. ... Machine Learning Tools.
How do you explain a science project in an interview?
0:2715:25Watch this before your data science interview | How to ... - YouTubeYouTubeStart of suggested clipEnd of suggested clipThe very first question is hey tell me about yourself. And immediately after that question you knowMoreThe very first question is hey tell me about yourself. And immediately after that question you know that the next question is going to be tell me about one of your projects that you did.
What types of ML projects would you be interested in working on?
Top 100 Machine Learning Project Ideas for Tech EnthusiastsAge and Gender Detection with Python. ... Amazon Alexa Reviews Sentiment Analysis. ... Amazon Product Reviews Sentiment Analysis. ... Amazon Recommendation System. ... Autocorrect Keyboard with Python and Machine Learning. ... Automatic License Number Plate Recognition System.More items...•
How do you explain data in a science project?
Let's look at each of these steps in detail:Step 1: Define Problem Statement. Before you even begin a Data Science project, you must define the problem you're trying to solve. ... Step 2: Data Collection. ... Step 3: Data Cleaning. ... Step 4: Data Analysis and Exploration. ... Step 5: Data Modelling. ... Step 6: Optimization and Deployment:
Summary
Do you think you are the only one who is reading this article and concerned about machine learning? Then fret not, you’ve got some company! It is not a surprise if you find yourself curious about machines and desperate about machine learning.
Is machine learning hard?
Well then, having known about machine learning, one might wonder, how do I start with machine learning? How do I get a basic idea of machine learning?
Latest projects on Machine Learning
Want to develop practical skills on Machine Learning? Checkout our latest projects and start learning for free
Difference between artificial intelligence and machine learning
As you have been hearing the words machine learning and article intelligence quite frequently in this article, are you aware of the difference between them?
What is machine learning for beginners?
Well, no one becomes a full-fledged and talented machine learning engineer just like that in one go. As we discussed above, when we start something new, its never easy, but as beginners in machine learning, you all need to be patient and learn all the required things as mentioned above.
How to develop a simple machine learning project?
The best way to develop a machine learning project is by understanding the concepts from basic and implementing the acquired concepts. You can also check our machine learning online courses which guide you to develop a machine learning project from basics.
What are the different approaches to machine learning?
As we discussed in the previous article, there are several approaches in machine learning, namely supervised learning, unsupervised learning, and reinforcement learning. We can determine the approach we take based on the data/problem we observed before.
Why do we need to find weaknesses and strengths of each machine learning method before modeling?
Other tips from me, find out first the weaknesses and strengths of each machine learning method before doing modeling because that will save a lot of time. Each method has advantages and disadvantages for certain data characteristics. For example, there are methods that work well if the input is normalized first, or methods if too large data will cause overfit and there are also methods that require very large data.
Why do we need to prepare data for training?
We need to prepare the data so that when entering the training phase, the data does not contain noise which will affect the performance of the model created.
Why do we need deep learning?
Because the cost required is very large . If it is possible to use traditional machine learning methods then use that method. However, if the case is very complex and cannot be handled by traditional machine learning methods , then you can use deep learning.
What is the first step in starting a project?
The initial stage in starting a project is knowing what problems we will solve. This applies to any project, including the machine learning project. Of course, everything starts with a problem, because if there are no problems, nothing needs to be solved.
How to determine if a model is good or not?
There are several ways to calculate performance including accuracy, f1 measure, silhouette score, etc. One other way to evaluate your model is to validate your model with people who are experts in their fields.
Can you use one method to make a model?
Do not let you spend time just trying one by one method to produce the best results. Because usually, one machine learning method has many parameters that we can modify. For example, if we want to make a model using the Neural Network we can change the learning rate parameters, the number of hidden layers, the activation function used, etc.
How much of data is training set?
Training set (usually 70-80% of data): Model learns on this.
What is data preprocessing?
Data preprocessing, preparing your data to be modelled.
What is feature engineering?
Feature engineering: transform data into (potentially) more meaningful representation by adding in domain knowledge
What to do after a model analysis?
After your analysis, you will have a good sense of what kind of errors your model is making and what factors are holding back performance. For a given diagnosis, there might be several potential solutions and the next step is to enumerate and prioritize them.
What is ML loop?
The purpose of the ML Engineering Loop is to put a rote mental framework around the development process, simplifying the decision making process to focus exclusively on the most important next steps. As practitioners progress in experience, the process becomes second nature and growing expertise enables rapid shifts between analysis and implementation without hesitation. That said, this framework is still immensely valuable for even the most experienced engineers when uncertainty increases — for example, when a model unexpectedly fails to meet requirements, when the teams’ goals are suddenly altered (e.g., the test set is changed to reflect changes in product needs), or as team progress stalls just short of the goal.
1. Planning
Before you start any machine learning project, there are a number of things that you need to plan. In this case, the term ‘plan’ encompasses a number of tasks. By completing this step, you’ll develop a better understanding of the problem that you’re trying to solve and can make a more informed decision on whether to proceed with the project or not.
2. Data
This step is focused on acquiring, exploring, and cleaning your data. More specifically, it includes the following tasks:
3. Modeling
Once your data is ready to go, you can move on to building your model. There are three main steps to this:
4. Production
The last step is to productionize your model. This step is not talked about as much in courses and online but is essential especially for enterprises. Without this step, you may not be able to get the full value out of your models that you build. There are two main things to consider in this step:
Terence Shin
Founder of ShinTwin | Let’s connect on LinkedIn | Project Portfolio is here.
How to Setup and Plan your Machine Learning Project?
It may be enticing to skip this section and see what the models can accomplish. But, all too frequently, you’ll waste time by postponing talks on the project’s goals and model assessment criteria. Instead, from the outset of the project, everyone should be working toward a single purpose.
How do you Specifying Machine Learning Project Requirements?
As software engineers, we use use-cases to specify machine learning project requirements. Unlike software engineers, our measure of success is how well the project predicts data. So we focus on the metric itself (Accuracy, Precision, Recall, etc).
How to Organize the priority of your Machine Learning Projects?
Here are models to think about for assessing and prioritizing your machine learning projects:
How do you organize Data Collection And Labeling in your Machine Learning Project?
To collect labeled data systematically, notice when a vehicle moves from a nearby lane into the Tesla’s lane and then rewind the video stream to label that a car will cut into the lane.
How to determine if a task is reasonably easy for a Machine Learning System to Learn?
When considering the feasibility of a machine learning project, consider the following questions:
Why do we use alphabets as input methods?from prezi.com
We used alphabets as the input methods so that a large number of inputs can be provided.
What algorithm will detect the alphabet written by the user on-air?from prezi.com
The OCR algorithm to our project will detect the alphabet written by the user on-air.
How to assess feasibility of ML project?
Ensure that the project is technically feasible — use external benchmarks to gau ge feasibility. If humans can give a high level of performance, that suggests that the project is feasible. Otherwise, it may be harder. HLP (Human Level Performance) is an important benchmark for assessing feasibility.
Why is scoping important in machine learning?
Though scoping lies at the boundary of project management and machine learning, most machine learning systems in production rely less on traditional project-based delivery methods and more on product development-based ones where flexibility and adaptability are critical for meeting customer needs .
What is the key specification in determining milestones?
Determine milestones — the key specification in determining milestones is to define the metrics and establish a timeline for achieving those metrics. This will give the desired clarity when working on a project. Establishing the following metrics can help in determining milestones for an ML project.
Why is there a disconnect between ML and real world business use cases?
The disconnect between ML projects and real-world business use case is often traced back to a lack of proper project scoping. For many practitioners, a machine learning project starts with obtaining data, processing, model training/evaluation and ends in model deployment. Too much focus on mechanics leads to missing the goal of the business side of the team.
Does machine learning meet business goals?
Most machine learning projects end up not meeting business goals. Even after training and testing, many models never make it to production use. This has led companies to re-examine the purpose of Machine Learning and Data Science teams in their organizations.
Understanding The Problem
Data Acquisition
- If you have found a problem and the goal that you want to solve, then the next step is to get the required data. There are many ways to do data collection. I will explain some of them. 1. The first way is to download open-sourced data on the internet such as Kaggle, Google dataset, UCI machine learning, etc. But you also have to pay attention to the limitations of using these datase…
Data Preparation
- After we get the data we need, the next step is to prepare the data before entering the training phase. Why is that? Let’s compare the data as material for cooking. Before we cook, we definitely process the raw ingredients first, such as washing, cleaning, removing unnecessary ingredients, cut into pieces, etc., it is not possible to put raw materials into a frying pan. So it is with data. W…
Modelling
- This is the part that you may have been waiting for the most. At this stage, we will make a machine learning model. As we discussed in the previous article, there are several approaches in machine learning, namely supervised learning, unsupervised learning, and reinforcement learning. We can determine the approach we take based on the data/problem we observed before. Other …
Evaluate
- The final stage is the evaluation process. You certainly don’t want if the performance of the model you are training doesn’t produce good results. Especially if your prediction model has a lot of mistakes. One of the fastest ways to determine whether the model that we are making is good or not is to measure its performance. There are several ways to calculate performance including ac…