The key difference is a non compete or other clause that prevents you from working multiple contracts.

with a 1099 you are independent

I’m on a career break, but I was as of December running the AI division of a consulting company.

I will say that finding part time or short term work is very hard. Longer term contract work is relatively easy.

most companies are struggling with the basics - data engineering, data analytics… maybe data science, but with data science you have to be able to talk to the c-suite well and without an mba the lingo is a little hard.

Machine learning projects often require a lot more time to deliver (beyond a proof of concept, and pocs don’t make money) and generally a team rather than an individual, and wayy more stakeholder support than you can muster

Usually ML projects require a lot of data which often puts you into a larger sized business which makes it very difficult to navigate as a freelancer…. You probably need to be in their system when it comes to invoicing and such and so you need to have your ducks in a row where most freelancers don’t. Freelancers, in general, succeed with smaller businesses.

Ignore anyone suggesting upwork.

One avenue I’d recommend is having an honest conversation with consulting company recruiters about what you’re looking for. Stay 1099 or do corp 2 corp. they’ll want you to come on as w2 but be a firm no. Generally these recruiters are looking for easy money and so are you. It’s definitely possible to make a meaningful business relationships here though at your level of seniority you might now know how to play the game at first

A typical machine learning curriculum should cover the following topics:

Introduction to machine learning

Linear Regression

Logistic Regression

Decision Trees and Random Forests

Naive Bayes

k-Nearest Neighbors (k-NN)

Support Vector Machines (SVMs)

Neural Networks

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

Generative Adversarial Networks (GANs)

Clustering (K-means, Hierarchical)

Dimensionality Reduction (PCA, t-SNE)

Ensemble Methods

Model evaluation and selection

Hyperparameter tuning

Regularization

Bias-Variance Trade-off

Overfitting and Underfitting

Model interpretability and explainability

Restrictive licensing and limited usefulness to industry problems.

I’m a consultant, and most recently was running the AI wing of a publicly traded consulting company as a full time employee , and OPs feedback is entirely correct.

I’m currently on a career break and returning as an independent consultant

Yes, and this is why business and sales people make a ton of money.

A great technology isn’t valuable unless you know how to monetize it